
"""simple docstring"""


import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
							from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor

class 					A_			:



							"""simple docstring"""






							def __init__(						self						:List[str]  ,							lowerCamelCase_						:Any  ,							lowerCamelCase_						:List[Any]=13  ,							lowerCamelCase_						:Optional[int]=32  ,							lowerCamelCase_						:str=2  ,							lowerCamelCase_						:str=3  ,							lowerCamelCase_						:str=16  ,							lowerCamelCase_						:Dict=[1, 2, 1]  ,							lowerCamelCase_						:str=[2, 2, 4]  ,							lowerCamelCase_						:int=2  ,							lowerCamelCase_						:Any=2.0  ,							lowerCamelCase_						:Optional[int]=True  ,							lowerCamelCase_						:Any=0.0  ,							lowerCamelCase_						:Optional[int]=0.0  ,							lowerCamelCase_						:Dict=0.1  ,							lowerCamelCase_						:List[str]="gelu"  ,							lowerCamelCase_						:Optional[Any]=False  ,							lowerCamelCase_						:int=True  ,							lowerCamelCase_						:List[str]=0.02  ,							lowerCamelCase_						:Optional[Any]=1e-5  ,							lowerCamelCase_						:List[str]=True  ,							lowerCamelCase_						:Any=None  ,							lowerCamelCase_						:Dict=True  ,							lowerCamelCase_						:Union[str, Any]=10  ,							lowerCamelCase_						:List[Any]=8  ,							lowerCamelCase_						:Union[str, Any]=["stage1", "stage2"]  ,							lowerCamelCase_						:Any=[1, 2]  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  Any  =parent
										lowerCamelCase__			:  Any  =batch_size
										lowerCamelCase__			:  List[Any]  =image_size
										lowerCamelCase__			:  Any  =patch_size
										lowerCamelCase__			:  Any  =num_channels
										lowerCamelCase__			:  str  =embed_dim
										lowerCamelCase__			:  Dict  =depths
										lowerCamelCase__			:  Any  =num_heads
										lowerCamelCase__			:  Dict  =window_size
										lowerCamelCase__			:  List[Any]  =mlp_ratio
										lowerCamelCase__			:  Tuple  =qkv_bias
										lowerCamelCase__			:  Dict  =hidden_dropout_prob
										lowerCamelCase__			:  str  =attention_probs_dropout_prob
										lowerCamelCase__			:  Union[str, Any]  =drop_path_rate
										lowerCamelCase__			:  Optional[Any]  =hidden_act
										lowerCamelCase__			:  int  =use_absolute_embeddings
										lowerCamelCase__			:  Tuple  =patch_norm
										lowerCamelCase__			:  Union[str, Any]  =layer_norm_eps
										lowerCamelCase__			:  str  =initializer_range
										lowerCamelCase__			:  str  =is_training
										lowerCamelCase__			:  str  =scope
										lowerCamelCase__			:  str  =use_labels
										lowerCamelCase__			:  Tuple  =type_sequence_label_size
										lowerCamelCase__			:  Any  =encoder_stride
										lowerCamelCase__			:  List[str]  =out_features
										lowerCamelCase__			:  int  =out_indices





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

										lowerCamelCase__			:  Optional[Any]  =None
										if self.use_labels:
													lowerCamelCase__			:  Optional[int]  =ids_tensor([self.batch_size]  ,							self.type_sequence_label_size )

										lowerCamelCase__			:  Tuple  =self.get_config()

										return config, pixel_values, labels





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										return SwinConfig(
										    image_size=self.image_size  ,							patch_size=self.patch_size  ,							num_channels=self.num_channels  ,							embed_dim=self.embed_dim  ,							depths=self.depths  ,							num_heads=self.num_heads  ,							window_size=self.window_size  ,							mlp_ratio=self.mlp_ratio  ,							qkv_bias=self.qkv_bias  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,							drop_path_rate=self.drop_path_rate  ,							hidden_act=self.hidden_act  ,							use_absolute_embeddings=self.use_absolute_embeddings  ,							path_norm=self.patch_norm  ,							layer_norm_eps=self.layer_norm_eps  ,							initializer_range=self.initializer_range  ,							encoder_stride=self.encoder_stride  ,							out_features=self.out_features  ,							out_indices=self.out_indices  ,							)





							def    UpperCAmelCase__							(						self						:int  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:Any ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  =SwinModel(config=lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  Any  =model(lowerCamelCase_ )

										lowerCamelCase__			:  Optional[int]  =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
										lowerCamelCase__			:  List[str]  =int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

										self.parent.assertEqual(result.last_hidden_state.shape  ,							(self.batch_size, expected_seq_len, expected_dim) )





