






'''simple docstring'''



import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_    =   get_tests_dir("fixtures/spiece.model")

@require_sentencepiece
@require_tokenizers
class 			SCREAMING_SNAKE_CASE					(							UpperCAmelCase_      ,unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				ErnieMTokenizer
      SCREAMING_SNAKE_CASE__ :				Optional[int]    =				False
      SCREAMING_SNAKE_CASE__ :				Tuple    =				True
      SCREAMING_SNAKE_CASE__ :				int    =				False
      SCREAMING_SNAKE_CASE__ :				Any    =				False




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          super().setUp()

          # We have a SentencePiece fixture for testing
          _snake_case : Tuple												=		ErnieMTokenizer(snake_case     ,					unk_token='<unk>'     ,					pad_token='<pad>'   )
          tokenizer.save_pretrained(self.tmpdirname   )




      def 	__UpperCAmelCase			(					self :		Dict     ,					snake_case :		Tuple   ):



          """simple docstring"""





          _snake_case : str												=		'this is a test'
          _snake_case : List[str]												=		'this is a test'
          return input_text, output_text




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Dict												=		'<pad>'
          _snake_case : Union[str, Any]												=		0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case   )     ,					snake_case   )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case   )     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		List[Any]   ):



          """simple docstring"""





          _snake_case : List[str]												=		list(self.get_tokenizer().get_vocab().keys()   )

          self.assertEqual(vocab_keys[0]     ,					'<pad>'   )
          self.assertEqual(vocab_keys[1]     ,					'<unk>'   )
          self.assertEqual(vocab_keys[-1]     ,					'▁eloquent'   )
          self.assertEqual(len(snake_case   )     ,					30000   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          self.assertEqual(self.get_tokenizer().vocab_size     ,					30000   )




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          if not self.test_rust_tokenizer:
              return

          _snake_case : List[str]												=		self.get_tokenizer()
          _snake_case : List[str]												=		self.get_rust_tokenizer()

          _snake_case : Union[str, Any]												=		'I was born in 92000, and this is falsé.'

          _snake_case : Union[str, Any]												=		tokenizer.tokenize(snake_case   )
          _snake_case : Optional[int]												=		rust_tokenizer.tokenize(snake_case   )
          self.assertListEqual(snake_case     ,					snake_case   )

          _snake_case : Dict												=		tokenizer.encode(snake_case     ,					add_special_tokens=snake_case   )
          _snake_case : List[str]												=		rust_tokenizer.encode(snake_case     ,					add_special_tokens=snake_case   )
          self.assertListEqual(snake_case     ,					snake_case   )

          _snake_case : List[str]												=		self.get_rust_tokenizer()
          _snake_case : Tuple												=		tokenizer.encode(snake_case   )
          _snake_case : List[Any]												=		rust_tokenizer.encode(snake_case   )
          self.assertListEqual(snake_case     ,					snake_case   )




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



          """simple docstring"""





          _snake_case : Any												=		ErnieMTokenizer(snake_case     ,					do_lower_case=snake_case     ,					unk_token='<unk>'     ,					pad_token='<pad>'   )

          _snake_case : int												=		tokenizer.tokenize('This is a test'   )
          self.assertListEqual(snake_case     ,					['▁this', '▁is', '▁a', '▁test']   )

          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case   )     ,					[48, 25, 21, 1289]   )

          _snake_case : int												=		tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
          # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
          self.assertListEqual(
              snake_case     ,					['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']   )
          _snake_case : int												=		tokenizer.convert_tokens_to_ids(snake_case   )
          self.assertListEqual(snake_case     ,					[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]   )

          _snake_case : Optional[Any]												=		tokenizer.convert_ids_to_tokens(snake_case   )
          self.assertListEqual(
              snake_case     ,					['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']     ,					)




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : str												=		ErnieMTokenizer(snake_case     ,					unk_token='<unk>'     ,					pad_token='<pad>'   )

          _snake_case : Tuple												=		tokenizer.encode('sequence builders'   )
          _snake_case : int												=		tokenizer.encode('multi-sequence build'   )

          _snake_case : str												=		tokenizer.build_inputs_with_special_tokens(snake_case   )
          _snake_case : Any												=		tokenizer.build_inputs_with_special_tokens(snake_case     ,					snake_case   )

          assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
          assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
              tokenizer.sep_token_id
          ] + text_a + [tokenizer.sep_token_id]




