

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

import sentencepiece as spm

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


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			"▁"

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

snake_case_        =			{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

snake_case_        =			{
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

# fmt: off
snake_case_        =			["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
				__lowerCamelCase						:	int														=					VOCAB_FILES_NAMES
				__lowerCamelCase						:	List[Any]														=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCamelCase						:	Tuple														=					PRETRAINED_VOCAB_FILES_MAP
				__lowerCamelCase						:	List[str]														=					['''input_ids''', '''attention_mask''']

				__lowerCamelCase						:	List[int]														=					[]
				__lowerCamelCase						:	List[int]														=					[]





				def __init__(				self						,       a						,       a=None						,       a=None						,       a="</s>"						,       a="</s>"						,       a="<s>"						,       a="<unk>"						,       a="<pad>"						,       a="<mask>"						,       a = None						,       **a						,       ):
									lowercase__   :       int				=	AddedToken(_snake_case						,       lstrip=_snake_case						,       rstrip=_snake_case) if isinstance(_snake_case						,       _snake_case) else mask_token

									lowercase__   :       Union[str, Any]				=	{} if sp_model_kwargs is None else sp_model_kwargs

									lowercase__   :       Any				=	kwargs.get('additional_special_tokens'						,       [])
									kwargs["additional_special_tokens"] += [
									    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
									]

									super().__init__(
									    src_lang=_snake_case						,       tgt_lang=_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__   :       str				=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
									self.sp_model.Load(str(_snake_case))
									lowercase__   :       str				=	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__   :       int				=	{'<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__   :       str				=	1

									lowercase__   :       Any				=	len(self.sp_model)
									lowercase__   :       List[str]				=	{
									    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_snake_case)
									}
									lowercase__   :       List[str]				=	{v: k for k, v in self.lang_code_to_id.items()}
									lowercase__   :       List[Any]				=	len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

									self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
									lowercase__   :       Dict				=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}

									lowercase__   :       Tuple				=	src_lang if src_lang is not None else 'en_XX'
									lowercase__   :       str				=	self.lang_code_to_id[self._src_lang]
									lowercase__   :       Optional[Any]				=	tgt_lang
									self.set_src_lang_special_tokens(self._src_lang)





				@property
				def 				snake_case_    (				self):
									return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token





				@property
				def 				snake_case_    (				self):
									return self._src_lang





				@src_lang.setter
				def 				snake_case_    (				self						,       a):
									lowercase__   :       Any				=	new_src_lang
									self.set_src_lang_special_tokens(self._src_lang)





				def __getstate__(				self):
									lowercase__   :       str				=	self.__dict__.copy()
									lowercase__   :       Union[str, Any]				=	None
									return state





				def __setstate__(				self						,       a):
									lowercase__   :       Optional[Any]				=	d

									# for backward compatibility
									if not hasattr(self						,       'sp_model_kwargs'):
														lowercase__   :       Union[str, Any]				=	{}

									lowercase__   :       Dict				=	spm.SentencePieceProcessor(**self.sp_model_kwargs)
									self.sp_model.Load(self.vocab_file)





				def 				snake_case_    (				self):
									lowercase__   :       List[str]				=	{self.convert_ids_to_tokens(_snake_case): i for i in range(self.vocab_size)}
									vocab.update(self.added_tokens_encoder)
									return vocab





				def 				snake_case_    (				self						,       a):
									return self.sp_model.encode(_snake_case						,       out_type=_snake_case)





				def 				snake_case_    (				self						,       a):


									if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
									lowercase__   :       Optional[int]				=	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 				snake_case_    (				self						,       a):