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:Any  ,							lowerCamelCase_						:int  ,							lowerCamelCase_						:List[str] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =SwinBackbone(config=lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  Tuple  =model(lowerCamelCase_ )

										# verify hidden states
										self.parent.assertEqual(len(result.feature_maps )  ,							len(config.out_features ) )
										self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,							[self.batch_size, model.channels[0], 16, 16] )

										# verify channels
										self.parent.assertEqual(len(model.channels )  ,							len(config.out_features ) )

										# verify backbone works with out_features=None
										lowerCamelCase__			:  List[str]  =None
										lowerCamelCase__			:  str  =SwinBackbone(config=lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  List[str]  =model(lowerCamelCase_ )

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps )  ,							1 )
										self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,							[self.batch_size, model.channels[-1], 4, 4] )

										# verify channels
										self.parent.assertEqual(len(model.channels )  ,							1 )





							def    UpperCAmelCase__							(						self						:int  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =SwinForMaskedImageModeling(config=lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  List[str]  =model(lowerCamelCase_ )
										self.parent.assertEqual(
										    result.logits.shape  ,							(self.batch_size, self.num_channels, self.image_size, self.image_size) )

										# test greyscale images
										lowerCamelCase__			:  int  =1
										lowerCamelCase__			:  Union[str, Any]  =SwinForMaskedImageModeling(lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()

										lowerCamelCase__			:  List[Any]  =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
										lowerCamelCase__			:  str  =model(lowerCamelCase_ )
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, 1, self.image_size, self.image_size) )





							def    UpperCAmelCase__							(						self						:List[Any]  ,							lowerCamelCase_						:Tuple  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Union[str, Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[str]  =self.type_sequence_label_size
										lowerCamelCase__			:  str  =SwinForImageClassification(lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()
										lowerCamelCase__			:  List[Any]  =model(lowerCamelCase_  ,							labels=lowerCamelCase_ )
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.type_sequence_label_size) )

										# test greyscale images
										lowerCamelCase__			:  int  =1
										lowerCamelCase__			:  int  =SwinForImageClassification(lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()

										lowerCamelCase__			:  Dict  =floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
										lowerCamelCase__			:  Dict  =model(lowerCamelCase_ )
										self.parent.assertEqual(result.logits.shape  ,							(self.batch_size, self.type_sequence_label_size) )





							def    UpperCAmelCase__							(						self						:int ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase__
										) ,       (
										    lowerCamelCase__
										) ,       (
										    lowerCamelCase__
										) ,       
										)			:  List[str]  =config_and_inputs
										lowerCamelCase__			:  Optional[int]  ={'pixel_values': pixel_values}
										return config, inputs_dict






@require_torch
class 					A_			(  A__   ,      A__   ,      unittest.TestCase     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     (
							    (
							        SwinModel,
							        SwinBackbone,
							        SwinForImageClassification,
							        SwinForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE_			   =     (
							    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
							    if is_torch_available()
							    else {}
							)
							SCREAMING_SNAKE_CASE_			   =     True

							SCREAMING_SNAKE_CASE_			   =     False
							SCREAMING_SNAKE_CASE_			   =     False
							SCREAMING_SNAKE_CASE_			   =     False





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =SwinModelTester(self )
										lowerCamelCase__			:  Dict  =ConfigTester(self  ,							config_class=lowerCamelCase_  ,							embed_dim=37 )





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										return





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  Union[str, Any]  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase_ )





							@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										pass





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										super().test_training_gradient_checkpointing()





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[Any]  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Union[str, Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  List[Any]  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_ )





							@unittest.skip(reason='Swin does not use inputs_embeds' )
							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										pass





