
from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase  :					Union[str, Any]													=       299792458

# Symbols
lowercase							,			lowercase							,			lowercase							,			lowercase  :					str													=       symbols('ct x y z')




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float)					->    float:






							'''simple docstring'''





							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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float)					->    float:






							'''simple docstring'''





							return 1 / sqrt(1 - beta(_lowerCamelCase) ** 2)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float)					->    np.ndarray:






							'''simple docstring'''





							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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			float						,  _lowerCamelCase :			np.ndarray | None = None)					->    np.ndarray:






							'''simple docstring'''





							if event is None:
														__UpperCamelCase	:   Optional[int]			   =					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:
		lowercase  :					str													=       transform(29979245)
		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
		lowercase  :					List[str]													=       {ct: c, x: 1, y: 1, z: 1}
		lowercase  :					Union[str, Any]													=       [four_vector[i].subs(sub_dict) for i in range(4)]

		print(f"\n{numerical_vector}")

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:Any     ,   a		:List[Any]=1_3     ,   a		:Any=3_0     ,   a		:Dict=2     ,   a		:List[str]=3     ,   a		:Union[str, Any]=True     ,   a		:Optional[int]=True     ,   a		:Tuple=3_2     ,   a		:Any=5     ,   a		:List[str]=4     ,   a		:Optional[Any]=3_7     ,   a		:List[Any]="gelu"     ,   a		:Any=0.1     ,   a		:Any=0.1     ,   a		:List[str]=1_0     ,   a		:str=0.02     ,   )		-> Union[str, Any]:
								__UpperCamelCase	:   int			   =					parent
								__UpperCamelCase	:   List[Any]			   =					batch_size
								__UpperCamelCase	:   Optional[Any]			   =					image_size
								__UpperCamelCase	:   int			   =					patch_size
								__UpperCamelCase	:   Optional[Any]			   =					num_channels
								__UpperCamelCase	:   Optional[int]			   =					is_training
								__UpperCamelCase	:   Union[str, Any]			   =					use_labels
								__UpperCamelCase	:   Tuple			   =					hidden_size
								__UpperCamelCase	:   List[Any]			   =					num_hidden_layers
								__UpperCamelCase	:   List[Any]			   =					num_attention_heads
								__UpperCamelCase	:   Dict			   =					intermediate_size
								__UpperCamelCase	:   Optional[int]			   =					hidden_act
								__UpperCamelCase	:   List[Any]			   =					hidden_dropout_prob
								__UpperCamelCase	:   str			   =					attention_probs_dropout_prob
								__UpperCamelCase	:   List[str]			   =					type_sequence_label_size
								__UpperCamelCase	:   List[str]			   =					initializer_range

								# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Union[str, Any]			   =					(image_size // patch_size) ** 2
								__UpperCamelCase	:   Optional[int]			   =					num_patches + 1







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								__UpperCamelCase	:   Tuple			   =					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=a     ,   initializer_range=self.initializer_range     ,   )

								return config, pixel_values







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[int]     ,   a		:Optional[int]						)		-> Optional[int]:
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTModel(config=a						)
								__UpperCamelCase	:   int			   =					model(a						)
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								__UpperCamelCase	:   Any			   =					(self.image_size, self.image_size)
								__UpperCamelCase	:   Optional[int]			   =					(self.patch_size, self.patch_size)
								__UpperCamelCase	:   Optional[int]			   =					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[str]     ,   a		:Any						)		-> str:
								__UpperCamelCase	:   Dict			   =					self.type_sequence_label_size
								__UpperCamelCase	:   Dict			   =					FlaxViTForImageClassification(config=a						)
								__UpperCamelCase	:   Tuple			   =					model(a						)
								self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)						)

								# test greyscale images
								__UpperCamelCase	:   Dict			   =					1
								__UpperCamelCase	:   Optional[Any]			   =					FlaxViTForImageClassification(a						)

								__UpperCamelCase	:   List[str]			   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
								__UpperCamelCase	:   str			   =					model(a						)







	def    _lowerCamelCase				(					self		:str						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					self.prepare_config_and_inputs()
								(
								    (
								    __UpperCamelCase
								)   ,						(
								    __UpperCamelCase
								)   ,						
								)	:   List[Any]			   =					config_and_inputs
								__UpperCamelCase	:   str			   =					{"pixel_values": pixel_values}
								return config, inputs_dict




@require_flax
class  lowerCamelCase__							(				__lowercase     ,						unittest.TestCase):




	'''simple docstring'''




	_A								=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> None:
								__UpperCamelCase	:   int			   =					FlaxViTModelTester(self						)
								__UpperCamelCase	:   Any			   =					ConfigTester(self     ,   config_class=a     ,   has_text_modality=a     ,   hidden_size=3_7						)







	def    _lowerCamelCase				(					self		:List[Any]						)		-> str:
								self.config_tester.run_common_tests()







	def    _lowerCamelCase				(					self		:List[Any]						)		-> List[str]:
								__UpperCamelCase	:   int			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a						)







	def    _lowerCamelCase				(					self		:List[str]						)		-> Union[str, Any]:
								__UpperCamelCase	:   Any			   =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*a						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   Optional[Any]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class(a						)
															__UpperCamelCase	:   Optional[int]			   =					inspect.signature(model.__call__						)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															__UpperCamelCase	:   Dict			   =					[*signature.parameters.keys()]

															__UpperCamelCase	:   Any			   =					["pixel_values"]
															self.assertListEqual(arg_names[:1]     ,   a						)







	def    _lowerCamelCase				(					self		:str						)		-> Any:
								__UpperCamelCase   ,						__UpperCamelCase	:   List[str]			   =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																						__UpperCamelCase	:   List[Any]			   =					self._prepare_for_class(a     ,   a						)
																						__UpperCamelCase	:   List[Any]			   =					model_class(a						)

																						@jax.jit
																						def model_jitted(a		:List[str]     ,   **a		:List[Any]						):
																													return model(pixel_values=a     ,   **a						)

																						with self.subTest("JIT Enabled"						):
																													__UpperCamelCase	:   int			   =					model_jitted(**a						).to_tuple()

																						with self.subTest("JIT Disabled"						):
																													with jax.disable_jit():
																																				__UpperCamelCase	:   Optional[Any]			   =					model_jitted(**a						).to_tuple()

																						self.assertEqual(len(a						)     ,   len(a						)						)
																						for jitted_output, output in zip(a     ,   a						):
																													self.assertEqual(jitted_output.shape     ,   output.shape						)







	@slow
	def    _lowerCamelCase				(					self		:Dict						)		-> str:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase	:   List[Any]			   =					model_class_name.from_pretrained("google/vit-base-patch16-224"						)
															__UpperCamelCase	:   Optional[int]			   =					model(np.ones((1, 3, 2_2_4, 2_2_4)						)						)
															self.assertIsNotNone(a						)