									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 				snake_case_    (				self						,       a):
									lowercase__   :       Optional[int]				=	[]
									lowercase__   :       Tuple				=	''
									lowercase__   :       Optional[int]				=	False
									for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																			if not prev_is_special:
																								out_string += " "
																			out_string += self.sp_model.decode(_snake_case) + token
																			lowercase__   :       Dict				=	True
																			lowercase__   :       Optional[Any]				=	[]
														else:
																			current_sub_tokens.append(_snake_case)
																			lowercase__   :       Optional[int]				=	False
									out_string += self.sp_model.decode(_snake_case)
									return out_string.strip()





				def 				snake_case_    (				self						,       a						,       a = None):


									if not os.path.isdir(_snake_case):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
														return
									lowercase__   :       Union[str, Any]				=	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__   :       Any				=	self.sp_model.serialized_model_proto()
																			fi.write(_snake_case)

									return (out_vocab_file,)





				def 				snake_case_    (				self						,       a						,       a = None						,       a = 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)

									lowercase__   :       List[str]				=	[1] * len(self.prefix_tokens)
									lowercase__   :       int				=	[1] * len(self.suffix_tokens)
									if token_ids_a is None:
														return prefix_ones + ([0] * len(_snake_case)) + suffix_ones
									return prefix_ones + ([0] * len(_snake_case)) + ([0] * len(_snake_case)) + suffix_ones





				def 				snake_case_    (				self						,       a						,       a = None):


									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a + self.suffix_tokens
									# We don't expect to process pairs, but leave the pair logic for API consistency
									return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





				def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       **a):


									if src_lang is None or tgt_lang is None:
														raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
									lowercase__   :       Optional[int]				=	src_lang
									lowercase__   :       str				=	self(_snake_case						,       add_special_tokens=_snake_case						,       return_tensors=_snake_case						,       **_snake_case)
									lowercase__   :       Optional[int]				=	self.convert_tokens_to_ids(_snake_case)
									lowercase__   :       List[Any]				=	tgt_lang_id
									return inputs





				def 				snake_case_    (				self						,       a						,       a = "en_XX"						,       a = None						,       a = "ro_RO"						,       **a						,       ):
									lowercase__   :       Tuple				=	src_lang
									lowercase__   :       List[Any]				=	tgt_lang
									return super().prepare_seqaseq_batch(_snake_case						,       _snake_case						,       **_snake_case)





				def 				snake_case_    (				self):
									return self.set_src_lang_special_tokens(self.src_lang)





				def 				snake_case_    (				self):
									return self.set_tgt_lang_special_tokens(self.tgt_lang)





				def 				snake_case_    (				self						,       a):
									lowercase__   :       int				=	self.lang_code_to_id[src_lang]
									lowercase__   :       Union[str, Any]				=	[self.cur_lang_code_id]
									lowercase__   :       int				=	[self.eos_token_id]





				def 				snake_case_    (				self						,       a):
									lowercase__   :       Tuple				=	self.lang_code_to_id[tgt_lang]
									lowercase__   :       List[Any]				=	[self.cur_lang_code_id]
									lowercase__   :       str				=	[self.eos_token_id]




from __future__ import annotations

from collections.abc import Callable

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Callable[[int | float], int | float]     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int | float     ,					SCREAMING_SNAKE_CASE_		:   int = 100     ,					):



     '''simple docstring'''





     lowercase__   :       Tuple				=	x_start
     lowercase__   :       Tuple				=	fnc(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[Any]				=	0.0
     for _ in range(SCREAMING_SNAKE_CASE_      ):
          # Approximates small segments of curve as linear and solve
          # for trapezoidal area
          lowercase__   :       Any				=	(x_end - x_start) / steps + xa
          lowercase__   :       Optional[Any]				=	fnc(SCREAMING_SNAKE_CASE_      )
          area += abs(fxa + fxa      ) * (xa - xa) / 2
          # Increment step
          lowercase__   :       Any				=	xa
          lowercase__   :       str				=	fxa
     return area


if __name__ == "__main__":

       def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple      ):



            '''simple docstring'''





            return x**3 + x**2

       print('''f(x) = x^3 + x^2''')
       print('''The area between the curve, x = -5, x = 5 and the x axis is:''')
       snake_case_        =			10
       while i <= 100_000:
              print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
              i *= 10