							@unittest.skip(reason='Swin Transformer does not use feedforward chunking' )
							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										pass





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  Tuple  =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  str  =model_class(lowerCamelCase_ )
													self.assertIsInstance(model.get_input_embeddings()  ,							(nn.Module) )
													lowerCamelCase__			:  str  =model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase_  ,							nn.Linear ) )





							def    UpperCAmelCase__							(						self						:str ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  Union[str, Any]  =self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  str  =model_class(lowerCamelCase_ )
													lowerCamelCase__			:  Dict  =inspect.signature(model.forward )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase__			:  str  =[*signature.parameters.keys()]

													lowerCamelCase__			:  Optional[Any]  =['pixel_values']
													self.assertListEqual(arg_names[:1]  ,							lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  Dict  =self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__			:  List[str]  =True

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  Optional[Any]  =True
													lowerCamelCase__			:  Union[str, Any]  =False
													lowerCamelCase__			:  str  =True
													lowerCamelCase__			:  Any  =model_class(lowerCamelCase_ )
													model.to(lowerCamelCase_ )
													model.eval()
													with torch.no_grad():
																lowerCamelCase__			:  int  =model(**self._prepare_for_class(lowerCamelCase_  ,							lowerCamelCase_ ) )
													lowerCamelCase__			:  List[Any]  =outputs.attentions
													lowerCamelCase__			:  Dict  =len(self.model_tester.depths )
													self.assertEqual(len(lowerCamelCase_ )  ,							lowerCamelCase_ )

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase__			:  Dict  =True
													lowerCamelCase__			:  Optional[int]  =config.window_size**2
													lowerCamelCase__			:  int  =model_class(lowerCamelCase_ )
													model.to(lowerCamelCase_ )
													model.eval()
													with torch.no_grad():
																lowerCamelCase__			:  List[str]  =model(**self._prepare_for_class(lowerCamelCase_  ,							lowerCamelCase_ ) )
													lowerCamelCase__			:  List[Any]  =outputs.attentions
													self.assertEqual(len(lowerCamelCase_ )  ,							lowerCamelCase_ )

													self.assertListEqual(
													    list(attentions[0].shape[-3:] )  ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,							)
													lowerCamelCase__			:  List[Any]  =len(lowerCamelCase_ )

													# Check attention is always last and order is fine
													lowerCamelCase__			:  List[Any]  =True
													lowerCamelCase__			:  Dict  =True
													lowerCamelCase__			:  Any  =model_class(lowerCamelCase_ )
													model.to(lowerCamelCase_ )
													model.eval()
													with torch.no_grad():
																lowerCamelCase__			:  int  =model(**self._prepare_for_class(lowerCamelCase_  ,							lowerCamelCase_ ) )

													# also another +1 for reshaped_hidden_states
													lowerCamelCase__			:  Union[str, Any]  =1 if model_class.__name__ == 'SwinBackbone' else 2
													self.assertEqual(out_len + added_hidden_states  ,							len(lowerCamelCase_ ) )

													lowerCamelCase__			:  str  =outputs.attentions

													self.assertEqual(len(lowerCamelCase_ )  ,							lowerCamelCase_ )

													self.assertListEqual(
													    list(self_attentions[0].shape[-3:] )  ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,							)





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Any  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Optional[int]  ,							lowerCamelCase_						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =model_class(lowerCamelCase_ )
										model.to(lowerCamelCase_ )
										model.eval()

										with torch.no_grad():
													lowerCamelCase__			:  int  =model(**self._prepare_for_class(lowerCamelCase_  ,							lowerCamelCase_ ) )

										lowerCamelCase__			:  str  =outputs.hidden_states

										lowerCamelCase__			:  str  =getattr(
										    self.model_tester  ,							'expected_num_hidden_layers'  ,							len(self.model_tester.depths ) + 1 )
										self.assertEqual(len(lowerCamelCase_ )  ,							lowerCamelCase_ )

