



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Tuple       						=["""image_processor""", """tokenizer"""]
				UpperCamelCase__			:   Any       						="""Pix2StructImageProcessor"""
				UpperCamelCase__			:   Tuple       						=("""T5Tokenizer""", """T5TokenizerFast""")




				def __init__(  self   :  Tuple,   __lowercase   :  Optional[int],   __lowercase   :  Dict  ):
							lowercase__   					=			False
							super().__init__(__lowercase,   __lowercase  )




				def __call__(  self   :  List[Any],   __lowercase   :  List[Any]=None,   __lowercase   :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,   __lowercase   :  bool = True,   __lowercase   :  Union[bool, str, PaddingStrategy] = False,   __lowercase   :  Union[bool, str, TruncationStrategy] = None,   __lowercase   :  Optional[int] = None,   __lowercase   :  Optional[int] = 2048,   __lowercase   :  int = 0,   __lowercase   :  Optional[int] = None,   __lowercase   :  Optional[bool] = None,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = False,   __lowercase   :  bool = True,   __lowercase   :  Optional[Union[str, TensorType]] = None,   **__lowercase   :  Tuple,   ):
							if images is None and text is None:
										raise ValueError("You have to specify either images or text."  )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
										lowercase__   					=			self.tokenizer
										lowercase__   					=			self.tokenizer(
										    text=__lowercase,   add_special_tokens=__lowercase,   padding=__lowercase,   truncation=__lowercase,   max_length=__lowercase,   stride=__lowercase,   pad_to_multiple_of=__lowercase,   return_attention_mask=__lowercase,   return_overflowing_tokens=__lowercase,   return_special_tokens_mask=__lowercase,   return_offsets_mapping=__lowercase,   return_token_type_ids=__lowercase,   return_length=__lowercase,   verbose=__lowercase,   return_tensors=__lowercase,   **__lowercase,   )
										return text_encoding

							if not self.image_processor.is_vqa:
										# add pixel_values
										lowercase__   					=			self.image_processor(
										    __lowercase,   return_tensors=__lowercase,   max_patches=__lowercase,   **__lowercase  )
							else:
										# add pixel_values and bbox
										lowercase__   					=			self.image_processor(
										    __lowercase,   return_tensors=__lowercase,   max_patches=__lowercase,   header_text=__lowercase,   **__lowercase  )

							if text is not None and not self.image_processor.is_vqa:
										lowercase__   					=			self.tokenizer(
										    text=__lowercase,   add_special_tokens=__lowercase,   padding=__lowercase,   truncation=__lowercase,   max_length=__lowercase,   stride=__lowercase,   pad_to_multiple_of=__lowercase,   return_attention_mask=__lowercase,   return_overflowing_tokens=__lowercase,   return_special_tokens_mask=__lowercase,   return_offsets_mapping=__lowercase,   return_token_type_ids=__lowercase,   return_length=__lowercase,   verbose=__lowercase,   return_tensors=__lowercase,   **__lowercase,   )

										if "attention_mask" in text_encoding:
													lowercase__   					=			text_encoding.pop("attention_mask"  )
										if "input_ids" in text_encoding:
													lowercase__   					=			text_encoding.pop("input_ids"  )
							else:
										lowercase__   					=			None

							if text_encoding is not None:
										encoding_image_processor.update(__lowercase  )

							return encoding_image_processor




				def 							A__ (  self   :  List[str],   *__lowercase   :  Union[str, Any],   **__lowercase   :  Optional[Any]  ):
							return self.tokenizer.batch_decode(*__lowercase,   **__lowercase  )




				def 							A__ (  self   :  Tuple,   *__lowercase   :  List[Any],   **__lowercase   :  int  ):
							return self.tokenizer.decode(*__lowercase,   **__lowercase  )




				@property
				def 							A__ (  self   :  int  ):
							lowercase__   					=			self.tokenizer.model_input_names
							lowercase__   					=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )









from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase_						=       2_9979_2458

# Symbols
lowercase_			,      lowercase_			,      lowercase_			,      lowercase_						=       symbols("""ct x y z""")



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_					) ** 2					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			return np.array(
			    [
			        [gamma(SCREAMING_SNAKE_CASE_					), -gamma(SCREAMING_SNAKE_CASE_					) * beta(SCREAMING_SNAKE_CASE_					), 0, 0],
			        [-gamma(SCREAMING_SNAKE_CASE_					) * beta(SCREAMING_SNAKE_CASE_					), gamma(SCREAMING_SNAKE_CASE_					), 0, 0],
			        [0, 0, 1, 0],
			        [0, 0, 0, 1],
			    ]					)



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

			return transformation_matrix(SCREAMING_SNAKE_CASE_					) @ event


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Example of symbolic vector:
							lowercase_						=       transform(2997_9245)
							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_						=       {ct: c, x: 1, y: 1, z: 1}
							lowercase_						=       [four_vector[i].subs(sub_dict) for i in range(4)]

							print(F'\n{numerical_vector}')




