import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


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

_SCREAMING_SNAKE_CASE:						List[Any]    	=       {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_SCREAMING_SNAKE_CASE:						Union[str, Any]    	=       {
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

_SCREAMING_SNAKE_CASE:						List[str]    	=       {
    '''openai-gpt''': 512,
}






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      text.strip()
			if not text:
						return []
			_snake_case              =      text.split()
			return tokens

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=True  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=True  ,					):






					"""simple docstring"""
					if never_split is None:
								_snake_case              =      []
					_snake_case              =      do_lower_case
					_snake_case              =      set(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      tokenize_chinese_chars
					_snake_case              =      strip_accents
					_snake_case              =      do_split_on_punc



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None   ):






					"""simple docstring"""
					_snake_case              =      self.never_split.union(set(__SCREAMING_SNAKE_CASE   )   ) if never_split else self.never_split
					_snake_case              =      self._clean_text(__SCREAMING_SNAKE_CASE   )

					# This was added on November 1st, 2018 for the multilingual and Chinese
					# models. This is also applied to the English models now, but it doesn't
					# matter since the English models were not trained on any Chinese data
					# and generally don't have any Chinese data in them (there are Chinese
					# characters in the vocabulary because Wikipedia does have some Chinese
					# words in the English Wikipedia.).
					if self.tokenize_chinese_chars:
								_snake_case              =      self._tokenize_chinese_chars(__SCREAMING_SNAKE_CASE   )
					# prevents treating the same character with different unicode codepoints as different characters
					_snake_case              =      unicodedata.normalize("NFC"  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      whitespace_tokenize(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					for token in orig_tokens:
								if token not in never_split:
											if self.do_lower_case:
														_snake_case              =      token.lower()
														if self.strip_accents is not False:
																	_snake_case              =      self._run_strip_accents(__SCREAMING_SNAKE_CASE   )
											elif self.strip_accents:
														_snake_case              =      self._run_strip_accents(__SCREAMING_SNAKE_CASE   )
								split_tokens.extend(self._run_split_on_punc(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )   )

					_snake_case              =      whitespace_tokenize(" ".join(__SCREAMING_SNAKE_CASE   )   )
					return output_tokens



		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      unicodedata.normalize("NFD"  ,					__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					for char in text:
								_snake_case              =      unicodedata.category(__SCREAMING_SNAKE_CASE   )
								if cat == "Mn":
											continue
								output.append(__SCREAMING_SNAKE_CASE   )
					return "".join(__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any  ,					__SCREAMING_SNAKE_CASE    :				int=None   ):






					"""simple docstring"""
					if not self.do_split_on_punc or (never_split is not None and text in never_split):
								return [text]
					_snake_case              =      list(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      0
					_snake_case              =      True
					_snake_case              =      []
					while i < len(__SCREAMING_SNAKE_CASE   ):
								_snake_case              =      chars[i]
								if _is_punctuation(__SCREAMING_SNAKE_CASE   ):
											output.append([char]   )
											_snake_case              =      True
								else:
											if start_new_word:
														output.append([]   )
											_snake_case              =      False
											output[-1].append(__SCREAMING_SNAKE_CASE   )
								i += 1

					return ["".join(__SCREAMING_SNAKE_CASE   ) for x in output]



		def 				_a				(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      []
					for char in text:
								_snake_case              =      ord(__SCREAMING_SNAKE_CASE   )
								if self._is_chinese_char(__SCREAMING_SNAKE_CASE   ):
											output.append(" "   )
											output.append(__SCREAMING_SNAKE_CASE   )
											output.append(" "   )
								else:
											output.append(__SCREAMING_SNAKE_CASE   )
					return "".join(__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Any   ):






					"""simple docstring"""
					# This defines a "chinese character" as anything in the CJK Unicode block:
					#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
					#
					# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
					# despite its name. The modern Korean Hangul alphabet is a different block,
					# as is Japanese Hiragana and Katakana. Those alphabets are used to write
					# space-separated words, so they are not treated specially and handled
					# like the all of the other languages.
					if (
					    (cp >= 0X4_e_0_0 and cp <= 0X9_f_f_f)
					    or (cp >= 0X3_4_0_0 and cp <= 0X4_d_b_f)  #
					    or (cp >= 0X2_0_0_0_0 and cp <= 0X2_a_6_d_f)  #
					    or (cp >= 0X2_a_7_0_0 and cp <= 0X2_b_7_3_f)  #
					    or (cp >= 0X2_b_7_4_0 and cp <= 0X2_b_8_1_f)  #
					    or (cp >= 0X2_b_8_2_0 and cp <= 0X2_c_e_a_f)  #
					    or (cp >= 0Xf_9_0_0 and cp <= 0Xf_a_f_f)
					    or (cp >= 0X2_f_8_0_0 and cp <= 0X2_f_a_1_f)  #
					):  #
								return True

					return False







		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      []
					for char in text:
								_snake_case              =      ord(__SCREAMING_SNAKE_CASE   )
								if cp == 0 or cp == 0Xf_f_f_d or _is_control(__SCREAMING_SNAKE_CASE   ):
											continue
								if _is_whitespace(__SCREAMING_SNAKE_CASE   ):
											output.append(" "   )
								else:
											output.append(__SCREAMING_SNAKE_CASE   )
					return "".join(__SCREAMING_SNAKE_CASE   )






