
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  :






			"""simple docstring"""




			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=13 ,				__UpperCamelCase=30 ,				__UpperCamelCase=2 ,				__UpperCamelCase=3 ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=32 ,				__UpperCamelCase=2 ,				__UpperCamelCase=4 ,				__UpperCamelCase=37 ,				__UpperCamelCase="gelu" ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=0.1 ,				__UpperCamelCase=10 ,				__UpperCamelCase=0.02 ,				__UpperCamelCase=3 ,				__UpperCamelCase=None ,				__UpperCamelCase=2 ,				):

				"""simple docstring"""


				snake_case_      		= parent
				snake_case_      		= batch_size
				snake_case_      		= image_size
				snake_case_      		= patch_size
				snake_case_      		= num_channels
				snake_case_      		= is_training
				snake_case_      		= use_labels
				snake_case_      		= hidden_size
				snake_case_      		= num_hidden_layers
				snake_case_      		= num_attention_heads
				snake_case_      		= intermediate_size
				snake_case_      		= hidden_act
				snake_case_      		= hidden_dropout_prob
				snake_case_      		= attention_probs_dropout_prob
				snake_case_      		= type_sequence_label_size
				snake_case_      		= initializer_range
				snake_case_      		= scope
				snake_case_      		= encoder_stride

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

				snake_case_      		= self.get_config()

				return config, pixel_values, labels


			def 			__lowerCAmelCase (    self							):

				"""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=__UpperCamelCase ,				initializer_range=self.initializer_range ,				encoder_stride=self.encoder_stride ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= TFDeiTModel(config=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= TFDeiTForMaskedImageModeling(config=__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(
				    result.reconstruction.shape ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= TFDeiTForMaskedImageModeling(__UpperCamelCase							)

				snake_case_      		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
				snake_case_      		= model(__UpperCamelCase							)
				self.parent.assertEqual(result.reconstruction.shape ,				(self.batch_size, 1, self.image_size, self.image_size)							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.type_sequence_label_size
				snake_case_      		= TFDeiTForImageClassification(__UpperCamelCase							)
				snake_case_      		= model(__UpperCamelCase ,				labels=__UpperCamelCase							)
				self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)							)

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= TFDeiTForImageClassification(__UpperCamelCase							)

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







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				snake_case_		,       snake_case_		,       snake_case_      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







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






			"""simple docstring"""




			__A						  =  (
			    (
			        TFDeiTModel,
			        TFDeiTForImageClassification,
			        TFDeiTForImageClassificationWithTeacher,
			        TFDeiTForMaskedImageModeling,
			    )
			    if is_tf_available()
			    else ()
			)
			__A						  =  (
			    {
			        """feature-extraction""": TFDeiTModel,
			        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
			    }
			    if is_tf_available()
			    else {}
			)

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFDeiTModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase ,				hidden_size=37							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				self.config_tester.run_common_tests()


			@unittest.skip(reason='DeiT does not use inputs_embeds'							)
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				pass


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase=False							):

				"""simple docstring"""


				snake_case_      		= super()._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase ,				return_labels=__UpperCamelCase							)

				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 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					snake_case_      		= TFDeiTModel.from_pretrained(__UpperCamelCase							)
					self.assertIsNotNone(__UpperCamelCase							)







def a():





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






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






			"""simple docstring"""




			@cached_property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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







			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= image_processor(images=__UpperCamelCase ,				return_tensors='tf'							)

				# forward pass
				snake_case_      		= model(**__UpperCamelCase							)

				# verify the logits
				snake_case_      		= tf.TensorShape((1, 10_00)							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= tf.constant([-1.0266, 0.1912, -1.2861]							)

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



from ..utils import DummyObject, requires_backends


class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







class SCREAMING_SNAKE_CASE  (		metaclass=__snake_case							):






			"""simple docstring"""




			__A						  =  ["""torch""", """transformers""", """onnx"""]


			def __init__(    self ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				['torch', 'transformers', 'onnx']							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)







			@classmethod
			def 			__lowerCAmelCase (    cls ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(cls ,				['torch', 'transformers', 'onnx']							)
