





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


lowercase      :       List[str]									=  logging.get_logger(__name__)

lowercase      :       Union[str, Any]									=  """▁"""

lowercase      :       Any									=  {"""vocab_file""": """sentencepiece.bpe.model"""}

lowercase      :       Dict									=  {
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowercase      :       Optional[Any]									=  {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
lowercase      :       Optional[int]									=  ["""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"""]





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				int         =  VOCAB_FILES_NAMES
   __A   :				Optional[int]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A   :				Dict         =  PRETRAINED_VOCAB_FILES_MAP
   __A   :				int         =  ['''input_ids''', '''attention_mask''']

   __A   :				List[int]         =  []
   __A   :				List[int]         =  []

   def __init__(					self					,  lowercase					,  lowercase="<s>"					,  lowercase="</s>"					,  lowercase="</s>"					,  lowercase="<s>"					,  lowercase="<unk>"					,  lowercase="<pad>"					,  lowercase="<mask>"					,  lowercase=None					,  lowercase=None					,  lowercase=None					,  lowercase = None					,  lowercase=None					,  **lowercase					,  )			->						Any:







          '''simple docstring'''

          a__						:	Union[str, Any] =				AddedToken(lowercase					,  lstrip=lowercase					,  rstrip=lowercase) if isinstance(lowercase					,  lowercase) else mask_token

          a__						:	str =				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=lowercase					,  eos_token=lowercase					,  unk_token=lowercase					,  sep_token=lowercase					,  cls_token=lowercase					,  pad_token=lowercase					,  mask_token=lowercase					,  tokenizer_file=lowercase					,  src_lang=lowercase					,  tgt_lang=lowercase					,  additional_special_tokens=lowercase					,  sp_model_kwargs=self.sp_model_kwargs					,  **lowercase					,  )

          a__						:	str =				spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(lowercase))
          a__						:	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
          a__						:	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
          a__						:	str =				1

          a__						:	Tuple =				len(self.sp_model)
          a__						:	str =				{
              code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase)
          }
          a__						:	int =				{v: k for k, v in self.lang_code_to_id.items()}
          a__						:	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)
          a__						:	List[Any] =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
          a__						:	Any =				list(self.lang_code_to_id.keys())

          if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 self._additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in self._additional_special_tokens])

          a__						:	str =				src_lang if src_lang is not None else 'en_XX'
          a__						:	Dict =				self.lang_code_to_id[self._src_lang]
          a__						:	int =				tgt_lang
          self.set_src_lang_special_tokens(self._src_lang)

   def __getstate__(					self)			->						Any:







          '''simple docstring'''

          a__						:	Tuple =				self.__dict__.copy()
          a__						:	Tuple =				None
          a__						:	List[Any] =				self.sp_model.serialized_model_proto()
          return state

   def __setstate__(					self					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Tuple =				d

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

          a__						:	List[str] =				spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  # Plus 1 for the mask token

   @property
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          return self._src_lang

   @src_lang.setter
   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	Union[str, Any] =				new_src_lang
          self.set_src_lang_special_tokens(self._src_lang)

   def 							__lowercase		(					self					,  lowercase					,  lowercase = None					,  lowercase = False)			->						List[int]:







          '''simple docstring'''

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

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase = None)			->						List[int]:







          '''simple docstring'''

          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 							__lowercase		(					self					,  lowercase					,  lowercase = None)			->						List[int]:







          '''simple docstring'''

          a__						:	Optional[Any] =				[self.sep_token_id]
          a__						:	Any =				[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]

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  **lowercase)			->						int:







          '''simple docstring'''

          if src_lang is None or tgt_lang is None:
                 raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
          a__						:	Union[str, Any] =				src_lang
          a__						:	Any =				self(lowercase					,  add_special_tokens=lowercase					,  return_tensors=lowercase					,  **lowercase)
          a__						:	Tuple =				self.convert_tokens_to_ids(lowercase)
          a__						:	Any =				tgt_lang_id
          return inputs

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Tuple =				{self.convert_ids_to_tokens(lowercase): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab

   def 							__lowercase		(					self					,  lowercase)			->						List[str]:







