



import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 				__snake_case						(     UpperCamelCase_						):




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       str):

									with open(A_				,				encoding='''utf-8''') as input_file:
											lowerCAmelCase_			:     List[Any]									= re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)''')
											lowerCAmelCase_			:     Any									= input_file.read()
											lowerCAmelCase_			:     int									= regexp.search(A_)

									return match




							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       str):

									with open(A_				,				encoding='''utf-8''') as input_file:
											lowerCAmelCase_			:     int									= re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''				,				re.DOTALL)
											lowerCAmelCase_			:     Dict									= input_file.read()
											# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
											lowerCAmelCase_			:     Optional[Any]									= regexp.finditer(A_)

									lowerCAmelCase_			:     str									= [match for match in matches if match is not None and match.group(1) is not None]
									return matches[0] if matches else None




							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     int									= Path('''./datasets''')
									lowerCAmelCase_			:     str									= list(dataset_paths.absolute().glob('''**/*.py'''))

									for dataset in dataset_files:
											if self._no_encoding_on_file_open(str(A_)):
													raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")







							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     Tuple									= Path('''./datasets''')
									lowerCAmelCase_			:     Union[str, Any]									= list(dataset_paths.absolute().glob('''**/*.py'''))

									for dataset in dataset_files:
											if self._no_print_statements(str(A_)):
													raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""")










"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


lowercase__		:				Optional[Any]          =	logging.get_logger(__name__)

lowercase__		:				List[str]          =	{"""tokenizer_file""": """tokenizer.json"""}

lowercase__		:				List[str]          =	{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     ["""input_ids""", """attention_mask"""]
      _SCREAMING_SNAKE_CASE  				=     None



      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]="<unk>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="</s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<pad>"    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      Any=False    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			unk_token=SCREAMING_SNAKE_CASE_    ,			bos_token=SCREAMING_SNAKE_CASE_    ,			eos_token=SCREAMING_SNAKE_CASE_    ,			pad_token=SCREAMING_SNAKE_CASE_    ,			add_prefix_space=SCREAMING_SNAKE_CASE_    ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_    ,			)
        lowerCAmelCase_   :				Optional[Any]								= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get('add_prefix_space'    ,			SCREAMING_SNAKE_CASE_       ) != add_prefix_space:
          lowerCAmelCase_   :				str								= getattr(SCREAMING_SNAKE_CASE_    ,			pre_tok_state.pop('type'       )       )
          lowerCAmelCase_   :				str								= add_prefix_space
          lowerCAmelCase_   :				str								= pre_tok_class(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				str								= add_prefix_space



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )
        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

        return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )

        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

        return super()._encode_plus(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = None       ):
        lowerCAmelCase_   :				Optional[int]								= self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,			name=SCREAMING_SNAKE_CASE_       )
        return tuple(SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      "Conversation"       ):
        lowerCAmelCase_   :				int								= []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       ) + [self.eos_token_id]       )

        if len(SCREAMING_SNAKE_CASE_       ) > self.model_max_length:
          lowerCAmelCase_   :				Any								= input_ids[-self.model_max_length :]
        return input_ids