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      set()
			_snake_case              =      word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)  )
						_snake_case              =      char
			return pairs






def    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      text.replace("—"							,	"-"  )
			_snake_case              =      text.replace("–"							,	"-"  )
			_snake_case              =      text.replace("―"							,	"-"  )
			_snake_case              =      text.replace("…"							,	"..."  )
			_snake_case              =      text.replace("´"							,	"'"  )
			_snake_case              =      re.sub(R"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"							,	R" \1 "							,	lowerCamelCase_  )
			_snake_case              =      re.sub(R"\s*\n\s*"							,	" \n "							,	lowerCamelCase_  )
			_snake_case              =      re.sub(R"[^\S\n]+"							,	" "							,	lowerCamelCase_  )
			return text.strip()







class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      VOCAB_FILES_NAMES
		A_						 =      PRETRAINED_VOCAB_FILES_MAP
		A_						 =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						 =      ['''input_ids''', '''attention_mask''']



		def __init__(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int="<unk>"  ,					**__SCREAMING_SNAKE_CASE    :				Union[str, Any]   ):






					"""simple docstring"""
					super().__init__(unk_token=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )

					try:
								import ftfy
								from spacy.lang.en import English

								_snake_case              =      English()
								_snake_case              =      _nlp.tokenizer
								_snake_case              =      ftfy.fix_text
					except ImportError:
								logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."   )
								_snake_case              =      BasicTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE   )
								_snake_case              =      None

					with open(__SCREAMING_SNAKE_CASE  ,					encoding="utf-8"   ) as vocab_handle:
								_snake_case              =      json.load(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      {v: k for k, v in self.encoder.items()}
					with open(__SCREAMING_SNAKE_CASE  ,					encoding="utf-8"   ) as merges_handle:
								_snake_case              =      merges_handle.read().split("\n"   )[1:-1]
					_snake_case              =      [tuple(merge.split()   ) for merge in merges]
					_snake_case              =      dict(zip(__SCREAMING_SNAKE_CASE  ,					range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
					_snake_case              =      {}



		@property
		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return True



		@property
		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return len(self.encoder   )



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					return dict(self.encoder  ,					**self.added_tokens_encoder   )



		def 				_a				(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      tuple(token[:-1]   ) + (token[-1] + "</w>",)
					if token in self.cache:
								return self.cache[token]
					_snake_case              =      get_pairs(__SCREAMING_SNAKE_CASE   )

					if not pairs:
								return token + "</w>"

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

											if word[i] == first and i < len(__SCREAMING_SNAKE_CASE   ) - 1 and word[i + 1] == second:
														new_word.append(first + second   )
														i += 2
											else:
														new_word.append(word[i]   )
														i += 1
								_snake_case              =      tuple(__SCREAMING_SNAKE_CASE   )
								_snake_case              =      new_word
								if len(__SCREAMING_SNAKE_CASE   ) == 1:
											break
								else:
											_snake_case              =      get_pairs(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      " ".join(__SCREAMING_SNAKE_CASE   )
					if word == "\n  </w>":
								_snake_case              =      "\n</w>"
					_snake_case              =      word
					return word



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      []
					if self.fix_text is None:
								# Using BERT's BasicTokenizer
								_snake_case              =      self.nlp.tokenize(__SCREAMING_SNAKE_CASE   )
								for token in text:
											split_tokens.extend(list(self.bpe(__SCREAMING_SNAKE_CASE   ).split(" "   )   )   )
					else:
								# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
								_snake_case              =      self.nlp(text_standardize(self.fix_text(__SCREAMING_SNAKE_CASE   )   )   )
								for token in text:
											split_tokens.extend(list(self.bpe(token.text.lower()   ).split(" "   )   )   )
					return split_tokens



		def 				_a				(			self    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]   ):






					"""simple docstring"""
					return self.encoder.get(__SCREAMING_SNAKE_CASE  ,					self.encoder.get(self.unk_token   )   )



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					return self.decoder.get(__SCREAMING_SNAKE_CASE  ,					self.unk_token   )



		def 				_a				(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				Dict   ):






					"""simple docstring"""
					_snake_case              =      "".join(__SCREAMING_SNAKE_CASE   ).replace("</w>"  ,					" "   ).strip()
					return out_string







		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[str] = None   ):






