code: 
  
  
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase			:		Any =			{
      'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
      'tokenization_layoutlm': ['LayoutLMTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowercase			:		Any =			['LayoutLMTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowercase			:		str =			[
  				    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'LayoutLMForMaskedLM',
  				    'LayoutLMForSequenceClassification',
  				    'LayoutLMForTokenClassification',
  				    'LayoutLMForQuestionAnswering',
  				    'LayoutLMModel',
  				    'LayoutLMPreTrainedModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__lowercase			:		List[str] =			[
  				    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'TFLayoutLMForMaskedLM',
  				    'TFLayoutLMForSequenceClassification',
  				    'TFLayoutLMForTokenClassification',
  				    'TFLayoutLMForQuestionAnswering',
  				    'TFLayoutLMMainLayer',
  				    'TFLayoutLMModel',
  				    'TFLayoutLMPreTrainedModel',
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
  				from .tokenization_layoutlm import LayoutLMTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_layoutlm import (
  								    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    LayoutLMForMaskedLM,
  								    LayoutLMForQuestionAnswering,
  								    LayoutLMForSequenceClassification,
  								    LayoutLMForTokenClassification,
  								    LayoutLMModel,
  								    LayoutLMPreTrainedModel,
  								)
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_layoutlm import (
  								    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    TFLayoutLMForMaskedLM,
  								    TFLayoutLMForQuestionAnswering,
  								    TFLayoutLMForSequenceClassification,
  								    TFLayoutLMForTokenClassification,
  								    TFLayoutLMMainLayer,
  								    TFLayoutLMModel,
  								    TFLayoutLMPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__lowercase			:		Tuple =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  import numpy as np
  
  from transformers import SwinConfig
  from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
  from transformers.utils import cached_property, is_tf_available, is_vision_available
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  	import tensorflow as tf
  
  	from transformers.models.swin.modeling_tf_swin import (
  	    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
  	    TFSwinForImageClassification,
  	    TFSwinForMaskedImageModeling,
  	    TFSwinModel,
  	)
  
  
  if is_vision_available():
  	from PIL import Image
  
  	from transformers import AutoImageProcessor
  
  
  
  
  
  class 							__UpperCamelCase       :
  
  
  
  
  	def __init__(     self		,       __a		,       __a=13		,       __a=32		,       __a=2		,       __a=3		,       __a=16		,       __a=[1, 2, 1]		,       __a=[2, 2, 4]		,       __a=2		,       __a=2.0		,       __a=True		,       __a=0.0		,       __a=0.0		,       __a=0.1		,       __a="gelu"		,       __a=False		,       __a=True		,       __a=0.02		,       __a=1E-5		,       __a=True		,       __a=None		,       __a=True		,       __a=10		,       __a=8		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						parent
  						__a    : int      					=						batch_size
  						__a    : str      					=						image_size
  						__a    : List[Any]      					=						patch_size
  						__a    : Any      					=						num_channels
  						__a    : int      					=						embed_dim
  						__a    : Optional[Any]      					=						depths
  						__a    : Optional[Any]      					=						num_heads
  						__a    : List[Any]      					=						window_size
  						__a    : Dict      					=						mlp_ratio
  						__a    : Any      					=						qkv_bias
  						__a    : Any      					=						hidden_dropout_prob
  						__a    : Union[str, Any]      					=						attention_probs_dropout_prob
  						__a    : str      					=						drop_path_rate
  						__a    : Dict      					=						hidden_act
  						__a    : List[str]      					=						use_absolute_embeddings
  						__a    : str      					=						patch_norm
  						__a    : List[str]      					=						layer_norm_eps
  						__a    : Optional[Any]      					=						initializer_range
  						__a    : str      					=						is_training
  						__a    : Any      					=						scope
  						__a    : Union[str, Any]      					=						use_labels
  						__a    : Optional[int]      					=						type_sequence_label_size
  						__a    : Optional[Any]      					=						encoder_stride
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
  
