



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 		_lowercase		:

   def __init__(   self      :							Dict				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							List[str]=13				, __lowerCAmelCase      :							Union[str, Any]=32				, __lowerCAmelCase      :							List[str]=2				, __lowerCAmelCase      :							Optional[Any]=3				, __lowerCAmelCase      :							List[str]=16				, __lowerCAmelCase      :							Optional[Any]=[1, 2, 1]				, __lowerCAmelCase      :							Dict=[2, 2, 4]				, __lowerCAmelCase      :							Optional[int]=2				, __lowerCAmelCase      :							Any=2.0				, __lowerCAmelCase      :							int=True				, __lowerCAmelCase      :							Union[str, Any]=0.0				, __lowerCAmelCase      :							Tuple=0.0				, __lowerCAmelCase      :							Optional[int]=0.1				, __lowerCAmelCase      :							Union[str, Any]="gelu"				, __lowerCAmelCase      :							List[Any]=False				, __lowerCAmelCase      :							Tuple=True				, __lowerCAmelCase      :							List[str]=0.0_2				, __lowerCAmelCase      :							Optional[Any]=1E-5				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Optional[Any]=None				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							str=10				, __lowerCAmelCase      :							Any=8				, __lowerCAmelCase      :							List[str]=["stage1", "stage2"]				, __lowerCAmelCase      :							Optional[Any]=[1, 2]				, )							->					Optional[Any]:


    """simple docstring"""






    a							=		parent
    a							=		batch_size
    a							=		image_size
    a							=		patch_size
    a							=		num_channels
    a							=		embed_dim
    a							=		depths
    a							=		num_heads
    a							=		window_size
    a							=		mlp_ratio
    a							=		qkv_bias
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		drop_path_rate
    a							=		hidden_act
    a							=		use_absolute_embeddings
    a							=		patch_norm
    a							=		layer_norm_eps
    a							=		initializer_range
    a							=		is_training
    a							=		scope
    a							=		use_labels
    a							=		type_sequence_label_size
    a							=		encoder_stride
    a							=		out_features
    a							=		out_indices

   def 		A				(   self      :							List[Any]				)							->					Optional[int]:


    """simple docstring"""






    a							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

    a							=		None
    if self.use_labels:
     a							=		ids_tensor([self.batch_size]				, self.type_sequence_label_size				)

    a							=		self.get_config()

    return config, pixel_values, labels

   def 		A				(   self      :							Tuple				)							->					Tuple:


    """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 		A				(   self      :							List[Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							List[Any]				)							->					Dict:


    """simple docstring"""






    a							=		SwinModel(config=_A				)
    model.to(_A				)
    model.eval()
    a							=		model(_A				)

    a							=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths				) - 1))
    a							=		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 		A				(   self      :							Any				, __lowerCAmelCase      :							Tuple				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Union[str, Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		SwinBackbone(config=_A				)
    model.to(_A				)
    model.eval()
    a							=		model(_A				)

    # 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
    a							=		None
    a							=		SwinBackbone(config=_A				)
    model.to(_A				)
    model.eval()
    a							=		model(_A				)

    # 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 		A				(   self      :							Dict				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							List[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		SwinForMaskedImageModeling(config=_A				)
    model.to(_A				)
    model.eval()
    a							=		model(_A				)
    self.parent.assertEqual(
        result.logits.shape				, (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

    # test greyscale images
    a							=		1
    a							=		SwinForMaskedImageModeling(_A				)
    model.to(_A				)
    model.eval()

    a							=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    a							=		model(_A				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, 1, self.image_size, self.image_size)				)

   def 		A				(   self      :							List[str]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Optional[Any]				)							->					List[str]:


    """simple docstring"""






    a							=		self.type_sequence_label_size
    a							=		SwinForImageClassification(_A				)
    model.to(_A				)
    model.eval()
    a							=		model(_A				, labels=_A				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)				)

    # test greyscale images
    a							=		1
    a							=		SwinForImageClassification(_A				)
    model.to(_A				)
    model.eval()

    a							=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
    a							=		model(_A				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)				)