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

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

					_snake_case              =      0
					with open(__SCREAMING_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 __SCREAMING_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!"   )
														_snake_case              =      token_index
											writer.write(" ".join(__SCREAMING_SNAKE_CASE   ) + "\n"   )
											index += 1

					return vocab_file, merge_file


import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


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

@dataclass
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      42
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				Union[str, Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )  ,					indent=2   ) + "\n"

@dataclass(frozen=_lowercase							)
class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		A_						 =      42
		A_						 =      None
		A_						 =      None
		A_						 =      None



		def 				_a				(			self    :				List[Any]   ):






					"""simple docstring"""
					return json.dumps(dataclasses.asdict(self   )   ) + "\n"

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					raise NotImplementedError()



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					if len(self.get_labels()   ) > 1:
								_snake_case              =      self.get_labels()[int(example.label   )]
					return example







		@classmethod
		def 				_a				(			cls    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple=None   ):






					"""simple docstring"""
					with open(__SCREAMING_SNAKE_CASE  ,					"r"  ,					encoding="utf-8-sig"   ) as f:
								return list(csv.reader(__SCREAMING_SNAKE_CASE  ,					delimiter="\t"  ,					quotechar=__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]="classification"  ,					__SCREAMING_SNAKE_CASE    :				Any=False   ):






					"""simple docstring"""
					_snake_case              =      [] if labels is None else labels
					_snake_case              =      [] if examples is None else examples
					_snake_case              =      mode
					_snake_case              =      verbose



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					return len(self.examples   )



		def __getitem__(			self    :				List[str]  ,					__SCREAMING_SNAKE_CASE    :				Tuple   ):






					"""simple docstring"""
					if isinstance(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								return SingleSentenceClassificationProcessor(labels=self.labels  ,					examples=self.examples[idx]   )
					return self.examples[idx]



		@classmethod
		def 				_a				(			cls    :				int  ,					__SCREAMING_SNAKE_CASE    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=""  ,					__SCREAMING_SNAKE_CASE    :				Tuple=0  ,					__SCREAMING_SNAKE_CASE    :				Dict=1  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=False  ,					**__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples_from_csv(
					    __SCREAMING_SNAKE_CASE  ,					split_name=__SCREAMING_SNAKE_CASE  ,					column_label=__SCREAMING_SNAKE_CASE  ,					column_text=__SCREAMING_SNAKE_CASE  ,					column_id=__SCREAMING_SNAKE_CASE  ,					skip_first_row=__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE  ,					)
					return processor



		@classmethod
		def 				_a				(			cls    :				Any  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					**__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      cls(**__SCREAMING_SNAKE_CASE   )
					processor.add_examples(__SCREAMING_SNAKE_CASE  ,					labels=__SCREAMING_SNAKE_CASE   )
					return processor



		def 				_a				(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				List[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict=""  ,					__SCREAMING_SNAKE_CASE    :				str=0  ,					__SCREAMING_SNAKE_CASE    :				List[str]=1  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=None  ,					__SCREAMING_SNAKE_CASE    :				List[str]=False  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				str=False  ,					):






					"""simple docstring"""
					_snake_case              =      self._read_tsv(__SCREAMING_SNAKE_CASE   )
					if skip_first_row:
								_snake_case              =      lines[1:]
					_snake_case              =      []
					_snake_case              =      []
					_snake_case              =      []
					for i, line in enumerate(__SCREAMING_SNAKE_CASE   ):
								texts.append(line[column_text]   )
								labels.append(line[column_label]   )
								if column_id is not None:
											ids.append(line[column_id]   )
								else:
											_snake_case              =      F'{split_name}-{i}' if split_name else str(__SCREAMING_SNAKE_CASE   )
											ids.append(__SCREAMING_SNAKE_CASE   )

					return self.add_examples(
					    __SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					overwrite_labels=__SCREAMING_SNAKE_CASE  ,					overwrite_examples=__SCREAMING_SNAKE_CASE   )



		def 				_a				(			self    :				Optional[Any]  ,					__SCREAMING_SNAKE_CASE    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				int=None  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Tuple=False  ,					__SCREAMING_SNAKE_CASE    :				str=False   ):






					"""simple docstring"""
					if labels is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(
								    F'Text and labels have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is not None and len(__SCREAMING_SNAKE_CASE   ) != len(__SCREAMING_SNAKE_CASE   ):
								raise ValueError(F'Text and ids have mismatched lengths {len(__SCREAMING_SNAKE_CASE   )} and {len(__SCREAMING_SNAKE_CASE   )}'   )
					if ids is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					if labels is None:
								_snake_case              =      [None] * len(__SCREAMING_SNAKE_CASE   )
					_snake_case              =      []
					_snake_case              =      set()
					for text_or_text_and_label, label, guid in zip(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   ):
								if isinstance(__SCREAMING_SNAKE_CASE  ,					(tuple, list)   ) and label is None:
											_snake_case       , _snake_case              =      text_or_text_and_label
								else:
											_snake_case              =      text_or_text_and_label
								added_labels.add(__SCREAMING_SNAKE_CASE   )
								examples.append(InputExample(guid=__SCREAMING_SNAKE_CASE  ,					text_a=__SCREAMING_SNAKE_CASE  ,					text_b=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					# Update examples
					if overwrite_examples:
								_snake_case              =      examples
					else:
								self.examples.extend(__SCREAMING_SNAKE_CASE   )