										# Swin has a different seq_length
										lowerCamelCase__			:  List[str]  =(
										    config.patch_size
										    if isinstance(config.patch_size  ,							collections.abc.Iterable )
										    else (config.patch_size, config.patch_size)
										)

										lowerCamelCase__			:  int  =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

										self.assertListEqual(
										    list(hidden_states[0].shape[-2:] )  ,							[num_patches, self.model_tester.embed_dim]  ,							)

										if not model_class.__name__ == "SwinBackbone":
													lowerCamelCase__			:  Union[str, Any]  =outputs.reshaped_hidden_states
													self.assertEqual(len(lowerCamelCase_ )  ,							lowerCamelCase_ )

													lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__ ,       lowerCamelCase__			:  Dict  =reshaped_hidden_states[0].shape
													lowerCamelCase__			:  int  =(
													    reshaped_hidden_states[0].view(lowerCamelCase_  ,							lowerCamelCase_  ,							height * width ).permute(0  ,							2  ,							1 )
													)
													self.assertListEqual(
													    list(reshaped_hidden_states.shape[-2:] )  ,							[num_patches, self.model_tester.embed_dim]  ,							)





							def    UpperCAmelCase__							(						self						:Any ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  Dict  =self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase__			:  Union[str, Any]  =(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size  ,							collections.abc.Iterable )
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  Optional[int]  =True
													self.check_hidden_states_output(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase__			:  Optional[int]  =True

													self.check_hidden_states_output(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Optional[int] ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  Any  =self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__			:  List[str]  =3

										lowerCamelCase__			:  Tuple  =(
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size  ,							collections.abc.Iterable )
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)
										lowerCamelCase__			:  Dict  =(
										    config.patch_size
										    if isinstance(config.patch_size  ,							collections.abc.Iterable )
										    else (config.patch_size, config.patch_size)
										)

										lowerCamelCase__			:  List[Any]  =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
										lowerCamelCase__			:  Tuple  =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

										for model_class in self.all_model_classes:
													lowerCamelCase__			:  Any  =True
													self.check_hidden_states_output(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							(padded_height, padded_width) )

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													lowerCamelCase__			:  Optional[int]  =True
													self.check_hidden_states_output(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							(padded_height, padded_width) )





							@slow
							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase__			:  List[Any]  =SwinModel.from_pretrained(lowerCamelCase_ )
													self.assertIsNotNone(lowerCamelCase_ )





							def    UpperCAmelCase__							(						self						:Tuple ):





										"""simple docstring"""

										lowerCamelCase__ ,       lowerCamelCase__			:  Union[str, Any]  =self.model_tester.prepare_config_and_inputs_for_common()

										lowerCamelCase__			:  List[Any]  =_config_zero_init(lowerCamelCase_ )
										for model_class in self.all_model_classes:
													lowerCamelCase__			:  Optional[int]  =model_class(config=lowerCamelCase_ )
													for name, param in model.named_parameters():
																if "embeddings" not in name and param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1e9).round() / 1e9).item()  ,							[0.0, 1.0]  ,							msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""  ,							)






@require_vision
@require_torch
class 					A_			(  unittest.TestCase     ):



							"""simple docstring"""






							@cached_property
							def    UpperCAmelCase__							(						self						:Union[str, Any] ):





										"""simple docstring"""

										return (
										    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224' )
										    if is_vision_available()
										    else None
										)





							@slow
							def    UpperCAmelCase__							(						self						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  int  =SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224' ).to(lowerCamelCase_ )
										lowerCamelCase__			:  Dict  =self.default_image_processor

										lowerCamelCase__			:  Tuple  =Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
										lowerCamelCase__			:  Dict  =image_processor(images=lowerCamelCase_  ,							return_tensors='pt' ).to(lowerCamelCase_ )

										# forward pass
										with torch.no_grad():
													lowerCamelCase__			:  List[Any]  =model(**lowerCamelCase_ )

										# verify the logits
										lowerCamelCase__			:  List[str]  =torch.Size((1, 1_000) )
										self.assertEqual(outputs.logits.shape  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Optional[Any]  =torch.tensor([-0.09_48, -0.64_54, -0.09_21] ).to(lowerCamelCase_ )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,							lowerCamelCase_  ,							atol=1e-4 ) )






@require_torch
class 					A_			(  unittest.TestCase   ,      A__     ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_			   =     (SwinBackbone,) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE_			   =     SwinConfig





							def    UpperCAmelCase__							(						self						:Dict ):