   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    (
        a
    )							=		config_and_inputs
    a							=		{'pixel_values': pixel_values}
    return config, inputs_dict



@require_torch
class 		_lowercase		(							snake_case_, snake_case_, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (
           SwinModel,
           SwinBackbone,
           SwinForImageClassification,
           SwinForMaskedImageModeling,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase  						=      (
       {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase  						=      True

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							List[str]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		SwinModelTester(self				)
    a							=		ConfigTester(self				, config_class=_A				, embed_dim=37				)

   def 		A				(   self      :							Union[str, Any]				)							->					Dict:


    """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 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return

   def 		A				(   self      :							Optional[int]				)							->					Optional[int]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_A				)

   @unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."				)
   def 		A				(   self      :							Tuple				)							->					Optional[Any]:


    """simple docstring"""






    pass

   def 		A				(   self      :							Tuple				)							->					List[Any]:


    """simple docstring"""






    super().test_training_gradient_checkpointing()

   def 		A				(   self      :							Any				)							->					Tuple:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*_A				)

   def 		A				(   self      :							Any				)							->					int:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*_A				)

   def 		A				(   self      :							Optional[Any]				)							->					str:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*_A				)

   @unittest.skip(reason="Swin does not use inputs_embeds"				)
   def 		A				(   self      :							List[Any]				)							->					Dict:


    """simple docstring"""






    pass

   @unittest.skip(reason="Swin Transformer does not use feedforward chunking"				)
   def 		A				(   self      :							List[str]				)							->					Optional[Any]:


    """simple docstring"""






    pass

   def 		A				(   self      :							Optional[int]				)							->					Dict:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

   def 		A				(   self      :							Optional[Any]				)							->					Tuple:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

     a							=		['pixel_values']
     self.assertListEqual(arg_names[:1]				, _A				)

   def 		A				(   self      :							Optional[int]				)							->					Any:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()
    a							=		True

    for model_class in self.all_model_classes:
     a							=		True
     a							=		False
     a							=		True
     a							=		model_class(_A				)
     model.to(_A				)
     model.eval()
     with torch.no_grad():
      a							=		model(**self._prepare_for_class(_A				, _A				)				)
     a							=		outputs.attentions
     a							=		len(self.model_tester.depths				)
     self.assertEqual(len(_A				)				, _A				)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     a							=		True
     a							=		config.window_size**2
     a							=		model_class(_A				)
     model.to(_A				)
     model.eval()
     with torch.no_grad():
      a							=		model(**self._prepare_for_class(_A				, _A				)				)
     a							=		outputs.attentions
     self.assertEqual(len(_A				)				, _A				)

     self.assertListEqual(
         list(attentions[0].shape[-3:]				)				, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				, )
     a							=		len(_A				)

     # Check attention is always last and order is fine
     a							=		True
     a							=		True
     a							=		model_class(_A				)
     model.to(_A				)
     model.eval()
     with torch.no_grad():
      a							=		model(**self._prepare_for_class(_A				, _A				)				)

     # also another +1 for reshaped_hidden_states
     a							=		1 if model_class.__name__ == 'SwinBackbone' else 2
     self.assertEqual(out_len + added_hidden_states				, len(_A				)				)

     a							=		outputs.attentions

     self.assertEqual(len(_A				)				, _A				)

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

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		model_class(_A				)
    model.to(_A				)
    model.eval()

    with torch.no_grad():
     a							=		model(**self._prepare_for_class(_A				, _A				)				)

    a							=		outputs.hidden_states

    a							=		getattr(
        self.model_tester				, "expected_num_hidden_layers"				, len(self.model_tester.depths				) + 1				)
    self.assertEqual(len(_A				)				, _A				)

    # Swin has a different seq_length
    a							=		(
        config.patch_size
        if isinstance(config.patch_size				, collections.abc.Iterable				)
        else (config.patch_size, config.patch_size)
    )

    a							=		(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":
     a							=		outputs.reshaped_hidden_states
     self.assertEqual(len(_A				)				, _A				)

