






"""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 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):


							def __init__(			self      :						List[Any]			,    snake_case      :						NestedDataStructureLike[PathLike]			,    snake_case      :						Optional[NamedSplit] = None			,    snake_case      :						Optional[Features] = None			,    snake_case      :						str = None			,    snake_case      :						bool = False			,    snake_case      :						bool = False			,    snake_case      :						Optional[str] = None			,    snake_case      :						Optional[int] = None			,    **snake_case      :						Optional[int]			,    ):
													'''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			,    )
													A__						:  Optional[int]		  =  field
													A__						:  Tuple		  =  path_or_paths if isinstance(snake_case			,    snake_case							) else {self.split: path_or_paths}
													A__						:  List[Any]		  =  Json(
													    cache_dir=snake_case			,    data_files=snake_case			,    features=snake_case			,    field=snake_case			,    **snake_case			,    )


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													if self.streaming:
																			A__						:  List[Any]		  =  self.builder.as_streaming_dataset(split=self.split							)
													# Build regular (map-style) dataset
													else:
																			A__						:  int		  =  None
																			A__						:  Dict		  =  None
																			A__						:  Tuple		  =  None
																			A__						:  Optional[Any]		  =  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			,    )
																			A__						:  str		  =  self.builder.as_dataset(
																			    split=self.split			,    verification_mode=snake_case			,    in_memory=self.keep_in_memory							)
													return dataset






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						Dataset			,    snake_case      :						Union[PathLike, BinaryIO]			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[int] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





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

													A__						:  Union[str, Any]		  =  dataset
													A__						:  Any		  =  path_or_buf
													A__						:  str		  =  batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													A__						:  str		  =  num_proc
													A__						:  Union[str, Any]		  =  """utf-8"""
													A__						:  Optional[int]		  =  to_json_kwargs


							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													A__						:  Any		  =  self.to_json_kwargs.pop("""path_or_buf"""			,    snake_case							)
													A__						:  List[Any]		  =  self.to_json_kwargs.pop("""orient"""			,    """records"""							)
													A__						:  List[str]		  =  self.to_json_kwargs.pop("""lines"""			,    True if orient == """records""" else False							)
													A__						:  str		  =  self.to_json_kwargs.pop("""index"""			,    False if orient in ["""split""", """table"""] else True							)
													A__						:  Optional[int]		  =  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:
																									A__						:  Tuple		  =  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."""							)
																			A__						:  List[Any]		  =  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      :						List[Any]			,    snake_case      :						List[str]							):
													'''simple docstring'''





													A__  ,  A__  ,  A__  ,  A__  ,  A__						:  int		  =  args

													A__						:  Any		  =  query_table(
													    table=self.dataset.data			,    key=slice(snake_case			,    offset + self.batch_size							)			,    indices=self.dataset._indices			,    )
													A__						:  List[Any]		  =  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			,    snake_case      :						BinaryIO			,    snake_case      :						List[Any]			,    snake_case      :						Tuple			,    snake_case      :						List[str]			,    **snake_case      :						str			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  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"""			,    ):
																									A__						:  List[str]		  =  self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
																									written += file_obj.write(snake_case							)
													else:
																			A__  ,  A__						:  Optional[int]		  =  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
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

A_    =							logging.get_logger(__name__)


A_    =							{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

A_    =							{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

A_    =							{'''facebook/blenderbot-3B''': 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _lowerCAmelCase		(							)		->Any:
						A__						:  Any		  =  (
						    list(range(ord("""!"""      ),						ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      ),						ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      ),						ord("""ÿ"""      ) + 1      )      )
						)
						A__						:  Optional[int]		  =  bs[:]
						A__						:  Union[str, Any]		  =  0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCAmelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						A__						:  Optional[int]		  =  [chr(UpperCAmelCase__      ) for n in cs]
						return dict(zip(UpperCAmelCase__,						UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Dict      )		->Tuple:
						A__						:  Any		  =  set()
						A__						:  Optional[int]		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  Dict		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						int			,    snake_case      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						str="replace"			,    snake_case      :						Tuple="<s>"			,    snake_case      :						Dict="</s>"			,    snake_case      :						Optional[Any]="</s>"			,    snake_case      :						int="<s>"			,    snake_case      :						Dict="<unk>"			,    snake_case      :						List[Any]="<pad>"			,    snake_case      :						Union[str, Any]="<mask>"			,    snake_case      :						Optional[Any]=False			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else bos_token
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else eos_token
													A__						:  List[str]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else sep_token
													A__						:  Tuple		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else cls_token
													A__						:  Dict		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else unk_token
													A__						:  int		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  str		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