					# Update labels
					if overwrite_labels:
								_snake_case              =      list(__SCREAMING_SNAKE_CASE   )
					else:
								_snake_case              =      list(set(self.labels   ).union(__SCREAMING_SNAKE_CASE   )   )

					return self.examples







		def 				_a				(			self    :				int  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]=None  ,					__SCREAMING_SNAKE_CASE    :				Any=False  ,					__SCREAMING_SNAKE_CASE    :				Any=0  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				List[str]=None  ,					):






					"""simple docstring"""
					if max_length is None:
								_snake_case              =      tokenizer.max_len

					_snake_case              =      {label: i for i, label in enumerate(self.labels   )}

					_snake_case              =      []
					for ex_index, example in enumerate(self.examples   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Tokenizing example {ex_index}'   )

								_snake_case              =      tokenizer.encode(
								    example.text_a  ,					add_special_tokens=__SCREAMING_SNAKE_CASE  ,					max_length=min(__SCREAMING_SNAKE_CASE  ,					tokenizer.max_len   )  ,					)
								all_input_ids.append(__SCREAMING_SNAKE_CASE   )

					_snake_case              =      max(len(__SCREAMING_SNAKE_CASE   ) for input_ids in all_input_ids   )

					_snake_case              =      []
					for ex_index, (input_ids, example) in enumerate(zip(__SCREAMING_SNAKE_CASE  ,					self.examples   )   ):
								if ex_index % 10_000 == 0:
											logger.info(F'Writing example {ex_index}/{len(self.examples   )}'   )
								# The mask has 1 for real tokens and 0 for padding tokens. Only real
								# tokens are attended to.
								_snake_case              =      [1 if mask_padding_with_zero else 0] * len(__SCREAMING_SNAKE_CASE   )

								# Zero-pad up to the sequence length.
								_snake_case              =      batch_length - len(__SCREAMING_SNAKE_CASE   )
								if pad_on_left:
											_snake_case              =      ([pad_token] * padding_length) + input_ids
											_snake_case              =      ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
								else:
											_snake_case              =      input_ids + ([pad_token] * padding_length)
											_snake_case              =      attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )
								if len(__SCREAMING_SNAKE_CASE   ) != batch_length:
											raise ValueError(F'Error with input length {len(__SCREAMING_SNAKE_CASE   )} vs {batch_length}'   )

								if self.mode == "classification":
											_snake_case              =      label_map[example.label]
								elif self.mode == "regression":
											_snake_case              =      float(example.label   )
								else:
											raise ValueError(self.mode   )

								if ex_index < 5 and self.verbose:
											logger.info("*** Example ***"   )
											logger.info(F'guid: {example.guid}'   )
											logger.info(F'input_ids: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in input_ids]   )}'   )
											logger.info(F'attention_mask: {" ".join([str(__SCREAMING_SNAKE_CASE   ) for x in attention_mask]   )}'   )
											logger.info(F'label: {example.label} (id = {label})'   )

								features.append(InputFeatures(input_ids=__SCREAMING_SNAKE_CASE  ,					attention_mask=__SCREAMING_SNAKE_CASE  ,					label=__SCREAMING_SNAKE_CASE   )   )

					if return_tensors is None:
								return features
					elif return_tensors == "tf":
								if not is_tf_available():
											raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported"   )
								import tensorflow as tf

								def gen():
											for ex in features:
														yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

								_snake_case              =      tf.data.Dataset.from_generator(
								    __SCREAMING_SNAKE_CASE  ,					({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,					({"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )}, tf.TensorShape([]   ))  ,					)
								return dataset
					elif return_tensors == "pt":
								if not is_torch_available():
											raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported"   )
								import torch
								from torch.utils.data import TensorDataset

								_snake_case              =      torch.tensor([f.input_ids for f in features]  ,					dtype=torch.long   )
								_snake_case              =      torch.tensor([f.attention_mask for f in features]  ,					dtype=torch.long   )
								if self.mode == "classification":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.long   )
								elif self.mode == "regression":
											_snake_case              =      torch.tensor([f.label for f in features]  ,					dtype=torch.float   )

								_snake_case              =      TensorDataset(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )
								return dataset
					else:
								raise ValueError("return_tensors should be one of 'tf' or 'pt'"   )