										"""simple docstring"""

										lowerCamelCase__			:  Optional[int]  =SwinModelTester(self )

"""simple docstring"""


import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
							import tensorflow as tf

							tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
							import torch

if is_datasets_available():
							from datasets import load_dataset


lowerCAmelCase			=		5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors






def      lowerCAmelCase_     (	snake_case_	:    Namespace    )     ->Optional[int]:
			return PTtoTFCommand(
			    args.model_name							,					args.local_dir							,					args.max_error							,					args.new_weights							,					args.no_pr							,					args.push							,					args.extra_commit_description							,					args.override_model_class							,					)

class 					A_			(  A__     ):



							"""simple docstring"""






							@staticmethod
							def    UpperCAmelCase__							(						lowerCamelCase_						:ArgumentParser ):





										"""simple docstring"""

										lowerCamelCase__			:  Tuple  =parser.add_parser(
										    'pt-to-tf'  ,							help=(
										        'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
										        ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
										    )  ,							)
										train_parser.add_argument(
										    '--model-name'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							help='The model name, including owner/organization, as seen on the hub.'  ,							)
										train_parser.add_argument(
										    '--local-dir'  ,							type=lowerCamelCase_  ,							default=''  ,							help='Optional local directory of the model repository. Defaults to /tmp/{model_name}'  ,							)
										train_parser.add_argument(
										    '--max-error'  ,							type=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help=(
										        f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
										    )  ,							)
										train_parser.add_argument(
										    '--new-weights'  ,							action='store_true'  ,							help='Optional flag to create new TensorFlow weights, even if they already exist.'  ,							)
										train_parser.add_argument(
										    '--no-pr'  ,							action='store_true'  ,							help='Optional flag to NOT open a PR with converted weights.' )
										train_parser.add_argument(
										    '--push'  ,							action='store_true'  ,							help='Optional flag to push the weights directly to `main` (requires permissions)'  ,							)
										train_parser.add_argument(
										    '--extra-commit-description'  ,							type=lowerCamelCase_  ,							default=''  ,							help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).'  ,							)
										train_parser.add_argument(
										    '--override-model-class'  ,							type=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='If you think you know better than the auto-detector, you can specify the model class here. '
										    'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.'  ,							)
										train_parser.set_defaults(func=lowerCamelCase_ )





							@staticmethod
							def    UpperCAmelCase__							(						lowerCamelCase_						:int  ,							lowerCamelCase_						:Optional[Any] ):





										"""simple docstring"""

										lowerCamelCase__			:  str  =set(pt_outputs.keys() )
										lowerCamelCase__			:  Union[str, Any]  =set(tf_outputs.keys() )
										if pt_out_attrs != tf_out_attrs:
													raise ValueError(
													    f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
													    f""" {tf_out_attrs})""" )

										# 2. For each output attribute, computes the difference
										def _find_pt_tf_differences(lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Any  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Tuple="" ):
													# If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
													# recursivelly, keeping the name of the attribute.
													if isinstance(lowerCamelCase_  ,							torch.Tensor ):
																lowerCamelCase__			:  List[str]  =np.max(np.abs(pt_out.numpy() - tf_out.numpy() ) )
																lowerCamelCase__			:  Dict  =tensor_difference
													else:
																lowerCamelCase__			:  List[str]  =attr_name
																for i, pt_item in enumerate(lowerCamelCase_ ):
																			# If it is a named attribute, we keep the name. Otherwise, just its index.
																			if isinstance(lowerCamelCase_  ,							lowerCamelCase_ ):
																						lowerCamelCase__			:  str  =root_name + pt_item
																						lowerCamelCase__			:  List[Any]  =tf_out[pt_item]
																						lowerCamelCase__			:  str  =pt_out[pt_item]
																			else:
																						lowerCamelCase__			:  Union[str, Any]  =root_name + f"""[{i}]"""
																						lowerCamelCase__			:  Tuple  =tf_out[i]
																			lowerCamelCase__			:  Any  =_find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )

													return differences

										return _find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_  ,							{} )





							def __init__(						self						:Union[str, Any]  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:float  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:bool  ,							lowerCamelCase_						:str  ,							lowerCamelCase_						:str  ,							*lowerCamelCase_						:Union[str, Any]  ,							):





