

'''simple docstring'''




import os
from typing import List, Optional, Union

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


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			Optional[Any]							=    {'vocab_file': 'vocab.txt'}

_a					:			Dict							=    {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

_a					:			List[Any]							=    {
    'facebook/esm2_t6_8M_UR50D': 1_024,
    'facebook/esm2_t12_35M_UR50D': 1_024,
}


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Any    ):

  with open(__UpperCAmelCase							, 'r'    ) as f:
    UpperCAmelCase    				=						f.read().splitlines()
    return [l.strip() for l in lines]



class 							lowercase_						(		_lowerCamelCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			Any     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Tuple     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			Optional[int]     	= ["input_ids", "attention_mask"]
  def __init__(  self ,			a_ ,			a_="<unk>" ,			a_="<cls>" ,			a_="<pad>" ,			a_="<mask>" ,			a_="<eos>" ,			**a_ ,			)				->					Optional[int]:


    """simple docstring"""
    super().__init__(**lowercase_  )
    UpperCAmelCase    				=						load_vocab_file(lowercase_  )
    UpperCAmelCase    				=						dict(enumerate(self.all_tokens  )  )
    UpperCAmelCase    				=						{tok: ind for ind, tok in enumerate(self.all_tokens  )}
    UpperCAmelCase    				=						unk_token
    UpperCAmelCase    				=						cls_token
    UpperCAmelCase    				=						pad_token
    UpperCAmelCase    				=						mask_token
    UpperCAmelCase    				=						eos_token
    UpperCAmelCase    				=						self.all_tokens
    self._create_trie(self.unique_no_split_tokens  )
  def     snake_case_      (  self ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    return self._id_to_token.get(lowercase_ ,			self.unk_token  )
  def     snake_case_      (  self ,			a_  )				->					str:


    """simple docstring"""
    return self._token_to_id.get(lowercase_ ,			self._token_to_id.get(self.unk_token  )  )
  def     snake_case_      (  self ,			a_ ,			**a_  )				->					Any:


    """simple docstring"""
    return text.split()
  def     snake_case_      (  self ,			a_=False  )				->					Optional[int]:


    """simple docstring"""
    return len(self._id_to_token  )
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    return {token: i for i, token in enumerate(self.all_tokens  )}
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self._token_to_id.get(lowercase_ ,			self._token_to_id.get(self.unk_token  )  )
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self._id_to_token.get(lowercase_ ,			self.unk_token  )
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.cls_token_id]
    UpperCAmelCase    				=						[self.eos_token_id]  # No sep token in ESM vocabulary
    if token_ids_a is None:
      if self.eos_token_id is None:
        return cls + token_ids_a
      else:
        return cls + token_ids_a + sep
    elif self.eos_token_id is None:
      raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!'  )
    return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = False  )				->					int:


    """simple docstring"""
    if already_has_special_tokens:
      if token_ids_a is not None:
        raise ValueError(
            'You should not supply a second sequence if the provided sequence of '
            'ids is already formatted with special tokens for the model.'  )

      return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
    UpperCAmelCase    				=						[1] + ([0] * len(lowercase_  )) + [1]
    if token_ids_a is not None:
      mask += [0] * len(lowercase_  ) + [1]
    return mask
  def     snake_case_      (  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						os.path.join(lowercase_ ,			(filename_prefix + '-' if filename_prefix else '') + 'vocab.txt'  )
    with open(lowercase_ ,			'w'  ) as f:
      f.write('\n'.join(self.all_tokens  )  )
    return (vocab_file,)
  @property
  def     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    return self.get_vocab_size(with_added_tokens=lowercase_  )





  def     snake_case_      (  self ,			a_ ,			a_ = False  )				->					Any:


    """simple docstring"""
    return super()._add_tokens(lowercase_ ,			special_tokens=lowercase_  )








'''simple docstring'''

import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Optional[int]			,							lowercase_       :   NestedDataStructureLike[PathLike]			,							lowercase_       :   Optional[NamedSplit] = None			,							lowercase_       :   Optional[Features] = None			,							lowercase_       :   str = None			,							lowercase_       :   bool = False			,							lowercase_       :   bool = False			,							lowercase_       :   Optional[str] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   Optional[Any]			,							):
									super().__init__(
									    lowercase_			,							split=lowercase_			,							features=lowercase_			,							cache_dir=lowercase_			,							keep_in_memory=lowercase_			,							streaming=lowercase_			,							num_proc=lowercase_			,							**lowercase_			,							)
									snake_case_		     =							field
									snake_case_		     =							path_or_paths if isinstance(lowercase_			,							lowercase_   ) else {self.split: path_or_paths}
									snake_case_		     =							Json(
									    cache_dir=lowercase_			,							data_files=lowercase_			,							features=lowercase_			,							field=lowercase_			,							**lowercase_			,							)