  						__a    : List[Any]      					=						None
  						if self.use_labels:
  											__a    : str      					=						ids_tensor([self.batch_size]		,       self.type_sequence_label_size							)
  
  						__a    : int      					=						self.get_config()
  
  						return config, pixel_values, labels
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''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		,       )
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						TFSwinModel(config=__a							)
  						__a    : List[str]      					=						model(__a							)
  
  						__a    : Dict      					=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
  						__a    : Any      					=						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		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						TFSwinForMaskedImageModeling(config=__a							)
  						__a    : Tuple      					=						model(__a							)
  						self.parent.assertEqual(
  						    result.logits.shape		,       (self.batch_size, self.num_channels, self.image_size, self.image_size)							)
  
  						# test greyscale images
  						__a    : str      					=						1
  						__a    : Union[str, Any]      					=						TFSwinForMaskedImageModeling(__a							)
  
  						__a    : Optional[int]      					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  						__a    : Any      					=						model(__a							)
  						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, 1, self.image_size, self.image_size)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						self.type_sequence_label_size
  						__a    : Tuple      					=						TFSwinForImageClassification(__a							)
  						__a    : Any      					=						model(__a		,       labels=__a							)
  						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)							)
  
  						# test greyscale images
  						__a    : Tuple      					=						1
  						__a    : str      					=						TFSwinForImageClassification(__a							)
  						__a    : List[Any]      					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  						__a    : List[str]      					=						model(__a							)
  						self.parent.assertEqual(result.logits.shape		,       (self.batch_size, self.type_sequence_label_size)							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						self.prepare_config_and_inputs()
  						__a					,       __a					,       __a    : Tuple      					=						config_and_inputs
  						__a    : Dict      					=						{'pixel_values': pixel_values}
  						return config, inputs_dict
  
  
  
  
  
  @require_tf
  class 							__UpperCamelCase       (	lowerCAmelCase_	,   lowerCAmelCase_	,   unittest.TestCase      ):
  	A_			    =					(
  	    (
  	        TFSwinModel,
  	        TFSwinForImageClassification,
  	        TFSwinForMaskedImageModeling,
  	    )
  	    if is_tf_available()
  	    else ()
  	)
  	A_			    =					(
  	    {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
  	    if is_tf_available()
  	    else {}
  	)
  
  	A_			    =					False
  	A_			    =					False
  	A_			    =					False
  	A_			    =					False
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[int]      					=						TFSwinModelTester(self							)
  						__a    : Union[str, Any]      					=						ConfigTester(self		,       config_class=__a		,       embed_dim=37							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''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							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_model(*__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						self.model_tester.prepare_config_and_inputs()
  						self.model_tester.create_and_check_for_masked_image_modeling(*__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						pass
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : int      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  											__a    : Dict      					=						model_class(__a							)
  											self.assertIsInstance(model.get_input_embeddings()		,       tf.keras.layers.Layer							)
  											__a    : str      					=						model.get_output_embeddings()
  											self.assertTrue(x is None or isinstance(__a		,       tf.keras.layers.Dense							)							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Dict      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  											__a    : Optional[int]      					=						model_class(__a							)
  											__a    : Union[str, Any]      					=						inspect.signature(model.call							)
  											# signature.parameters is an OrderedDict => so arg_names order is deterministic
  											__a    : Optional[int]      					=						[*signature.parameters.keys()]
  
  											__a    : str      					=						['pixel_values']
  											self.assertListEqual(arg_names[:1]		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Any      					=						self.model_tester.prepare_config_and_inputs_for_common()
  						__a    : Optional[int]      					=						True
  
  						for model_class in self.all_model_classes:
  											__a    : Optional[int]      					=						True
  											__a    : Dict      					=						False
  											__a    : Any      					=						True
  											__a    : Tuple      					=						model_class(__a							)
  											__a    : List[str]      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  											__a    : int      					=						outputs.attentions
  											__a    : List[Any]      					=						len(self.model_tester.depths							)
  											self.assertEqual(len(__a							)		,       __a							)
  