										"""simple docstring"""

										lowerCamelCase__			:  Dict  =logging.get_logger('transformers-cli/pt_to_tf' )
										lowerCamelCase__			:  List[str]  =model_name
										lowerCamelCase__			:  str  =local_dir if local_dir else os.path.join('/tmp'  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Dict  =max_error
										lowerCamelCase__			:  Union[str, Any]  =new_weights
										lowerCamelCase__			:  Optional[Any]  =no_pr
										lowerCamelCase__			:  int  =push
										lowerCamelCase__			:  List[str]  =extra_commit_description
										lowerCamelCase__			:  int  =override_model_class





							def    UpperCAmelCase__							(						self						:Optional[int]  ,							lowerCamelCase_						:Dict  ,							lowerCamelCase_						:Optional[int]  ,							lowerCamelCase_						:Any ):





										"""simple docstring"""

										def _get_audio_input():
													lowerCamelCase__			:  int  =load_dataset('hf-internal-testing/librispeech_asr_dummy'  ,							'clean'  ,							split='validation' )
													lowerCamelCase__			:  Dict  =ds.sort('id' ).select(range(2 ) )[:2]['audio']
													lowerCamelCase__			:  List[str]  =[x['array'] for x in speech_samples]
													return raw_samples

										lowerCamelCase__			:  Union[str, Any]  =type(pt_model.config )
										if model_config_class in PROCESSOR_MAPPING:
													lowerCamelCase__			:  Dict  =AutoProcessor.from_pretrained(self._local_dir )
													if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
																lowerCamelCase__			:  Dict  =processor.tokenizer.eos_token
										elif model_config_class in IMAGE_PROCESSOR_MAPPING:
													lowerCamelCase__			:  Tuple  =AutoImageProcessor.from_pretrained(self._local_dir )
										elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
													lowerCamelCase__			:  List[Any]  =AutoFeatureExtractor.from_pretrained(self._local_dir )
										elif model_config_class in TOKENIZER_MAPPING:
													lowerCamelCase__			:  Optional[Any]  =AutoTokenizer.from_pretrained(self._local_dir )
													if processor.pad_token is None:
																lowerCamelCase__			:  Union[str, Any]  =processor.eos_token
										else:
													raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})""" )

										lowerCamelCase__			:  Tuple  =set(inspect.signature(pt_model.forward ).parameters.keys() )
										lowerCamelCase__			:  Any  ={}
										if "input_ids" in model_forward_signature:
													processor_inputs.update(
													    {
													        'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
													        'padding': True,
													        'truncation': True,
													    } )
										if "pixel_values" in model_forward_signature:
													lowerCamelCase__			:  List[str]  =load_dataset('cifar10'  ,							'plain_text'  ,							split='test' )[:2]['img']
													processor_inputs.update({'images': sample_images} )
										if "input_features" in model_forward_signature:
													lowerCamelCase__			:  Optional[Any]  =inspect.signature(processor.feature_extractor ).parameters
													# Pad to the largest input length by default but take feature extractor default
													# padding value if it exists e.g. "max_length" and is not False or None
													if "padding" in feature_extractor_signature:
																lowerCamelCase__			:  str  =feature_extractor_signature['padding'].default
																if default_strategy is not False and default_strategy is not None:
																			lowerCamelCase__			:  Tuple  =default_strategy
																else:
																			lowerCamelCase__			:  List[str]  =True
													else:
																lowerCamelCase__			:  Optional[int]  =True
													processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy} )
										if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
													processor_inputs.update({'audio': _get_audio_input(), 'padding': True} )
										lowerCamelCase__			:  str  =processor(**lowerCamelCase_  ,							return_tensors='pt' )
										lowerCamelCase__			:  Optional[int]  =processor(**lowerCamelCase_  ,							return_tensors='tf' )

