






"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
						    TFDeiTForImageClassification,
						    TFDeiTForImageClassificationWithTeacher,
						    TFDeiTForMaskedImageModeling,
						    TFDeiTModel,
						)
						from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DeiTImageProcessor






class 							__SCREAMING_SNAKE_CASE						:


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





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

													# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
													A__						:  int		  =  (image_size // patch_size) ** 2
													A__						:  Optional[Any]		  =  num_patches + 2


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





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

													A__						:  Tuple		  =  None
													if self.use_labels:
																			A__						:  str		  =  ids_tensor([self.batch_size]			,    self.type_sequence_label_size							)

													A__						:  Union[str, Any]		  =  self.get_config()

													return config, pixel_values, labels


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													return DeiTConfig(
													    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=snake_case			,    initializer_range=self.initializer_range			,    encoder_stride=self.encoder_stride			,    )


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Tuple			,    snake_case      :						Optional[Any]			,    snake_case      :						str							):
													'''simple docstring'''





													A__						:  Any		  =  TFDeiTModel(config=snake_case							)
													A__						:  Optional[Any]		  =  model(snake_case							)
													self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size)							)


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int			,    snake_case      :						int			,    snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Dict		  =  TFDeiTForMaskedImageModeling(config=snake_case							)
													A__						:  Optional[Any]		  =  model(snake_case							)
													self.parent.assertEqual(
													    result.reconstruction.shape			,    (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

													# test greyscale images
													A__						:  str		  =  1
													A__						:  List[Any]		  =  TFDeiTForMaskedImageModeling(snake_case							)

													A__						:  Dict		  =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
													A__						:  List[Any]		  =  model(snake_case							)
													self.parent.assertEqual(result.reconstruction.shape			,    (self.batch_size, 1, self.image_size, self.image_size)							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Optional[Any]			,    snake_case      :						int			,    snake_case      :						Any							):
													'''simple docstring'''





													A__						:  Any		  =  self.type_sequence_label_size
													A__						:  Any		  =  TFDeiTForImageClassification(snake_case							)
													A__						:  int		  =  model(snake_case			,    labels=snake_case							)
													self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.type_sequence_label_size)							)

													# test greyscale images
													A__						:  Union[str, Any]		  =  1
													A__						:  Tuple		  =  TFDeiTForImageClassification(snake_case							)

													A__						:  Dict		  =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
													A__						:  int		  =  model(snake_case			,    labels=snake_case							)
													self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.type_sequence_label_size)							)


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  List[str]		  =  self.prepare_config_and_inputs()
													A__  ,  A__  ,  A__						:  int		  =  config_and_inputs
													A__						:  Any		  =  {"""pixel_values""": pixel_values}
													return config, inputs_dict






@require_tf
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							(
							    (
							        TFDeiTModel,
							        TFDeiTForImageClassification,
							        TFDeiTForImageClassificationWithTeacher,
							        TFDeiTForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							snake_case_     =							(
							    {
							        'feature-extraction': TFDeiTModel,
							        'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
							    }
							    if is_tf_available()
							    else {}
							)

							snake_case_     =							False
							snake_case_     =							False
							snake_case_     =							False
							snake_case_     =							False


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  TFDeiTModelTester(self							)
													A__						:  Union[str, Any]		  =  ConfigTester(self			,    config_class=snake_case			,    has_text_modality=snake_case			,    hidden_size=37							)


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													self.config_tester.run_common_tests()


							@unittest.skip(reason="""DeiT does not use inputs_embeds"""							)
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													pass


							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





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

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


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





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

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

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


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  int		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*snake_case							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*snake_case							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Dict			,    snake_case      :						List[str]			,    snake_case      :						Union[str, Any]=False							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  super()._prepare_for_class(snake_case			,    snake_case			,    return_labels=snake_case							)

													if return_labels:
																			if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call							).parameters:
																									del inputs_dict["labels"]

													return inputs_dict


							@slow
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			A__						:  str		  =  TFDeiTModel.from_pretrained(snake_case							)
																			self.assertIsNotNone(snake_case							)




def      _lowerCAmelCase		(							)		->Dict:
						A__						:  Optional[Any]		  =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
						return image






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


							@cached_property
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													return (
													    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)
													    if is_vision_available()
													    else None
													)


							@slow
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													A__						:  int		  =  TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)

													A__						:  str		  =  self.default_image_processor
													A__						:  str		  =  prepare_img()
													A__						:  Tuple		  =  image_processor(images=snake_case			,    return_tensors="""tf"""							)

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

													# verify the logits
													A__						:  int		  =  tf.TensorShape((1, 1000)							)
													self.assertEqual(outputs.logits.shape			,    snake_case							)

													A__						:  List[Any]		  =  tf.constant([-1.0266, 0.1912, -1.2861]							)

													self.assertTrue(np.allclose(outputs.logits[0, :3]			,    snake_case			,    atol=1e-4							)							)














"""simple docstring"""



import os

try:
						from .build_directory_md import good_file_paths
except ImportError:
						from build_directory_md import good_file_paths  # type: ignore

A_    =							list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_    =							[file for file in filepaths if file != file.lower()]
if upper_files:
						print(F'{len(upper_files)} files contain uppercase characters:')
						print('''\n'''.join(upper_files) + '''\n''')

A_    =							[file for file in filepaths if ''' ''' in file]
if space_files:
						print(F'{len(space_files)} files contain space characters:')
						print('''\n'''.join(space_files) + '''\n''')

A_    =							[file for file in filepaths if '''-''' in file]
if hyphen_files:
						print(F'{len(hyphen_files)} files contain hyphen characters:')
						print('''\n'''.join(hyphen_files) + '''\n''')

A_    =							[file for file in filepaths if os.sep not in file]
if nodir_files:
						print(F'{len(nodir_files)} files are not in a directory:')
						print('''\n'''.join(nodir_files) + '''\n''')

A_    =							len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
						import sys

						sys.exit(bad_files)