          '''simple docstring'''

          return self.sp_model.encode(lowercase					,  out_type=lowercase)

   def 							__lowercase		(					self					,  lowercase)			->						Any:







          '''simple docstring'''

          if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
          a__						:	Dict =				self.sp_model.PieceToId(lowercase)

          # 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 							__lowercase		(					self					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          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 							__lowercase		(					self					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	Tuple =				''.join(lowercase).replace(lowercase					,  ' ').strip()
          return out_string

   def 							__lowercase		(					self					,  lowercase					,  lowercase = None)			->						Tuple[str]:







          '''simple docstring'''

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

          if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase) and os.path.isfile(self.vocab_file):
                 copyfile(self.vocab_file					,  lowercase)
          elif not os.path.isfile(self.vocab_file):
                 with open(lowercase					,  'wb') as fi:
                        a__						:	Optional[Any] =				self.sp_model.serialized_model_proto()
                        fi.write(lowercase)

          return (out_vocab_file,)

   def 							__lowercase		(					self					,  lowercase					,  lowercase = "en_XX"					,  lowercase = None					,  lowercase = "ro_RO"					,  **lowercase					,  )			->						BatchEncoding:







          '''simple docstring'''

          a__						:	List[str] =				src_lang
          a__						:	Any =				tgt_lang
          return super().prepare_seqaseq_batch(lowercase					,  lowercase					,  **lowercase)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          return self.set_src_lang_special_tokens(self.src_lang)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return self.set_tgt_lang_special_tokens(self.tgt_lang)

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.lang_code_to_id[src_lang]
          a__						:	int =				[]
          a__						:	Optional[Any] =				[self.eos_token_id, self.cur_lang_code]

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[Any] =				self.lang_code_to_id[lang]
          a__						:	List[str] =				[]
          a__						:	Tuple =				[self.eos_token_id, self.cur_lang_code]









from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def A_	(		A__				)  ->		Tuple:

       # A local function to see if a dot lands in the circle.
       def is_in_circle(A__		,      A__				) -> bool:
              a__						:	List[str] =				sqrt((x**2) + (y**2)				)
              # Our circle has a radius of 1, so a distance
              # greater than 1 would land outside the circle.
              return distance_from_centre <= 1

       # The proportion of guesses that landed in the circle
       a__						:	List[str] =				mean(
           int(is_in_circle(uniform(-1.0		,      1.0				)		,      uniform(-1.0		,      1.0				)				)				)
           for _ in range(A__				)				)
       # The ratio of the area for circle to square is pi/4.
       a__						:	Optional[Any] =				proportion * 4
       print(F'The estimated value of pi is {pi_estimate}'				)
       print(F'The numpy value of pi is {pi}'				)
       print(F'The total error is {abs(pi - pi_estimate				)}'				)



def A_	(		A__		,      A__		,      A__ = 0.0		,      A__ = 1.0		,      )  ->		float:
       return mean(
           function_to_integrate(uniform(A__		,      A__				)				) for _ in range(A__				)				) * (max_value - min_value)



def A_	(		A__		,      A__ = 0.0		,      A__ = 1.0				)  ->		None:

       def identity_function(A__				) -> float:
              return x

       a__						:	List[Any] =				area_under_curve_estimator(
           A__		,      A__		,      A__		,      A__				)
       a__						:	Union[str, Any] =				(max_value * max_value - min_value * min_value) / 2

       print('******************'				)
       print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {expected_value}'				)
       print(F'Total error is {abs(estimated_value - expected_value				)}'				)
       print('******************'				)



def A_	(		A__				)  ->		None:

       def function_to_integrate(A__				) -> float:
              return sqrt(4.0 - x * x				)

       a__						:	Dict =				area_under_curve_estimator(
           A__		,      A__		,      0.0		,      2.0				)

       print('******************'				)
       print('Estimating pi using area_under_curve_estimator'				)
       print(F'Estimated value is {estimated_value}'				)
       print(F'Expected value is {pi}'				)
       print(F'Total error is {abs(estimated_value - pi				)}'				)
       print('******************'				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()