										# Extra input requirements, in addition to the input modality
										if (
										    config.is_encoder_decoder
										    or (hasattr(lowerCamelCase_  ,							'encoder' ) and hasattr(lowerCamelCase_  ,							'decoder' ))
										    or "decoder_input_ids" in tf_dummy_inputs
										):
													lowerCamelCase__			:  Union[str, Any]  =np.asarray([[1], [1]]  ,							dtype=lowerCamelCase_ ) * (pt_model.config.decoder_start_token_id or 0)
													pt_input.update({'decoder_input_ids': torch.tensor(lowerCamelCase_ )} )
													tf_input.update({'decoder_input_ids': tf.convert_to_tensor(lowerCamelCase_ )} )

										return pt_input, tf_input





							def    UpperCAmelCase__							(						self						:List[Any] ):





										"""simple docstring"""

										if version.parse(huggingface_hub.__version__ ) < version.parse('0.9.0' ):
													raise ImportError(
													    'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
													    ' installation.' )
										else:
													from huggingface_hub import Repository, create_commit
													from huggingface_hub._commit_api import CommitOperationAdd

										# Fetch remote data
										lowerCamelCase__			:  Dict  =Repository(local_dir=self._local_dir  ,							clone_from=self._model_name )

										# Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
										lowerCamelCase__			:  Union[str, Any]  =AutoConfig.from_pretrained(self._local_dir )
										lowerCamelCase__			:  Optional[int]  =config.architectures
										if self._override_model_class is not None:
													if self._override_model_class.startswith('TF' ):
																lowerCamelCase__			:  List[Any]  =[self._override_model_class[2:]]
													else:
																lowerCamelCase__			:  str  =[self._override_model_class]
													try:
																lowerCamelCase__			:  str  =getattr(import_module('transformers' )  ,							architectures[0] )
													except AttributeError:
																raise ValueError(f"""Model class {self._override_model_class} not found in transformers.""" )
													try:
																lowerCamelCase__			:  Tuple  =getattr(import_module('transformers' )  ,							'TF' + architectures[0] )
													except AttributeError:
																raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers.""" )
										elif architectures is None:  # No architecture defined -- use auto classes
													lowerCamelCase__			:  List[str]  =getattr(import_module('transformers' )  ,							'AutoModel' )
													lowerCamelCase__			:  Union[str, Any]  =getattr(import_module('transformers' )  ,							'TFAutoModel' )
													self._logger.warning('No detected architecture, using AutoModel/TFAutoModel' )
										else:  # Architecture defined -- use it
													if len(lowerCamelCase_ ) > 1:
																raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})""" )
													self._logger.warning(f"""Detected architecture: {architectures[0]}""" )
													lowerCamelCase__			:  str  =getattr(import_module('transformers' )  ,							architectures[0] )
													try:
																lowerCamelCase__			:  Tuple  =getattr(import_module('transformers' )  ,							'TF' + architectures[0] )
													except AttributeError:
																raise AttributeError(f"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers.""" )

        # Check the TF dummy inputs to see what keys we need in the forward pass
										lowerCamelCase__			:  Any  =tf_class.from_config(lowerCamelCase_ )
										lowerCamelCase__			:  List[Any]  =tf_from_pt_model.dummy_inputs

										del tf_from_pt_model  # Try to keep only one model in memory at a time

										# Load the model and get some basic inputs
										lowerCamelCase__			:  List[str]  =pt_class.from_pretrained(self._local_dir )
										pt_model.eval()

										lowerCamelCase__ ,       lowerCamelCase__			:  Dict  =self.get_inputs(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_ )

										with torch.no_grad():
													lowerCamelCase__			:  int  =pt_model(**lowerCamelCase_  ,							output_hidden_states=lowerCamelCase_ )
										del pt_model  # will no longer be used, and may have a large memory footprint

										lowerCamelCase__			:  Tuple  =tf_class.from_pretrained(self._local_dir  ,							from_pt=lowerCamelCase_ )
										lowerCamelCase__			:  int  =tf_from_pt_model(**lowerCamelCase_  ,							output_hidden_states=lowerCamelCase_  ,							training=lowerCamelCase_ )

