



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       """▁"""

lowercase_						=       {"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase_						=       {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase_						=       {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class 			_snake_case (   lowerCamelCase__):
				UpperCamelCase__			:   Any       						=VOCAB_FILES_NAMES
				UpperCamelCase__			:   Optional[int]       						=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__			:   Tuple       						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__			:   Dict       						=["""input_ids""", """attention_mask"""]




				def __init__(  self   :  str,   __lowercase   :  List[str],   __lowercase   :  Union[str, Any]="<s>",   __lowercase   :  Any="</s>",   __lowercase   :  Tuple="</s>",   __lowercase   :  Tuple="<s>",   __lowercase   :  Optional[int]="<unk>",   __lowercase   :  Tuple="<pad>",   __lowercase   :  List[Any]="<mask>",   __lowercase   :  Union[str, Any] = None,   **__lowercase   :  int,   ):
							lowercase__   					=			AddedToken(snake_case__,   lstrip=snake_case__,   rstrip=snake_case__  ) if isinstance(snake_case__,   snake_case__  ) else mask_token

							lowercase__   					=			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=snake_case__,   eos_token=snake_case__,   unk_token=snake_case__,   sep_token=snake_case__,   cls_token=snake_case__,   pad_token=snake_case__,   mask_token=snake_case__,   sp_model_kwargs=self.sp_model_kwargs,   **snake_case__,   )

							lowercase__   					=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(str(snake_case__  )  )
							lowercase__   					=			vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							lowercase__   					=			{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							lowercase__   					=			1

							lowercase__   					=			len(self.sp_model  ) + self.fairseq_offset
							lowercase__   					=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}




				def __getstate__(  self   :  Any  ):
							lowercase__   					=			self.__dict__.copy()
							lowercase__   					=			None
							lowercase__   					=			self.sp_model.serialized_model_proto()
							return state




				def __setstate__(  self   :  Any,   __lowercase   :  Dict  ):
							lowercase__   					=			d

							# for backward compatibility
							if not hasattr(self,   "sp_model_kwargs"  ):
										lowercase__   					=			{}

							lowercase__   					=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )




				def 							A__ (  self   :  Any,   __lowercase   :  Optional[int],   __lowercase   :  Tuple = None  ):





							if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase__   					=			[self.cls_token_id]
							lowercase__   					=			[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




				def 							A__ (  self   :  int,   __lowercase   :  str,   __lowercase   :  Tuple = None,   __lowercase   :  List[str] = False  ):





							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=snake_case__,   token_ids_a=snake_case__,   already_has_special_tokens=snake_case__  )

							if token_ids_a is None:
										return [1] + ([0] * len(snake_case__  )) + [1]
							return [1] + ([0] * len(snake_case__  )) + [1, 1] + ([0] * len(snake_case__  )) + [1]




				def 							A__ (  self   :  Optional[int],   __lowercase   :  Union[str, Any],   __lowercase   :  int = None  ):
							lowercase__   					=			[self.sep_token_id]
							lowercase__   					=			[self.cls_token_id]

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




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token




				def 							A__ (  self   :  str  ):
							lowercase__   					=			{self.convert_ids_to_tokens(snake_case__  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def 							A__ (  self   :  int,   __lowercase   :  str  ):
							return self.sp_model.encode(snake_case__,   out_type=snake_case__  )




				def 							A__ (  self   :  Tuple,   __lowercase   :  Union[str, Any]  ):





							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowercase__   					=			self.sp_model.PieceToId(snake_case__  )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def 							A__ (  self   :  str,   __lowercase   :  Union[str, Any]  ):





							if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset  )




				def 							A__ (  self   :  List[Any],   __lowercase   :  str  ):
							lowercase__   					=			''''''.join(snake_case__  ).replace(snake_case__,   " "  ).strip()
							return out_string




				def 							A__ (  self   :  Optional[int],   __lowercase   :  Optional[int],   __lowercase   :  List[Any] = None  ):





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

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(snake_case__  ) and os.path.isfile(self.vocab_file  ):
										copyfile(self.vocab_file,   snake_case__  )
							elif not os.path.isfile(self.vocab_file  ):
										with open(snake_case__,   "wb"  ) as fi:
													lowercase__   					=			self.sp_model.serialized_model_proto()
													fi.write(snake_case__  )

							return (out_vocab_file,)









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}')




