



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	UpperCAmelCase							 =					None


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase							 =					{
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
UpperCAmelCase							 =					{
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}

class  __snake_case      (   UpperCamelCase_):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Optional[int]         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[Any]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      Tuple         =					["""input_ids""", """attention_mask"""]
							UpperCamelCase__				:      Any         =					TaTokenizer

							UpperCamelCase__				:      Dict         =					[]


							def __init__(							self  ,  a_=None  ,  a_=None  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_=100  ,  a_=None  ,  **a_  ,  ):





											if extra_ids > 0 and additional_special_tokens is None:
															a__				      =		[F'''<extra_id_{i}>''' for i in range(UpperCamelCase__		)]
											elif extra_ids > 0 and additional_special_tokens is not None:
															# Check that we have the right number of extra special tokens
															a__				      =		len(set(filter(lambda a_		: bool("""extra_id_""" in str(UpperCamelCase__		)		)  ,  UpperCamelCase__		)		)		)
															if extra_tokens != extra_ids:
																			raise ValueError(
																			    F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																			    """ provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
																			    """ tokens"""		)

											super().__init__(
											    UpperCamelCase__  ,  tokenizer_file=UpperCamelCase__  ,  eos_token=UpperCamelCase__  ,  unk_token=UpperCamelCase__  ,  pad_token=UpperCamelCase__  ,  extra_ids=UpperCamelCase__  ,  additional_special_tokens=UpperCamelCase__  ,  **UpperCamelCase__  ,  )

											a__				      =		vocab_file
											a__				      =		False if not self.vocab_file else True
											a__				      =		extra_ids


							@staticmethod
							def _a      (							a_  ,  a_  ,  a_		):





											if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
															a__				      =		TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
															if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
															elif init_max_model_length is None:
																			warnings.warn(
																			    """This tokenizer was incorrectly instantiated with a model max length of"""
																			    F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
																			    """ behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
																			    """ `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
																			    F''' {pretrained_model_name_or_path} automatically truncating your input to'''
																			    F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
																			    F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
																			    """ `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
																			    """ instantiate this tokenizer with `model_max_length` set to your preferred value."""  ,  UpperCamelCase__  ,  )

											return max_model_length


							def _a      (							self  ,  a_  ,  a_ = None		):





											if not self.can_save_slow_tokenizer:
															raise ValueError(
															    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
															    """tokenizer."""		)

											if not os.path.isdir(UpperCamelCase__		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    UpperCamelCase__  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		):
															copyfile(self.vocab_file  ,  UpperCamelCase__		)
															logger.info(F'''Copy vocab file to {out_vocab_file}'''		)

											return (out_vocab_file,)


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
															return self.prefix_tokens + token_ids_a
											else:
															a__				      =		token_ids_a + [self.eos_token_id]
															return self.prefix_tokens + token_ids_a + token_ids_a


							def _a      (							self  ,  a_  ,  a_ = None		):
											a__				      =		[self.eos_token_id]

											if token_ids_a is None:
															return len(token_ids_a + eos		) * [0]
											return len(token_ids_a + eos + token_ids_a + eos		) * [0]


							def _a      (							self		):
											return list(
											    set(filter(lambda a_		: bool(re.search(r"""<extra_id_\d+>"""  ,  UpperCamelCase__		)		) is not None  ,  self.additional_special_tokens		)		)		)



							def _a      (							self		):
											return [self.convert_tokens_to_ids(UpperCamelCase__		) for token in self.get_sentinel_tokens()]






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

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

def 			A_      (	__a     :				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 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

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

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	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
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

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

