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


UpperCamelCase__											=					logging.get_logger(__name__)

UpperCamelCase__											=					'▁'

UpperCamelCase__											=					{'vocab_file': 'sentencepiece.bpe.model'}

UpperCamelCase__											=					{
    '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'
        ),
    }
}

UpperCamelCase__											=					{
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
UpperCamelCase__											=					['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_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : List[Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Optional[int]      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : str      =			['input_ids', 'attention_mask']

   __UpperCAmelCase : List[int]      =			[]
   __UpperCAmelCase : List[int]      =			[]


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Tuple="<s>"       ,  __UpperCAmelCase     :						List[Any]="</s>"       ,  __UpperCAmelCase     :						str="</s>"       ,  __UpperCAmelCase     :						str="<s>"       ,  __UpperCAmelCase     :						Any="<unk>"       ,  __UpperCAmelCase     :						Union[str, Any]="<pad>"       ,  __UpperCAmelCase     :						Optional[int]="<mask>"       ,  __UpperCAmelCase     :						List[str]=None       ,  __UpperCAmelCase     :						Dict=None       ,  __UpperCAmelCase     :						List[Any]=None       ,  __UpperCAmelCase     :						Optional[Dict[str, Any]] = None       ,  __UpperCAmelCase     :						Tuple=None       ,  **__UpperCAmelCase     :						str       ,  )						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  AddedToken(__UpperCAmelCase       ,  lstrip=__UpperCAmelCase       ,  rstrip=__UpperCAmelCase		) if isinstance(__UpperCAmelCase       ,  __UpperCAmelCase		) else mask_token

         UpperCAmelCase__								=  {} if sp_model_kwargs is None else sp_model_kwargs

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

         UpperCAmelCase__								=  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(__UpperCAmelCase		)		)
         UpperCAmelCase__								=  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
         UpperCAmelCase__								=  {"<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
         UpperCAmelCase__								=  1

         UpperCAmelCase__								=  len(self.sp_model		)
         UpperCAmelCase__								=  {
             code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__UpperCAmelCase		)
         }
         UpperCAmelCase__								=  {v: k for k, v in self.lang_code_to_id.items()}
         UpperCAmelCase__								=  len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

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

         UpperCAmelCase__								=  src_lang if src_lang is not None else "en_XX"
         UpperCAmelCase__								=  self.lang_code_to_id[self._src_lang]
         UpperCAmelCase__								=  tgt_lang
         self.set_src_lang_special_tokens(self._src_lang		)


   def __getstate__(self     :						int		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.__dict__.copy()
         UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self.sp_model.serialized_model_proto()
         return state


   def __setstate__(self     :						int       ,  __UpperCAmelCase     :						int		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  d

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

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


   @property
   def 			lowercase_      (self     :						int		)						->				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		)						->				str:
         """simple docstring"""



         return self._src_lang


   @src_lang.setter
   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						str		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang		)


   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None       ,  __UpperCAmelCase     :						bool = False		)						->				List[int]:
         """simple docstring"""



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

         UpperCAmelCase__								=  [1] * len(self.prefix_tokens		)
         UpperCAmelCase__								=  [1] * len(self.suffix_tokens		)
         if token_ids_a is None:
               return prefix_ones + ([0] * len(__UpperCAmelCase		)) + suffix_ones
         return prefix_ones + ([0] * len(__UpperCAmelCase		)) + ([0] * len(__UpperCAmelCase		)) + suffix_ones


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = 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     :						Dict       ,  __UpperCAmelCase     :						List[int]       ,  __UpperCAmelCase     :						Optional[List[int]] = None		)						->				List[int]:
         """simple docstring"""



