




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_		:



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

								"""simple docstring"""

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

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



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

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

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

								SCREAMING_SNAKE_CASE_					      =							self.get_config()

								return config, pixel_values, labels



						def 	__a					(			self	:   List[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 	__a					(			self	:   List[Any]					,							snake_case__	:   str					,							snake_case__	:   int					,							snake_case__	:   Dict  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   Optional[int]					,							snake_case__	:   Tuple					,							snake_case__	:   List[str]  ):

								"""simple docstring"""

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

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

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



						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   int					,							snake_case__	:   Any					,							snake_case__	:   str  ):

								"""simple docstring"""

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

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

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






						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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



@require_tf
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(
						    (
						        TFDeiTModel,
						        TFDeiTForImageClassification,
						        TFDeiTForImageClassificationWithTeacher,
						        TFDeiTForMaskedImageModeling,
						    )
						    if is_tf_available()
						    else ()
						)
						lowerCAmelCase__			      =(
						    {
						        "feature-extraction": TFDeiTModel,
						        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
						    }
						    if is_tf_available()
						    else {}
						)

						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							TFDeiTModelTester(self  )
								SCREAMING_SNAKE_CASE_					      =							ConfigTester(self					,							config_class=snake_case__					,							has_text_modality=snake_case__					,							hidden_size=37  )



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								self.config_tester.run_common_tests()



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

								"""simple docstring"""

								pass



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()

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



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs_for_common()

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

										SCREAMING_SNAKE_CASE_					      =							['pixel_values']
										self.assertListEqual(arg_names[:1]					,							snake_case__  )



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__  )



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__  )



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*snake_case__  )



						def 	__a					(			self	:   str					,							snake_case__	:   str					,							snake_case__	:   Any					,							snake_case__	:   Tuple=False  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							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 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

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


def 					_a							(					)->	int:
		SCREAMING_SNAKE_CASE_					      =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image




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



						@cached_property
						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

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






						@slow
						def 	__a					(			self	:   str  ):

								"""simple docstring"""

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

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

								# forward pass
								SCREAMING_SNAKE_CASE_					      =							model(**snake_case__  )

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

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

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





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


def 					_a							(					lowerCAmelCase )->	float:
		if velocity > c:
				raise ValueError('Speed must not exceed light speed 299,792,458 [m/s]!' )
		elif velocity < 1:
				# Usually the speed should be much higher than 1 (c order of magnitude)
				raise ValueError('Speed must be greater than or equal to 1!' )

		return velocity / c


def 					_a							(					lowerCAmelCase )->	float:
		return 1 / sqrt(1 - beta(lowerCAmelCase ) ** 2 )


def 					_a							(					lowerCAmelCase )->	np.ndarray:
		return np.array(
		    [
		        [gamma(lowerCAmelCase ), -gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), 0, 0],
		        [-gamma(lowerCAmelCase ) * beta(lowerCAmelCase ), gamma(lowerCAmelCase ), 0, 0],
		        [0, 0, 1, 0],
		        [0, 0, 0, 1],
		    ] )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_SNAKE_CASE_					      =							np.array([ct, x, y, z] )  # Symbolic four vector
		else:
				event[0] *= c  # x0 is ct (speed of light * time)

		return transformation_matrix(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						print('''Example of four vector: ''')
						print(f"""ct' = {four_vector[0]}""")
						print(f"""x' = {four_vector[1]}""")
						print(f"""y' = {four_vector[2]}""")
						print(f"""z' = {four_vector[3]}""")

						# Substitute symbols with numerical values
						SCREAMING_SNAKE_CASE:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

						print(f"""\n{numerical_vector}""")