




import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor



class    lowercase_		:



						def __init__(			self	:   Union[str, Any]					,							snake_case__	:   int					,							snake_case__	:   Optional[int]=13					,							snake_case__	:   Union[str, Any]=30					,							snake_case__	:   Union[str, Any]=2					,							snake_case__	:   int=3					,							snake_case__	:   List[str]=True					,							snake_case__	:   Union[str, Any]=True					,							snake_case__	:   Optional[int]=32					,							snake_case__	:   Optional[Any]=5					,							snake_case__	:   str=4					,							snake_case__	:   str=37					,							snake_case__	:   Optional[Any]="gelu"					,							snake_case__	:   Union[str, Any]=0.1					,							snake_case__	:   Optional[Any]=0.1					,							snake_case__	:   Optional[Any]=10					,							snake_case__	:   Optional[int]=0.02					,							snake_case__	:   Optional[Any]=None					,							snake_case__	:   Union[str, Any]=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 ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								SCREAMING_SNAKE_CASE_					      =							(image_size // patch_size) ** 2
								SCREAMING_SNAKE_CASE_					      =							num_patches + 1



						def 	__a					(			self	:   Optional[int]  ):

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



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ViTModel(config=snake_case__  )
								model.to(snake_case__  )
								model.eval()
								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	:   Any					,							snake_case__	:   Dict					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ViTForMaskedImageModeling(config=snake_case__  )
								model.to(snake_case__  )
								model.eval()
								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_					      =							ViTForMaskedImageModeling(snake_case__  )
								model.to(snake_case__  )
								model.eval()

								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	:   Any					,							snake_case__	:   Optional[int]					,							snake_case__	:   List[str]					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.type_sequence_label_size
								SCREAMING_SNAKE_CASE_					      =							ViTForImageClassification(snake_case__  )
								model.to(snake_case__  )
								model.eval()
								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_					      =							ViTForImageClassification(snake_case__  )
								model.to(snake_case__  )
								model.eval()

								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.logits.shape					,							(self.batch_size, self.type_sequence_label_size)  )






						def 	__a					(			self	:   Any  ):

								"""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_torch
class    lowercase_		(SCREAMING_SNAKE_CASE__							,	SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =(
						    (
						        ViTModel,
						        ViTForImageClassification,
						        ViTForMaskedImageModeling,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase__			      =(
						    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
						    if is_torch_available()
						    else {}
						)
						lowerCAmelCase__			      =True

						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False
						lowerCAmelCase__			      =False



						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								self.config_tester.run_common_tests()



						@unittest.skip(reason='ViT does not use inputs_embeds'  )
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								pass



						def 	__a					(			self	:   str  ):

								"""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()					,							(nn.Module)  )
										SCREAMING_SNAKE_CASE_					      =							model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(snake_case__					,							nn.Linear  )  )



						def 	__a					(			self	:   Tuple  ):

								"""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.forward  )
										# 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	:   List[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	:   int  ):

								"""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	:   str  ):

								"""simple docstring"""

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






						@slow
						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

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


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




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



						@cached_property
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

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



						@slow
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'  ).to(snake_case__  )

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

								# forward pass
								with torch.no_grad():
										SCREAMING_SNAKE_CASE_					      =							model(**snake_case__  )

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

								SCREAMING_SNAKE_CASE_					      =							torch.tensor([-0.27_44, 0.82_15, -0.08_36]  ).to(snake_case__  )

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



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ViTModel.from_pretrained('facebook/dino-vits8'  ).to(snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							ViTImageProcessor.from_pretrained('facebook/dino-vits8'					,							size=4_80  )
								SCREAMING_SNAKE_CASE_					      =							prepare_img()
								SCREAMING_SNAKE_CASE_					      =							image_processor(images=snake_case__					,							return_tensors='pt'  )
								SCREAMING_SNAKE_CASE_					      =							inputs.pixel_values.to(snake_case__  )

								# forward pass
								with torch.no_grad():
										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							interpolate_pos_encoding=snake_case__  )

								# verify the logits
								SCREAMING_SNAKE_CASE_					      =							torch.Size((1, 36_01, 3_84)  )
								self.assertEqual(outputs.last_hidden_state.shape					,							snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							torch.tensor(
								    [[4.23_40, 4.39_06, -6.66_92], [4.54_63, 1.89_28, -6.72_57], [4.44_29, 0.84_96, -5.85_85]]  ).to(snake_case__  )

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






						@slow
						@require_accelerate
						@require_torch_gpu
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							ViTModel.from_pretrained('facebook/dino-vits8'					,							torch_dtype=torch.floataa					,							device_map='auto'  )
								SCREAMING_SNAKE_CASE_					      =							self.default_image_processor

								SCREAMING_SNAKE_CASE_					      =							prepare_img()
								SCREAMING_SNAKE_CASE_					      =							image_processor(images=snake_case__					,							return_tensors='pt'  )
								SCREAMING_SNAKE_CASE_					      =							inputs.pixel_values.to(snake_case__  )

								# forward pass to make sure inference works in fp16
								with torch.no_grad():
										SCREAMING_SNAKE_CASE_					      =							model(snake_case__  )





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}""")