



'''simple docstring'''

from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFViTForImageClassification, TFViTModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor



class 		lowerCamelCase__      :




					'''simple docstring'''



					def __init__(  self				:	List[Any]     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	List[Any]=13     ,  UpperCamelCase_				:	Dict=30     ,  UpperCamelCase_				:	Optional[Any]=2     ,  UpperCamelCase_				:	Optional[Any]=3     ,  UpperCamelCase_				:	Any=True     ,  UpperCamelCase_				:	List[str]=True     ,  UpperCamelCase_				:	Any=32     ,  UpperCamelCase_				:	str=2     ,  UpperCamelCase_				:	Tuple=4     ,  UpperCamelCase_				:	Any=37     ,  UpperCamelCase_				:	str="gelu"     ,  UpperCamelCase_				:	Dict=0.1     ,  UpperCamelCase_				:	int=0.1     ,  UpperCamelCase_				:	Union[str, Any]=10     ,  UpperCamelCase_				:	Tuple=0.02     ,  UpperCamelCase_				:	Optional[Any]=3     ,  UpperCamelCase_				:	Any=None     ,  )  ->       str:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		parent
						_lowercase					:       List[str]       =		batch_size
						_lowercase					:       Dict       =		image_size
						_lowercase					:       Optional[int]       =		patch_size
						_lowercase					:       List[str]       =		num_channels
						_lowercase					:       Optional[Any]       =		is_training
						_lowercase					:       Optional[Any]       =		use_labels
						_lowercase					:       Any       =		hidden_size
						_lowercase					:       Dict       =		num_hidden_layers
						_lowercase					:       Union[str, Any]       =		num_attention_heads
						_lowercase					:       int       =		intermediate_size
						_lowercase					:       str       =		hidden_act
						_lowercase					:       Union[str, Any]       =		hidden_dropout_prob
						_lowercase					:       Optional[int]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		type_sequence_label_size
						_lowercase					:       Dict       =		initializer_range
						_lowercase					:       List[Any]       =		scope

						# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
						_lowercase					:       int       =		(image_size // patch_size) ** 2
						_lowercase					:       int       =		num_patches + 1




					def 						__UpperCAmelCase     (  self				:	Any					)  ->       Tuple:



						'''simple docstring'''





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

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

						_lowercase					:       Any       =		self.get_config()

						return config, pixel_values, labels




					def 						__UpperCAmelCase     (  self				:	Tuple					)  ->       int:



						'''simple docstring'''





						return ViTConfig(
						    image_size=self.image_size     ,  patch_size=self.patch_size     ,  num_channels=self.num_channels     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  is_decoder=UpperCamelCase_     ,  initializer_range=self.initializer_range     ,  )




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[Any]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		TFViTModel(config=UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		model(UpperCamelCase_     ,  training=UpperCamelCase_					)
						self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)

						# Test with an image with different size than the one specified in config.
						_lowercase					:       Union[str, Any]       =		self.image_size // 2
						_lowercase					:       List[str]       =		pixel_values[:, :, :image_size, :image_size]
						_lowercase					:       str       =		model(UpperCamelCase_     ,  interpolate_pos_encoding=UpperCamelCase_     ,  training=UpperCamelCase_					)
						_lowercase					:       Dict       =		(image_size // self.patch_size) ** 2 + 1
						self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, seq_length, self.hidden_size)					)




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	List[str]     ,  UpperCamelCase_				:	List[Any]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.type_sequence_label_size
						_lowercase					:       int       =		TFViTForImageClassification(UpperCamelCase_					)
						_lowercase					:       Dict       =		model(UpperCamelCase_     ,  labels=UpperCamelCase_     ,  training=UpperCamelCase_					)
						self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)					)

