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       UpperCAmelCase__     (		SCREAMING_SNAKE_CASE_       ):



  """simple docstring"""






  def __init__(					self:      List[str]					,					__lowerCAmelCase:      NestedDataStructureLike[PathLike]					,					__lowerCAmelCase:      Optional[NamedSplit] = None					,					__lowerCAmelCase:      Optional[Features] = None					,					__lowerCAmelCase:      str = None					,					__lowerCAmelCase:      bool = False					,					__lowerCAmelCase:      bool = False					,					__lowerCAmelCase:      Optional[str] = None					,					__lowerCAmelCase:      Optional[int] = None					,					**__lowerCAmelCase:      Tuple					,					)			->			Any:




    '''simple docstring'''




    super().__init__(
        __snake_case					,					split=__snake_case					,					features=__snake_case					,					cache_dir=__snake_case					,					keep_in_memory=__snake_case					,					streaming=__snake_case					,					num_proc=__snake_case					,					**__snake_case					,					)
    __UpperCAmelCase         =							field
    __UpperCAmelCase         =							path_or_paths if isinstance(__snake_case					,					__snake_case ) else {self.split: path_or_paths}
    __UpperCAmelCase         =							Json(
        cache_dir=__snake_case					,					data_files=__snake_case					,					features=__snake_case					,					field=__snake_case					,					**__snake_case					,					)






  def 						_UpperCAmelCase					(					self:      Union[str, Any] )			->			Any:




    '''simple docstring'''




    if self.streaming:
      __UpperCAmelCase         =							self.builder.as_streaming_dataset(split=self.split )
    # Build regular (map-style) dataset
    else:
      __UpperCAmelCase         =							None
      __UpperCAmelCase         =							None
      __UpperCAmelCase         =							None
      __UpperCAmelCase         =							None

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






class       UpperCAmelCase__     :



  """simple docstring"""






  def __init__(					self:      List[str]					,					__lowerCAmelCase:      Dataset					,					__lowerCAmelCase:      Union[PathLike, BinaryIO]					,					__lowerCAmelCase:      Optional[int] = None					,					__lowerCAmelCase:      Optional[int] = None					,					**__lowerCAmelCase:      Dict					,					)			->			Dict:




    '''simple docstring'''




    if num_proc is not None and num_proc <= 0:
      raise ValueError(F'''num_proc {num_proc} must be an integer > 0.''' )

    __UpperCAmelCase         =							dataset
    __UpperCAmelCase         =							path_or_buf
    __UpperCAmelCase         =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
    __UpperCAmelCase         =							num_proc
    __UpperCAmelCase         =							"""utf-8"""
    __UpperCAmelCase         =							to_json_kwargs






  def 						_UpperCAmelCase					(					self:      Union[str, Any] )			->			int:




    '''simple docstring'''




    __UpperCAmelCase         =							self.to_json_kwargs.pop("path_or_buf"					,					__snake_case )
    __UpperCAmelCase         =							self.to_json_kwargs.pop("orient"					,					"records" )
    __UpperCAmelCase         =							self.to_json_kwargs.pop("lines"					,					True if orient == "records" else False )
    __UpperCAmelCase         =							self.to_json_kwargs.pop("index"					,					False if orient in ["split", "table"] else True )
    __UpperCAmelCase         =							self.to_json_kwargs.pop("compression"					,					__snake_case )

    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=__snake_case ) as buffer:
        __UpperCAmelCase         =							self._write(file_obj=__snake_case					,					orient=__snake_case					,					lines=__snake_case					,					index=__snake_case					,					**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." )
      __UpperCAmelCase         =							self._write(
          file_obj=self.path_or_buf					,					orient=__snake_case					,					lines=__snake_case					,					index=__snake_case					,					**self.to_json_kwargs )
    return written






  def 						_UpperCAmelCase					(					self:      Dict					,					__lowerCAmelCase:      List[Any] )			->			Any:




    '''simple docstring'''




    __UpperCAmelCase         =							args

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






  def 						_UpperCAmelCase					(					self:      int					,					__lowerCAmelCase:      BinaryIO					,					__lowerCAmelCase:      Any					,					__lowerCAmelCase:      Optional[int]					,					__lowerCAmelCase:      Union[str, Any]					,					**__lowerCAmelCase:      Optional[Any]					,					)			->			int:




    '''simple docstring'''




    __UpperCAmelCase         =							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"					,					):
        __UpperCAmelCase         =							self._batch_json((offset, orient, lines, index, to_json_kwargs) )
        written += file_obj.write(__snake_case )
    else:
      __UpperCAmelCase         =							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					,					__snake_case					,					__snake_case )]					,					)					,					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(__snake_case )

    return written











"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase							=  {
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


__lowerCamelCase							=  {
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				VOCAB_FILES_NAMES
					UpperCAmelCase__								=				PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__								=				PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__								=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__								=				SqueezeBertTokenizer

					def __init__(			self  :      Tuple     ,		__snake_case  :      str=None     ,		__snake_case  :      Optional[Any]=None     ,		__snake_case  :      str=True     ,		__snake_case  :      str="[UNK]"     ,		__snake_case  :      int="[SEP]"     ,		__snake_case  :      int="[PAD]"     ,		__snake_case  :      Any="[CLS]"     ,		__snake_case  :      List[Any]="[MASK]"     ,		__snake_case  :      Union[str, Any]=True     ,		__snake_case  :      Dict=None     ,		**__snake_case  :      str     ,		)			->							List[Any]:
											super().__init__(
											    __snake_case     ,		tokenizer_file=__snake_case     ,		do_lower_case=__snake_case     ,		unk_token=__snake_case     ,		sep_token=__snake_case     ,		pad_token=__snake_case     ,		cls_token=__snake_case     ,		mask_token=__snake_case     ,		tokenize_chinese_chars=__snake_case     ,		strip_accents=__snake_case     ,		**__snake_case     ,		)

											__magic_name__:	List[Any]							=       json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
											if (
											    normalizer_state.get("""lowercase"""     ,		__snake_case					) != do_lower_case
											    or normalizer_state.get("""strip_accents"""     ,		__snake_case					) != strip_accents
											    or normalizer_state.get("""handle_chinese_chars"""     ,		__snake_case					) != tokenize_chinese_chars
											):
																	__magic_name__:	Dict							=       getattr(__snake_case     ,		normalizer_state.pop("""type"""					)					)
																	__magic_name__:	Optional[Any]							=       do_lower_case
																	__magic_name__:	Any							=       strip_accents
																	__magic_name__:	Any							=       tokenize_chinese_chars
																	__magic_name__:	Union[str, Any]							=       normalizer_class(**__snake_case					)

											__magic_name__:	Optional[Any]							=       do_lower_case

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      List[Any]     ,		__snake_case  :      int=None					)			->							Optional[int]:
											__magic_name__:	int							=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

											if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

											return output

					def   lowerCamelCase__   (			self  :      Any     ,		__snake_case  :      List[int]     ,		__snake_case  :      Optional[List[int]] = None					)			->							List[int]:
											__magic_name__:	Dict							=       [self.sep_token_id]
											__magic_name__:	List[str]							=       [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					) * [0] + len(token_ids_a + sep					) * [1]






					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      str     ,		__snake_case  :      Optional[str] = None					)			->							Tuple[str]:
											__magic_name__:	Any							=       self._tokenizer.model.save(__snake_case     ,		name=__snake_case					)
											return tuple(__snake_case					)