				def     A_					(						self       :   Optional[Any]   ):
									# Build iterable dataset
									if self.streaming:
														snake_case_		     =							self.builder.as_streaming_dataset(split=self.split   )
									# Build regular (map-style) dataset
									else:
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None
														snake_case_		     =							None

														self.builder.download_and_prepare(
														    download_config=lowercase_			,							download_mode=lowercase_			,							verification_mode=lowercase_			,							base_path=lowercase_			,							num_proc=self.num_proc			,							)
														snake_case_		     =							self.builder.as_dataset(
														    split=self.split			,							verification_mode=lowercase_			,							in_memory=self.keep_in_memory   )
									return dataset







class 		a							:


				def __init__(						self       :   Dict			,							lowercase_       :   Dataset			,							lowercase_       :   Union[PathLike, BinaryIO]			,							lowercase_       :   Optional[int] = None			,							lowercase_       :   Optional[int] = None			,							**lowercase_       :   List[str]			,							):
									if num_proc is not None and num_proc <= 0:
														raise ValueError(F"num_proc {num_proc} must be an integer > 0."   )

									snake_case_		     =							dataset
									snake_case_		     =							path_or_buf
									snake_case_		     =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									snake_case_		     =							num_proc
									snake_case_		     =							'''utf-8'''
									snake_case_		     =							to_json_kwargs


				def     A_					(						self       :   str   ):
									snake_case_		     =							self.to_json_kwargs.pop('''path_or_buf'''			,							lowercase_   )
									snake_case_		     =							self.to_json_kwargs.pop('''orient'''			,							'''records'''   )
									snake_case_		     =							self.to_json_kwargs.pop('''lines'''			,							True if orient == '''records''' else False   )
									snake_case_		     =							self.to_json_kwargs.pop('''index'''			,							False if orient in ['''split''', '''table'''] else True   )
									snake_case_		     =							self.to_json_kwargs.pop('''compression'''			,							lowercase_   )

									if compression not in [None, "infer", "gzip", "bz2", "xz"]:
														raise NotImplementedError(F"`datasets` currently does not support {compression} compression"   )

									if isinstance(self.path_or_buf			,							(str, bytes, os.PathLike)   ):
														with fsspec.open(self.path_or_buf			,							'''wb'''			,							compression=lowercase_   ) as buffer:
																			snake_case_		     =							self._write(file_obj=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									else:
														if compression:
																			raise NotImplementedError(
																			    F"The compression parameter is not supported when writing to a buffer, but compression={compression}"
																			    ''' was passed. Please provide a local path instead.'''   )
														snake_case_		     =							self._write(
														    file_obj=self.path_or_buf			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**self.to_json_kwargs   )
									return written


				def     A_					(						self       :   List[str]			,							lowercase_       :   Dict   ):
									snake_case_     ,snake_case_     ,snake_case_     ,snake_case_     ,snake_case_		     =							args

									snake_case_		     =							query_table(
									    table=self.dataset.data			,							key=slice(lowercase_			,							offset + self.batch_size   )			,							indices=self.dataset._indices			,							)
									snake_case_		     =							batch.to_pandas().to_json(
									    path_or_buf=lowercase_			,							orient=lowercase_			,							lines=lowercase_			,							index=lowercase_			,							**lowercase_   )
									if not json_str.endswith('''\n'''   ):
														json_str += "\n"
									return json_str.encode(self.encoding   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   BinaryIO			,							lowercase_       :   List[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							**lowercase_       :   Optional[int]			,							):
									snake_case_		     =							0

									if self.num_proc is None or self.num_proc == 1:
														for offset in logging.tqdm(
														    range(0			,							len(self.dataset   )			,							self.batch_size   )			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																			snake_case_		     =							self._batch_json((offset, orient, lines, index, to_json_kwargs)   )
																			written += file_obj.write(lowercase_   )
									else:
														snake_case_     ,snake_case_		     =							len(self.dataset   ), self.batch_size
														with multiprocessing.Pool(self.num_proc   ) as pool:
																			for json_str in logging.tqdm(
																			    pool.imap(
																			        self._batch_json			,							[(offset, orient, lines, index, to_json_kwargs) for offset in range(0			,							lowercase_			,							lowercase_   )]			,							)			,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size			,							unit='''ba'''			,							disable=not logging.is_progress_bar_enabled()			,							desc='''Creating json from Arrow format'''			,							):
																								written += file_obj.write(lowercase_   )

									return written