         UpperCAmelCase__								=  [self.sep_token_id]
         UpperCAmelCase__								=  [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     :						Optional[Any]       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str]       ,  __UpperCAmelCase     :						Optional[str]       ,  **__UpperCAmelCase     :						int		)						->				str:
         """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"		)
         UpperCAmelCase__								=  src_lang
         UpperCAmelCase__								=  self(__UpperCAmelCase       ,  add_special_tokens=__UpperCAmelCase       ,  return_tensors=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  self.convert_tokens_to_ids(__UpperCAmelCase		)
         UpperCAmelCase__								=  tgt_lang_id
         return inputs


   def 			lowercase_      (self     :						Dict		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  {self.convert_ids_to_tokens(__UpperCAmelCase		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						str		)						->				List[str]:
         """simple docstring"""



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


   def 			lowercase_      (self     :						List[str]       ,  __UpperCAmelCase     :						str		)						->				int:
         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase__								=  self.sp_model.PieceToId(__UpperCAmelCase		)

         # 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     :						str       ,  __UpperCAmelCase     :						List[Any]		)						->				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     :						Optional[Any]       ,  __UpperCAmelCase     :						Any		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  "".join(__UpperCAmelCase		).replace(__UpperCAmelCase       ,  " "		).strip()
         return out_string


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  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		) and os.path.isfile(self.vocab_file		):
               copyfile(self.vocab_file       ,  __UpperCAmelCase		)
         elif not os.path.isfile(self.vocab_file		):
               with open(__UpperCAmelCase       ,  "wb"		) as fi:
                     UpperCAmelCase__								=  self.sp_model.serialized_model_proto()
                     fi.write(__UpperCAmelCase		)

         return (out_vocab_file,)


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						str = "en_XX"       ,  __UpperCAmelCase     :						Optional[List[str]] = None       ,  __UpperCAmelCase     :						str = "ro_RO"       ,  **__UpperCAmelCase     :						Union[str, Any]       ,  )						->				BatchEncoding:
         """simple docstring"""



         UpperCAmelCase__								=  src_lang
         UpperCAmelCase__								=  tgt_lang
         return super().prepare_seqaseq_batch(__UpperCAmelCase       ,  __UpperCAmelCase       ,  **__UpperCAmelCase		)


   def 			lowercase_      (self     :						Any		)						->				List[Any]:
         """simple docstring"""



         return self.set_src_lang_special_tokens(self.src_lang		)


   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         return self.set_tgt_lang_special_tokens(self.tgt_lang		)


   def 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  self.lang_code_to_id[src_lang]
         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  [self.eos_token_id, self.cur_lang_code]





   def 			lowercase_      (self     :						int       ,  __UpperCAmelCase     :						str		)						->				None:
         """simple docstring"""



         UpperCAmelCase__								=  self.lang_code_to_id[lang]
         UpperCAmelCase__								=  []
         UpperCAmelCase__								=  [self.eos_token_id, self.cur_lang_code]











"""simple docstring"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   ):



  '''simple docstring'''
  _a  :       Optional[Any]             =	[redshift, radiation_density, matter_density, dark_energy]
  if any(p < 0 for p in parameters				):
    raise ValueError("""All input parameters must be positive"""				)

  if any(p > 1 for p in parameters[1:4]				):
    raise ValueError("""Relative densities cannot be greater than one"""				)
  else:
    _a  :       Tuple             =	1 - (matter_density + radiation_density + dark_energy)

    _a  :       int             =	(
        radiation_density * (redshift + 1) ** 4
        + matter_density * (redshift + 1) ** 3
        + curvature * (redshift + 1) ** 2
        + dark_energy
    )

    _a  :       List[str]             =	hubble_constant * e_a ** (1 / 2)
    return hubble


if __name__ == "__main__":
 import doctest

 # run doctest
 doctest.testmod()

 # demo LCDM approximation
 _snake_case          =   0.3

 print(
     hubble_parameter(
         hubble_constant=68.3,
         radiation_density=1e-4,
         matter_density=matter_density,
         dark_energy=1 - matter_density,
         redshift=0,
     )
 )

