
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  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=1_3       ,							SCREAMING_SNAKE_CASE_  :			int=3_0       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=3       ,							SCREAMING_SNAKE_CASE_  :			Tuple=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			str=3_2       ,							SCREAMING_SNAKE_CASE_  :			Dict=2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=4       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_7       ,							SCREAMING_SNAKE_CASE_  :			Any="gelu"       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Any=0.1       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=1_0       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Dict=3       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=None       ,							SCREAMING_SNAKE_CASE_  :			int=2       ,							)       ->     Any:
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					image_size
					lowercase_       =					patch_size
					lowercase_       =					num_channels
					lowercase_       =					is_training
					lowercase_       =					use_labels
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					type_sequence_label_size
					lowercase_       =					initializer_range
					lowercase_       =					scope
					lowercase_       =					encoder_stride

					# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
					lowercase_       =					(image_size // patch_size) ** 2
					lowercase_       =					num_patches + 2
	def 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

					lowercase_       =					None
					if self.use_labels:
									lowercase_       =					ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )

					lowercase_       =					self.get_config()

					return config, pixel_values, labels
	def 						_lowercase  (       self  :			Optional[int]       )       ->     str:
					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=SCREAMING_SNAKE_CASE_       ,							initializer_range=self.initializer_range       ,							encoder_stride=self.encoder_stride       ,							)
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]       )       ->     Optional[Any]:
					lowercase_       =					TFDeiTModel(config=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       )
					self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)       )
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[Any]:
					lowercase_       =					TFDeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       )
					self.parent.assertEqual(
					    result.reconstruction.shape       ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

					# test greyscale images
					lowercase_       =					1
					lowercase_       =					TFDeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       )
					self.parent.assertEqual(result.reconstruction.shape       ,							(self.batch_size, 1, self.image_size, self.image_size)       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					lowercase_       =					self.type_sequence_label_size
					lowercase_       =					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_       )
					self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)       )

					# test greyscale images
					lowercase_       =					1
					lowercase_       =					TFDeiTForImageClassification(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       ,							labels=SCREAMING_SNAKE_CASE_       )
					self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)       )




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Dict:
					lowercase_       =					self.prepare_config_and_inputs()
					lowercase_  ,	lowercase_  ,	lowercase_       =					config_and_inputs
					lowercase_       =					{'''pixel_values''': pixel_values}
					return config, inputs_dict

@require_tf
class  lowercase__(				UpperCAmelCase ,						UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:str         =	(
	    (
	        TFDeiTModel,
	        TFDeiTForImageClassification,
	        TFDeiTForImageClassificationWithTeacher,
	        TFDeiTForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	a			:Tuple         =	(
	    {
	        'feature-extraction': TFDeiTModel,
	        'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
	    }
	    if is_tf_available()
	    else {}
	)

	a			:Any         =	False
	a			:Optional[int]         =	False
	a			:Tuple         =	False
	a			:int         =	False
	def 						_lowercase  (       self  :			Any       )       ->     Union[str, Any]:
					lowercase_       =					TFDeiTModelTester(self       )
					lowercase_       =					ConfigTester(self       ,							config_class=SCREAMING_SNAKE_CASE_       ,							has_text_modality=SCREAMING_SNAKE_CASE_       ,							hidden_size=3_7       )
	def 						_lowercase  (       self  :			Dict       )       ->     Tuple:
					self.config_tester.run_common_tests()
	@unittest.skip(reason='''DeiT does not use inputs_embeds'''       )
	def 						_lowercase  (       self  :			List[str]       )       ->     str:
					pass
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Union[str, Any]:
					lowercase_  ,	lowercase_       =					self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowercase_       =					model_class(SCREAMING_SNAKE_CASE_       )
									self.assertIsInstance(model.get_input_embeddings()       ,							(tf.keras.layers.Layer)       )
									lowercase_       =					model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_       ,							tf.keras.layers.Dense       )       )
	def 						_lowercase  (       self  :			Optional[int]       )       ->     List[str]:
					lowercase_  ,	lowercase_       =					self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_       =					['''pixel_values''']
									self.assertListEqual(arg_names[:1]       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     Dict:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       )       ->     List[str]:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     List[str]:
					lowercase_       =					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[str]=False       )       ->     List[str]:
					lowercase_       =					super()._prepare_for_class(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							return_labels=SCREAMING_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 						_lowercase  (       self  :			int       )       ->     int:
					for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_       =					TFDeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
				return image


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



	"""simple docstring"""




	@cached_property
	def 						_lowercase  (       self  :			Any       )       ->     Optional[int]:
					return (
					    DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''       )
					    if is_vision_available()
					    else None
					)




	@slow
	def 						_lowercase  (       self  :			List[str]       )       ->     Any:
					lowercase_       =					TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''       )

					lowercase_       =					self.default_image_processor
					lowercase_       =					prepare_img()
					lowercase_       =					image_processor(images=SCREAMING_SNAKE_CASE_       ,							return_tensors='''tf'''       )

					# forward pass
					lowercase_       =					model(**SCREAMING_SNAKE_CASE_       )

					# verify the logits
					lowercase_       =					tf.TensorShape((1, 1_0_0_0)       )
					self.assertEqual(outputs.logits.shape       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					tf.constant([-1.02_66, 0.19_12, -1.28_61]       )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a  			=					{
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




