


import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__  				=			logging.get_logger(__name__)

snake_case__  				=			{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case__  				=			{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

snake_case__  				=			{'Helsinki-NLP/opus-mt-en-de': 512}
snake_case__  				=			{}



class      snake_case__	(   _A	):
				__lowerCAmelCase		=   VOCAB_FILES_NAMES
				__lowerCAmelCase		=   PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase		=   PRETRAINED_INIT_CONFIGURATION
				__lowerCAmelCase		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase		=   ['''input_ids''', '''attention_mask''']
				__lowerCAmelCase		=   re.compile('''>>.+<<'''	)  # type: re.Pattern
				def __init__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase="<unk>"  ,   _UpperCAmelCase="</s>"  ,   _UpperCAmelCase="<pad>"  ,   _UpperCAmelCase=512  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase=False  ,   **_UpperCAmelCase  ,   ):




										"""simple docstring"""




										__magic_name__					:				int				=   {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
										    source_lang=_UpperCAmelCase  ,   target_lang=_UpperCAmelCase  ,   unk_token=_UpperCAmelCase  ,   eos_token=_UpperCAmelCase  ,   pad_token=_UpperCAmelCase  ,   model_max_length=_UpperCAmelCase  ,   sp_model_kwargs=self.sp_model_kwargs  ,   target_vocab_file=_UpperCAmelCase  ,   separate_vocabs=_UpperCAmelCase  ,   **_UpperCAmelCase  ,   )
										assert Path(_UpperCAmelCase   ).exists(), F"cannot find spm source {source_spm}"

										__magic_name__					:				int				=   separate_vocabs
										__magic_name__					:				Optional[int]				=   load_json(_UpperCAmelCase   )
										if self.unk_token not in self.encoder:
																raise KeyError('<unk> token must be in vocab'   )
										assert self.pad_token in self.encoder

										if separate_vocabs:
																__magic_name__					:				Optional[int]				=   load_json(_UpperCAmelCase   )
																__magic_name__					:				Dict				=   {v: k for k, v in self.target_encoder.items()}
																__magic_name__					:				List[Any]				=   []
										else:
																__magic_name__					:				Dict				=   {v: k for k, v in self.encoder.items()}
																__magic_name__					:				list				=   [k for k in self.encoder if k.startswith('>>'   ) and k.endswith('<<'   )]

										__magic_name__					:				Union[str, Any]				=   source_lang
										__magic_name__					:				List[Any]				=   target_lang
										__magic_name__					:				List[Any]				=   [source_spm, target_spm]

										# load SentencePiece model for pre-processing
										__magic_name__					:				Union[str, Any]				=   load_spm(_UpperCAmelCase  ,   self.sp_model_kwargs   )
										__magic_name__					:				int				=   load_spm(_UpperCAmelCase  ,   self.sp_model_kwargs   )
										__magic_name__					:				List[Any]				=   self.spm_source
										__magic_name__					:				int				=   self.encoder

										# Multilingual target side: default to using first supported language code.

										self._setup_normalizer()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										try:
																from sacremoses import MosesPunctNormalizer

																__magic_name__					:				Union[str, Any]				=   MosesPunctNormalizer(self.source_lang   ).normalize
										except (ImportError, FileNotFoundError):
																warnings.warn('Recommended: pip install sacremoses.'   )
																__magic_name__					:				Union[str, Any]				=   lambda _UpperCAmelCase   : x
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										return self.punc_normalizer(_UpperCAmelCase   ) if x else ""
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										return self.current_encoder.get(_UpperCAmelCase  ,   self.current_encoder[self.unk_token]   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				int				=   self.language_code_re.match(_UpperCAmelCase   )
										__magic_name__					:				list				=   [match.group(0   )] if match else []
										return code, self.language_code_re.sub(''  ,   _UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				Optional[int]				=   self.remove_language_code(_UpperCAmelCase   )
										__magic_name__					:				str				=   self.current_spm.encode(_UpperCAmelCase  ,   out_type=_UpperCAmelCase   )
										return code + pieces
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										return self.decoder.get(_UpperCAmelCase  ,   self.unk_token   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										return super().batch_decode(_UpperCAmelCase  ,   **_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										return super().decode(_UpperCAmelCase  ,   **_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				str				=   self.spm_source if self._decode_use_source_tokenizer else self.spm_target
										__magic_name__					:				Union[str, Any]				=   []
										__magic_name__					:				Any				=   ''
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += sp_model.decode_pieces(_UpperCAmelCase   ) + token + " "
																						__magic_name__					:				Optional[int]				=   []
																else:
																						current_sub_tokens.append(_UpperCAmelCase   )
										out_string += sp_model.decode_pieces(_UpperCAmelCase   )
										return out_string.strip()
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=None   ):