													super().__init__(
													    errors=snake_case			,    bos_token=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			,    add_prefix_space=snake_case			,    **snake_case			,    )

													with open(snake_case			,    encoding="""utf-8"""							) as vocab_handle:
																			A__						:  Dict		  =  json.load(snake_case							)
													A__						:  Tuple		  =  {v: k for k, v in self.encoder.items()}
													A__						:  Union[str, Any]		  =  errors  # how to handle errors in decoding
													A__						:  int		  =  bytes_to_unicode()
													A__						:  str		  =  {v: k for k, v in self.byte_encoder.items()}
													with open(snake_case			,    encoding="""utf-8"""							) as merges_handle:
																			A__						:  Tuple		  =  merges_handle.read().split("""\n"""							)[1:-1]
													A__						:  Optional[Any]		  =  [tuple(merge.split()							) for merge in bpe_merges]
													A__						:  List[str]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Union[str, Any]		  =  {}
													A__						:  int		  =  add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Optional[Any]		  =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													return dict(self.encoder			,    **self.added_tokens_encoder							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  Tuple		  =  tuple(snake_case							)
													A__						:  Union[str, Any]		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

													while True:
																			A__						:  Any		  =  min(snake_case			,    key=lambda snake_case							: self.bpe_ranks.get(snake_case			,    float("""inf"""							)							)							)
																			if bigram not in self.bpe_ranks:
																									break
																			A__  ,  A__						:  Optional[int]		  =  bigram
																			A__						:  Tuple		  =  []
																			A__						:  Dict		  =  0
																			while i < len(snake_case							):
																									try:
																															A__						:  int		  =  word.index(snake_case			,    snake_case							)
																									except ValueError:
																															new_word.extend(word[i:]							)
																															break
																									else:
																															new_word.extend(word[i:j]							)
																															A__						:  Optional[Any]		  =  j

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  Optional[int]		  =  tuple(snake_case							)
																			A__						:  str		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  List[str]		  =  get_pairs(snake_case							)
													A__						:  int		  =  """ """.join(snake_case							)
													A__						:  Optional[int]		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  Optional[Any]		  =  """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													return self.encoder.get(snake_case			,    self.encoder.get(self.unk_token							)							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						int							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  """""".join(snake_case							)
													A__						:  Dict		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





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

													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as f:
																			f.write(json.dumps(self.encoder			,    indent=2			,    sort_keys=snake_case			,    ensure_ascii=snake_case							) + """\n"""							)

													A__						:  str		  =  0
													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as writer:
																			writer.write("""#version: 0.2\n"""							)
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,    key=lambda snake_case							: kv[1]							):
																									if index != token_index:
																															logger.warning(
																															    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																															    """ Please check that the tokenizer is not corrupted!"""							)
																															A__						:  List[str]		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





													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							)

													if token_ids_a is None:
																			return [1] + ([0] * len(snake_case							)) + [1]
													return [1] + ([0] * len(snake_case							)) + [1, 1] + ([0] * len(snake_case							)) + [1]


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  [self.sep_token_id]
													A__						:  Optional[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 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Optional[int]			,    snake_case      :						List[Any]=False			,    **snake_case      :						Tuple							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  int		  =  """ """ + text
													return (text, kwargs)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													return token_ids_a + [self.eos_token_id]


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						"Conversation"							):
													'''simple docstring'''





													A__						:  str		  =  []
													for is_user, text in conversation.iter_texts():
																			if is_user:
																									# We need to space prefix as it's being done within blenderbot
																									inputs.append(""" """ + text							)
																			else:
																									# Generated responses should contain them already.
																									inputs.append(snake_case							)

													A__						:  Tuple		  =  """  """.join(snake_case							)
													A__						:  Dict		  =  self.encode(snake_case							)
													if len(snake_case							) > self.model_max_length:
																			A__						:  Optional[int]		  =  input_ids[-self.model_max_length :]
																			logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'							)
													return input_ids