										# Confirms that cross loading PT weights into TF worked.
										lowerCamelCase__			:  Dict  =self.find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Optional[Any]  ={k: v for k, v in crossload_differences.items() if 'hidden' not in k}
										lowerCamelCase__			:  str  ={k: v for k, v in crossload_differences.items() if 'hidden' in k}
										if len(lowerCamelCase_ ) == 0 and architectures is not None:
													raise ValueError(
													    f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
													    ' output was found. All outputs start with \'hidden\'' )
										lowerCamelCase__			:  Any  =max(output_differences.values() ) if output_differences else 0.0
										lowerCamelCase__			:  List[Any]  =max(hidden_differences.values() )
										if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
													raise ValueError(
													    'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
													    + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error] )
													    + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error] ) )

										# Save the weights in a TF format (if needed) and confirms that the results are still good
										lowerCamelCase__			:  Union[str, Any]  =os.path.join(self._local_dir  ,							lowerCamelCase_ )
										lowerCamelCase__			:  List[str]  =os.path.join(self._local_dir  ,							lowerCamelCase_ )
										if (not os.path.exists(lowerCamelCase_ ) and not os.path.exists(lowerCamelCase_ )) or self._new_weights:
													tf_from_pt_model.save_pretrained(self._local_dir )
										del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

										lowerCamelCase__			:  Optional[int]  =tf_class.from_pretrained(self._local_dir )
										lowerCamelCase__			:  List[Any]  =tf_model(**lowerCamelCase_  ,							output_hidden_states=lowerCamelCase_ )

										lowerCamelCase__			:  Any  =self.find_pt_tf_differences(lowerCamelCase_  ,							lowerCamelCase_ )
										lowerCamelCase__			:  Tuple  ={k: v for k, v in conversion_differences.items() if 'hidden' not in k}
										lowerCamelCase__			:  Tuple  ={k: v for k, v in conversion_differences.items() if 'hidden' in k}
										if len(lowerCamelCase_ ) == 0 and architectures is not None:
													raise ValueError(
													    f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
													    ' output was found. All outputs start with \'hidden\'' )
										lowerCamelCase__			:  Any  =max(output_differences.values() ) if output_differences else 0.0
										lowerCamelCase__			:  Any  =max(hidden_differences.values() )
										if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
													raise ValueError(
													    'The converted TensorFlow model has different outputs, something went wrong!\n'
													    + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error] )
													    + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
													    + '\n'.join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error] ) )

										lowerCamelCase__			:  List[str]  ='Update TF weights' if self._new_weights else 'Add TF weights'
										if self._push:
													repo.git_add(auto_lfs_track=lowerCamelCase_ )
													repo.git_commit(lowerCamelCase_ )
													repo.git_push(blocking=lowerCamelCase_ )  # this prints a progress bar with the upload
													self._logger.warning(f"""TF weights pushed into {self._model_name}""" )
										elif not self._no_pr:
													self._logger.warning('Uploading the weights into a new PR...' )
													lowerCamelCase__			:  List[Any]  =(
													    'Model converted by the [`transformers`\' `pt_to_tf`'
													    ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
													    'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
													    f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
													    f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
													    f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
													    f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
													)
													if self._max_error > MAX_ERROR:
																commit_descrition += (
																    f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
																)
													if self._extra_commit_description:
																commit_descrition += "\n\n" + self._extra_commit_description

													# sharded model -> adds all related files (index and .h5 shards)
													if os.path.exists(lowerCamelCase_ ):
																lowerCamelCase__			:  Dict  =[
																    CommitOperationAdd(path_in_repo=lowerCamelCase_  ,							path_or_fileobj=lowerCamelCase_ )
																]
																for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5' ):
																			operations += [
																			    CommitOperationAdd(path_in_repo=os.path.basename(lowerCamelCase_ )  ,							path_or_fileobj=lowerCamelCase_ )
																			]
													else:
																lowerCamelCase__			:  Optional[Any]  =[CommitOperationAdd(path_in_repo=lowerCamelCase_  ,							path_or_fileobj=lowerCamelCase_ )]

													lowerCamelCase__			:  Tuple  =create_commit(
													    repo_id=self._model_name  ,							operations=lowerCamelCase_  ,							commit_message=lowerCamelCase_  ,							commit_description=lowerCamelCase_  ,							repo_type='model'  ,							create_pr=lowerCamelCase_  ,							).pr_url
													self._logger.warning(f"""PR open in {hub_pr_url}""" )