										"""simple docstring"""




										if token_ids_a is None:
																return token_ids_a + [self.eos_token_id]
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return token_ids_a + token_ids_a + [self.eos_token_id]
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   self.spm_source
										__magic_name__					:				List[Any]				=   self.encoder
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   self.spm_target
										if self.separate_vocabs:
																__magic_name__					:				Any				=   self.target_encoder
				@property
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return len(self.encoder   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										if not os.path.isdir(_UpperCAmelCase   ):
																logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
																return
										__magic_name__					:				Dict				=   []

										if self.separate_vocabs:
																__magic_name__					:				int				=   os.path.join(
																    _UpperCAmelCase  ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']  ,   )
																__magic_name__					:				Any				=   os.path.join(
																    _UpperCAmelCase  ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']  ,   )
																save_json(self.encoder  ,   _UpperCAmelCase   )
																save_json(self.target_encoder  ,   _UpperCAmelCase   )
																saved_files.append(_UpperCAmelCase   )
																saved_files.append(_UpperCAmelCase   )
										else:
																__magic_name__					:				Dict				=   os.path.join(
																    _UpperCAmelCase  ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']   )
																save_json(self.encoder  ,   _UpperCAmelCase   )
																saved_files.append(_UpperCAmelCase   )

										for spm_save_filename, spm_orig_path, spm_model in zip(
										    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]  ,   self.spm_files  ,   [self.spm_source, self.spm_target]  ,   ):
																__magic_name__					:				Optional[int]				=   os.path.join(
																    _UpperCAmelCase  ,   (filename_prefix + '-' if filename_prefix else '') + spm_save_filename   )
																if os.path.abspath(_UpperCAmelCase   ) != os.path.abspath(_UpperCAmelCase   ) and os.path.isfile(_UpperCAmelCase   ):
																						copyfile(_UpperCAmelCase  ,   _UpperCAmelCase   )
																						saved_files.append(_UpperCAmelCase   )
																elif not os.path.isfile(_UpperCAmelCase   ):
																						with open(_UpperCAmelCase  ,   'wb'   ) as fi:
																												__magic_name__					:				Optional[Any]				=   spm_model.serialized_model_proto()
																												fi.write(_UpperCAmelCase   )
																						saved_files.append(_UpperCAmelCase   )

										return tuple(_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return self.get_src_vocab()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return dict(self.encoder  ,   **self.added_tokens_encoder   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										return dict(self.target_encoder  ,   **self.added_tokens_decoder   )
				def __getstate__(	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.__dict__.copy()
										state.update(
										    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}   )
										return state
				def __setstate__(	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   d

										# for backward compatibility
										if not hasattr(self  ,   'sp_model_kwargs'   ):
																__magic_name__					:				str				=   {}

										__magic_name__		,			__magic_name__					:				Any				=   (load_spm(_UpperCAmelCase  ,   self.sp_model_kwargs   ) for f in self.spm_files)
										__magic_name__					:				Any				=   self.spm_source
										self._setup_normalizer()
				def 			UpperCamelCase_    (	self  ,   *_UpperCAmelCase  ,   **_UpperCAmelCase   ):




										"""simple docstring"""




										return 1
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				str				=   set(self.all_special_ids   )  # call it once instead of inside list comp
										all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special
										return [1 if x in all_special_ids else 0 for x in seq]





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase = None  ,   _UpperCAmelCase = False   ):




										"""simple docstring"""




										if already_has_special_tokens:
																return self._special_token_mask(_UpperCAmelCase   )
										elif token_ids_a is None:
																return self._special_token_mask(_UpperCAmelCase   ) + [1]
										else:
																return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]






def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str   ,    lowercase__	:Dict[str, Any]					)							->	sentencepiece.SentencePieceProcessor:
						__magic_name__					:				Any				=   sentencepiece.SentencePieceProcessor(**lowercase__					)
						spm.Load(lowercase__					)
						return spm
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[Any]   ,    lowercase__	:str					)							->	None:
						with open(lowercase__   ,    'w'					) as f:
												json.dump(lowercase__   ,    lowercase__   ,    indent=2					)

def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str					)							->	Union[Dict, List]:
						with open(lowercase__   ,    'r'					) as f:
												return json.load(lowercase__					)






from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