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

   def 		A				(   self      :							Optional[int]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		(
        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:
     a							=		True
     self.check_hidden_states_output(_A				, _A				, _A				, _A				)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     a							=		True

     self.check_hidden_states_output(_A				, _A				, _A				, _A				)

   def 		A				(   self      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()
    a							=		3

    a							=		(
        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)
    )
    a							=		(
        config.patch_size
        if isinstance(config.patch_size				, collections.abc.Iterable				)
        else (config.patch_size, config.patch_size)
    )

    a							=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    a							=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
     a							=		True
     self.check_hidden_states_output(_A				, _A				, _A				, (padded_height, padded_width)				)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     a							=		True
     self.check_hidden_states_output(_A				, _A				, _A				, (padded_height, padded_width)				)

   @slow
   def 		A				(   self      :							int				)							->					Optional[Any]:


    """simple docstring"""






    for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a							=		SwinModel.from_pretrained(_A				)
     self.assertIsNotNone(_A				)




   def 		A				(   self      :							Optional[Any]				)							->					Union[str, Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		_config_zero_init(_A				)
    for model_class in self.all_model_classes:
     a							=		model_class(config=_A				)
     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 		_lowercase		(							unittest.TestCase						):

   @cached_property
   def 		A				(   self      :							List[Any]				)							->					List[str]:


    """simple docstring"""






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




   @slow
   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    a							=		SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"				).to(_A				)
    a							=		self.default_image_processor

    a							=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
    a							=		image_processor(images=_A				, return_tensors="pt"				).to(_A				)

    # forward pass
    with torch.no_grad():
     a							=		model(**_A				)

    # verify the logits
    a							=		torch.Size((1, 1000)				)
    self.assertEqual(outputs.logits.shape				, _A				)
    a							=		torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]				).to(_A				)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]				, _A				, atol=1E-4				)				)



@require_torch
class 		_lowercase		(							unittest.TestCase, snake_case_						):
   _UpperCAmelCase  						=      (SwinBackbone,) if is_torch_available() else ()
   _UpperCAmelCase  						=      SwinConfig

   def 		A				(   self      :							List[Any]				)							->					Any:


    """simple docstring"""






    a							=		SwinModelTester(self				)








import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						@patch('sys.argv'		, ['fakeprogrampath', 'env']						)
						def UpperCAmelCase_					(   self   :   Tuple						)				->     Dict:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							with CaptureStd() as cs:
								transformers.commands.transformers_cli.main()
							self.assertIn('Python version'		, cs.out						)
							self.assertIn('Platform'		, cs.out						)
							self.assertIn('Using distributed or parallel set-up in script?'		, cs.out						)






						@is_pt_tf_cross_test
						@patch(
						    'sys.argv'		, ['fakeprogrampath', 'pt-to-tf', '--model-name', 'hf-internal-testing/tiny-random-gptj', '--no-pr']						)
						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Optional[Any]:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		, ignore_errors=_A						)  # cleans potential past runs
							transformers.commands.transformers_cli.main()

							# The original repo has no TF weights -- if they exist, they were created by the CLI
							self.assertTrue(os.path.exists('/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'						)						)






						@require_torch
						@patch('sys.argv'		, ['fakeprogrampath', 'download', 'hf-internal-testing/tiny-random-gptj', '--cache-dir', '/tmp']						)
						def UpperCAmelCase_					(   self   :   List[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--tiny-random-gptj'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/refs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'						)						)






						@require_torch
						@patch(
						    'sys.argv'		, [
						        'fakeprogrampath',
						        'download',
						        'hf-internal-testing/test_dynamic_model_with_tokenizer',
						        '--trust-remote-code',
						        '--cache-dir',
						        '/tmp',
						    ]		, )
						def UpperCAmelCase_					(   self   :   Optional[Any]						)				->     str:







							"""simple docstring"""


							import transformers.commands.transformers_cli

							# # remove any previously downloaded model to start clean
							shutil.rmtree('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'		, ignore_errors=_A						)

							# run the command
							transformers.commands.transformers_cli.main()

							# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'						)						)
							self.assertTrue(os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'						)						)
							self.assertTrue(
							    os.path.exists('/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'						)						)