  											# check that output_attentions also work using config
  											del inputs_dict["output_attentions"]
  											__a    : Any      					=						True
  											__a    : List[Any]      					=						config.window_size**2
  											__a    : Optional[Any]      					=						model_class(__a							)
  											__a    : Tuple      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  											__a    : str      					=						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    : Any      					=						len(__a							)
  
  											# Check attention is always last and order is fine
  											__a    : int      					=						True
  											__a    : Any      					=						True
  											__a    : str      					=						model_class(__a							)
  											__a    : str      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  
  											if hasattr(self.model_tester		,       'num_hidden_states_types'							):
  																__a    : Optional[int]      					=						self.model_tester.num_hidden_states_types
  											else:
  																# also another +1 for reshaped_hidden_states
  																__a    : Union[str, Any]      					=						2
  											self.assertEqual(out_len + added_hidden_states		,       len(__a							)							)
  
  											__a    : Dict      					=						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    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						model_class(__a							)
  						__a    : int      					=						model(**self._prepare_for_class(__a		,       __a							)							)
  						__a    : Tuple      					=						outputs.hidden_states
  
  						__a    : Any      					=						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    : Optional[int]      					=						to_atuple(config.patch_size							)
  
  						__a    : List[str]      					=						(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]		,       )
  
  						__a    : str      					=						outputs.reshaped_hidden_states
  						self.assertEqual(len(__a							)		,       __a							)
  
  						__a					,       __a					,       __a					,       __a    : str      					=						reshaped_hidden_states[0].shape
  
  						__a    : int      					=						tf.reshape(reshaped_hidden_states[0]		,       (batch_size, num_channels, height * width)							)
  						__a    : List[str]      					=						tf.transpose(__a		,       (0, 2, 1)							)
  
  						self.assertListEqual(
  						    list(reshaped_hidden_states.shape[-2:]							)		,       [num_patches, self.model_tester.embed_dim]		,       )
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Optional[Any]      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						__a    : Union[str, Any]      					=						to_atuple(self.model_tester.image_size							)
  
  						for model_class in self.all_model_classes:
  											__a    : Optional[int]      					=						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    : Tuple      					=						True
  
  											self.check_hidden_states_output(__a		,       __a		,       __a		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[str]      					=						self.model_tester.prepare_config_and_inputs_for_common()
  						__a    : List[str]      					=						3
  
  						__a    : Any      					=						to_atuple(self.model_tester.image_size							)
  						__a    : Any      					=						to_atuple(config.patch_size							)
  
  						__a    : Any      					=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  						__a    : Optional[int]      					=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
  						for model_class in self.all_model_classes:
  											__a    : Union[str, Any]      					=						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    : int      					=						True
  											self.check_hidden_states_output(__a		,       __a		,       __a		,       (padded_height, padded_width)							)
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  											__a    : List[Any]      					=						TFSwinModel.from_pretrained(__a							)
  											self.assertIsNotNone(__a							)
  
  
  
  
  
  @require_vision
  @require_tf
  class 							__UpperCamelCase       (	unittest.TestCase      ):
  
  
  
  
  	@cached_property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return (
  						    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
  						    if is_vision_available()
  						    else None
  						)
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Optional[Any]      					=						TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
  						__a    : str      					=						self.default_image_processor
  
  						__a    : Optional[Any]      					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
  						__a    : Optional[Any]      					=						image_processor(images=__a		,       return_tensors='tf'							)
  
  						# forward pass
  						__a    : List[Any]      					=						model(__a							)
  
  						# verify the logits
  						__a    : List[str]      					=						tf.TensorShape((1, 1000)							)
  						self.assertEqual(outputs.logits.shape		,       __a							)
  						__a    : Optional[Any]      					=						tf.constant([-0.0948, -0.6454, -0.0921]							)
  						self.assertTrue(np.allclose(outputs.logits[0, :3]		,       __a		,       atol=1E-4							)							)
  
  
  
  