      @slow
      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Tuple												=		{'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=snake_case     ,					model_name='susnato/ernie-m-base_pytorch'     ,					sequences=[
                  'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
                  'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
                  'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
                  'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
                  'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
                  'conditioning on both left and right context in all layers.',
                  'The quick brown fox jumps over the lazy dog.',
              ]     ,					)






'''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 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






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



          """simple docstring"""





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




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          _snake_case : Optional[int]												=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

          _snake_case : Optional[int]												=		self.get_config()

          return config, pixel_values, labels




      def 	__UpperCAmelCase			(					self :		Any   ):



          """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 :		Tuple     ,					snake_case :		Any     ,					snake_case :		str     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : Any												=		TFSwinModel(config=snake_case   )
          _snake_case : List[str]												=		model(snake_case   )

          _snake_case : Tuple												=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
          _snake_case : 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 :		Optional[Any]     ,					snake_case :		Union[str, Any]     ,					snake_case :		List[str]     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : Any												=		TFSwinForMaskedImageModeling(config=snake_case   )
          _snake_case : Tuple												=		model(snake_case   )
          self.parent.assertEqual(
              result.logits.shape     ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

          # test greyscale images
          _snake_case : Any												=		1
          _snake_case : Any												=		TFSwinForMaskedImageModeling(snake_case   )

          _snake_case : int												=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
          _snake_case : List[Any]												=		model(snake_case   )
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, 1, self.image_size, self.image_size)   )




      def 	__UpperCAmelCase			(					self :		Any     ,					snake_case :		str     ,					snake_case :		int     ,					snake_case :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : List[str]												=		self.type_sequence_label_size
          _snake_case : int												=		TFSwinForImageClassification(snake_case   )
          _snake_case : int												=		model(snake_case     ,					labels=snake_case   )
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)   )

          # test greyscale images
          _snake_case : Union[str, Any]												=		1
          _snake_case : Tuple												=		TFSwinForImageClassification(snake_case   )
          _snake_case : Any												=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
          _snake_case : Union[str, Any]												=		model(snake_case   )
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : str												=		self.prepare_config_and_inputs()
          _snake_case					,							_snake_case					,							_snake_case : Any												=		config_and_inputs
          _snake_case : Tuple												=		{'pixel_values': pixel_values}
          return config, inputs_dict



@require_tf
class 			SCREAMING_SNAKE_CASE					(							lowercase_      ,lowercase_      ,unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				List[Any]    =				(
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      SCREAMING_SNAKE_CASE__ :				List[Any]    =				(
          {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				False
      SCREAMING_SNAKE_CASE__ :				List[Any]    =				False
      SCREAMING_SNAKE_CASE__ :				str    =				False
      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				False




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : List[str]												=		TFSwinModelTester(self   )
          _snake_case : Tuple												=		ConfigTester(self     ,					config_class=snake_case     ,					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 :		str   ):



          """simple docstring"""





          return




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



          """simple docstring"""





          _snake_case : Tuple												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : Union[str, Any]												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case   )




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : List[Any]												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case   )




      @unittest.skip(reason='Swin does not use inputs_embeds'   )
      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          pass




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



          """simple docstring"""





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

          for model_class in self.all_model_classes:
              _snake_case : Any												=		model_class(snake_case   )
              self.assertIsInstance(model.get_input_embeddings()     ,					tf.keras.layers.Layer   )
              _snake_case : str												=		model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(snake_case     ,					tf.keras.layers.Dense   )   )




      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          _snake_case					,							_snake_case : Optional[int]												=		self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              _snake_case : Optional[int]												=		model_class(snake_case   )
              _snake_case : Any												=		inspect.signature(model.call   )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _snake_case : List[Any]												=		[*signature.parameters.keys()]

