



import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, 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 NatBackbone, NatForImageClassification, NatModel
     from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							List[str]=13				, __lowerCAmelCase      :							Dict=64				, __lowerCAmelCase      :							List[str]=4				, __lowerCAmelCase      :							int=3				, __lowerCAmelCase      :							Dict=16				, __lowerCAmelCase      :							Any=[1, 2, 1]				, __lowerCAmelCase      :							Optional[Any]=[2, 4, 8]				, __lowerCAmelCase      :							str=3				, __lowerCAmelCase      :							Any=2.0				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Optional[Any]=0.0				, __lowerCAmelCase      :							List[Any]=0.0				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Any="gelu"				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Union[str, Any]=0.0_2				, __lowerCAmelCase      :							Any=1E-5				, __lowerCAmelCase      :							Any=True				, __lowerCAmelCase      :							Optional[int]=None				, __lowerCAmelCase      :							Optional[int]=True				, __lowerCAmelCase      :							Optional[int]=10				, __lowerCAmelCase      :							List[str]=["stage1", "stage2"]				, __lowerCAmelCase      :							List[str]=[1, 2]				, )							->					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							=		kernel_size
    a							=		mlp_ratio
    a							=		qkv_bias
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		drop_path_rate
    a							=		hidden_act
    a							=		patch_norm
    a							=		layer_norm_eps
    a							=		initializer_range
    a							=		is_training
    a							=		scope
    a							=		use_labels
    a							=		num_labels
    a							=		out_features
    a							=		out_indices

   def 		A				(   self      :							int				)							->					Dict:


    """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.num_labels				)

    a							=		self.get_config()

    return config, pixel_values, labels

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    return NatConfig(
        num_labels=self.num_labels				, 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				, kernel_size=self.kernel_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				, patch_norm=self.patch_norm				, layer_norm_eps=self.layer_norm_eps				, initializer_range=self.initializer_range				, out_features=self.out_features				, out_indices=self.out_indices				, )

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


    """simple docstring"""






    a							=		NatModel(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				)

    a							=		a							=		(config.image_size // config.patch_size) // (2 ** (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_height, expected_width, expected_dim)				)

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


    """simple docstring"""






    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		model(__lowerCAmelCase				, labels=__lowerCAmelCase				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

    # test greyscale images
    a							=		1
    a							=		NatForImageClassification(__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()

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

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


    """simple docstring"""






    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		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
    a							=		None
    a							=		NatBackbone(config=__lowerCAmelCase				)
    model.to(__lowerCAmelCase				)
    model.eval()
    a							=		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 		A				(   self      :							int				)							->					int:


    """simple docstring"""






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



@require_natten
@require_torch
class 		_lowercase		(							UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (
           NatModel,
           NatForImageClassification,
           NatBackbone,
       )
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase  						=      (
       {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase  						=      False

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							Dict				)							->					str:


    """simple docstring"""






    a							=		NatModelTester(self				)
    a							=		ConfigTester(self				, config_class=__lowerCAmelCase				, embed_dim=37				)

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


    """simple docstring"""






    return

   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






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

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


    """simple docstring"""






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

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


    """simple docstring"""






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

   @unittest.skip(reason="Nat does not use inputs_embeds"				)
   def 		A				(   self      :							int				)							->					Optional[Any]:


    """simple docstring"""






    pass

   @unittest.skip(reason="Nat does not use feedforward chunking"				)
   def 		A				(   self      :							int				)							->					Dict:


    """simple docstring"""






    pass

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


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

   def 		A				(   self      :							Any				)							->					Tuple:


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     a							=		model_class(__lowerCAmelCase				)
     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]				, __lowerCAmelCase				)

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


    """simple docstring"""






    self.skipTest("Nat's attention operation is handled entirely by NATTEN."				)

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


    """simple docstring"""






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

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

    a							=		outputs.hidden_states

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

    # Nat 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[0] // patch_size[0]
    a							=		image_size[1] // patch_size[1]

    self.assertListEqual(
        list(hidden_states[0].shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

    if model_class.__name__ != "NatBackbone":
     a							=		outputs.reshaped_hidden_states
     self.assertEqual(len(__lowerCAmelCase				)				, __lowerCAmelCase				)

     a		,	a		,	a		,	a							=		reshaped_hidden_states[0].shape
     a							=		(
         reshaped_hidden_states[0].view(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				).permute(0				, 2				, 3				, 1				)
     )
     self.assertListEqual(
         list(reshaped_hidden_states.shape[-3:]				)				, [height, width, self.model_tester.embed_dim]				, )

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


    """simple docstring"""






    a		,	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(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

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

     self.check_hidden_states_output(__lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				, __lowerCAmelCase				)

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


    """simple docstring"""






    for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a							=		NatModel.from_pretrained(__lowerCAmelCase				)
     self.assertIsNotNone(__lowerCAmelCase				)




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


    """simple docstring"""






    a		,	a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		_config_zero_init(__lowerCAmelCase				)
    for model_class in self.all_model_classes:
     a							=		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_natten
@require_vision
@require_torch
class 		_lowercase		(							unittest.TestCase						):

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


    """simple docstring"""






    return AutoImageProcessor.from_pretrained("shi-labs/nat-mini-in1k-224"				) if is_vision_available() else None




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


    """simple docstring"""






    a							=		NatForImageClassification.from_pretrained("shi-labs/nat-mini-in1k-224"				).to(__lowerCAmelCase				)
    a							=		self.default_image_processor

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

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

    # verify the logits
    a							=		torch.Size((1, 1000)				)
    self.assertEqual(outputs.logits.shape				, __lowerCAmelCase				)
    a							=		torch.tensor([0.3_8_0_5, -0.8_6_7_6, -0.3_9_1_2]				).to(__lowerCAmelCase				)
    self.assertTrue(torch.allclose(outputs.logits[0, :3]				, __lowerCAmelCase				, atol=1E-4				)				)



@require_torch
@require_natten
class 		_lowercase		(							unittest.TestCase, UpperCAmelCase__						):
   _UpperCAmelCase  						=      (NatBackbone,) if is_torch_available() else ()
   _UpperCAmelCase  						=      NatConfig

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		NatModelTester(self				)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

						super().__init__(pad_token_id=lowerCamelCase_						,			**lowerCamelCase_						)

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.task == "multiple-choice":
											a   	=  {0: "batch", 1: "choice", 2: "sequence"}
						else:
											a   	=  {0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ]						)