						# Test with an image with different size than the one specified in config.
						_lowercase					:       List[Any]       =		self.image_size // 2
						_lowercase					:       Any       =		pixel_values[:, :, :image_size, :image_size]
						_lowercase					:       Optional[Any]       =		model(UpperCamelCase_     ,  interpolate_pos_encoding=UpperCamelCase_     ,  training=UpperCamelCase_					)
						self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)					)

						# test greyscale images
						_lowercase					:       Dict       =		1
						_lowercase					:       Union[str, Any]       =		TFViTForImageClassification(UpperCamelCase_					)
						_lowercase					:       Tuple       =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
						_lowercase					:       Optional[int]       =		model(UpperCamelCase_					)
						self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.type_sequence_label_size)					)




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



						'''simple docstring'''





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






@require_tf
class 		lowerCamelCase__      (						A	,   A	,   unittest.TestCase     ):




					'''simple docstring'''



					A_						    =						(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
					A_						    =						(
					    {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
					    if is_tf_available()
					    else {}
					)

					A_						    =						False
					A_						    =						False
					A_						    =						False




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



						'''simple docstring'''





						_lowercase					:       str       =		TFViTModelTester(self					)
						_lowercase					:       Dict       =		ConfigTester(self     ,  config_class=UpperCamelCase_     ,  has_text_modality=UpperCamelCase_     ,  hidden_size=37					)




					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Any:



						'''simple docstring'''





						self.config_tester.run_common_tests()




					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Dict:



						'''simple docstring'''





						pass




					@unittest.skip(reason='ViT does not use inputs_embeds'					)
					def 						__UpperCAmelCase     (  self				:	Union[str, Any]					)  ->       Any:



						'''simple docstring'''





						pass




					def 						__UpperCAmelCase     (  self				:	List[str]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Tuple       =		self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							_lowercase					:       Optional[int]       =		model_class(UpperCamelCase_					)
							self.assertIsInstance(model.get_input_embeddings()     ,  (tf.keras.layers.Layer)					)
							_lowercase					:       List[Any]       =		model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(UpperCamelCase_     ,  tf.keras.layers.Layer					)					)




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



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		self.model_tester.prepare_config_and_inputs_for_common()

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

							_lowercase					:       Tuple       =		['pixel_values']
							self.assertListEqual(arg_names[:1]     ,  UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*UpperCamelCase_					)




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



						'''simple docstring'''





						_lowercase					:       Any       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_					)




					@slow
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		TFViTModel.from_pretrained('google/vit-base-patch16-224'					)
						self.assertIsNotNone(UpperCamelCase_					)







def 						__UpperCamelCase				(							) ->			int:
	_lowercase					:       List[Any]       =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
	return image






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




					'''simple docstring'''



					@cached_property
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'					) if is_vision_available() else None




					@slow
					def 						__UpperCAmelCase     (  self				:	Optional[Any]					)  ->       str:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'					)

						_lowercase					:       List[Any]       =		self.default_image_processor
						_lowercase					:       Dict       =		prepare_img()
						_lowercase					:       int       =		image_processor(images=UpperCamelCase_     ,  return_tensors='tf'					)

						# forward pass
						_lowercase					:       Optional[int]       =		model(**UpperCamelCase_					)

						# verify the logits
						_lowercase					:       Optional[Any]       =		tf.TensorShape((1, 1000)					)
						self.assertEqual(outputs.logits.shape     ,  UpperCamelCase_					)

						_lowercase					:       str       =		tf.constant([-0.27_44, 0.82_15, -0.08_36]					)

						tf.debugging.assert_near(outputs.logits[0, :3]     ,  UpperCamelCase_     ,  atol=1E-4					)





'''simple docstring'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return f'''{i * "  "}*''' if i else "\n##"







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[Any]       =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(_lowercase					) or old_parts[i] != new_part) and new_part:
			print(f'''{md_prefix(_lowercase					)} {new_part.replace("_",				" "					).title()}'''					)
	return new_path







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


if __name__ == "__main__":
		print_directory_md('''.''')