              _snake_case : List[Any]												=		['pixel_values']
              self.assertListEqual(arg_names[:1]     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          _snake_case					,							_snake_case : Dict												=		self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case : Any												=		True

          for model_class in self.all_model_classes:
              _snake_case : Optional[int]												=		True
              _snake_case : int												=		False
              _snake_case : Tuple												=		True
              _snake_case : Dict												=		model_class(snake_case   )
              _snake_case : int												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )
              _snake_case : Any												=		outputs.attentions
              _snake_case : Optional[int]												=		len(self.model_tester.depths   )
              self.assertEqual(len(snake_case   )     ,					snake_case   )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              _snake_case : Any												=		True
              _snake_case : str												=		config.window_size**2
              _snake_case : Optional[int]												=		model_class(snake_case   )
              _snake_case : Optional[int]												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )
              _snake_case : List[Any]												=		outputs.attentions
              self.assertEqual(len(snake_case   )     ,					snake_case   )

              self.assertListEqual(
                  list(attentions[0].shape[-3:]   )     ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,					)
              _snake_case : Optional[int]												=		len(snake_case   )

              # Check attention is always last and order is fine
              _snake_case : Any												=		True
              _snake_case : Tuple												=		True
              _snake_case : int												=		model_class(snake_case   )
              _snake_case : Dict												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )

              if hasattr(self.model_tester     ,					'num_hidden_states_types'   ):
                  _snake_case : Any												=		self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  _snake_case : Union[str, Any]												=		2
              self.assertEqual(out_len + added_hidden_states     ,					len(snake_case   )   )

              _snake_case : Tuple												=		outputs.attentions

              self.assertEqual(len(snake_case   )     ,					snake_case   )

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




      def 	__UpperCAmelCase			(					self :		int     ,					snake_case :		List[str]     ,					snake_case :		Union[str, Any]     ,					snake_case :		Dict     ,					snake_case :		Tuple   ):



          """simple docstring"""





          _snake_case : int												=		model_class(snake_case   )
          _snake_case : Any												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )
          _snake_case : Optional[int]												=		outputs.hidden_states

          _snake_case : List[str]												=		getattr(
              self.model_tester     ,					'expected_num_hidden_layers'     ,					len(self.model_tester.depths   ) + 1   )
          self.assertEqual(len(snake_case   )     ,					snake_case   )

          # Swin has a different seq_length
          _snake_case : Optional[int]												=		to_atuple(config.patch_size   )

          _snake_case : Optional[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]     ,					)

          _snake_case : Optional[int]												=		outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case   )     ,					snake_case   )

          _snake_case					,							_snake_case					,							_snake_case					,							_snake_case : List[str]												=		reshaped_hidden_states[0].shape

          _snake_case : Any												=		tf.reshape(reshaped_hidden_states[0]     ,					(batch_size, num_channels, height * width)   )
          _snake_case : List[Any]												=		tf.transpose(snake_case     ,					(0, 2, 1)   )

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




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case					,							_snake_case : List[Any]												=		self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case : Optional[int]												=		to_atuple(self.model_tester.image_size   )

          for model_class in self.all_model_classes:
              _snake_case : Dict												=		True
              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					snake_case   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _snake_case : Dict												=		True

              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					snake_case   )




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



          """simple docstring"""





          _snake_case					,							_snake_case : Tuple												=		self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case : Tuple												=		3

          _snake_case : Any												=		to_atuple(self.model_tester.image_size   )
          _snake_case : str												=		to_atuple(config.patch_size   )

          _snake_case : List[str]												=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          _snake_case : Tuple												=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
              _snake_case : Union[str, Any]												=		True
              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					(padded_height, padded_width)   )

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




      @slow
      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _snake_case : Optional[int]												=		TFSwinModel.from_pretrained(snake_case   )
              self.assertIsNotNone(snake_case   )



@require_vision
@require_tf
class 			SCREAMING_SNAKE_CASE					(							unittest.TestCase					):






      '''simple docstring'''






      @cached_property
      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





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




      @slow
      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Dict												=		TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   )
          _snake_case : str												=		self.default_image_processor

          _snake_case : List[str]												=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
          _snake_case : int												=		image_processor(images=snake_case     ,					return_tensors='tf'   )

          # forward pass
          _snake_case : Union[str, Any]												=		model(snake_case   )

          # verify the logits
          _snake_case : List[Any]												=		tf.TensorShape((1, 1000)   )
          self.assertEqual(outputs.logits.shape     ,					snake_case   )
          _snake_case : List[Any]												=		tf.constant([-0.0948, -0.6454, -0.0921]   )
          self.assertTrue(np.allclose(outputs.logits[0, :3]     ,					snake_case     ,					atol=1e-4   )   )



