




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowercase_	:				Union[str, Any]  				= logging.get_logger(__name__)

lowercase_	:				str  				= {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase_	:				List[Any]  				= {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

lowercase_	:				int  				= {
    'roberta-base': 5_12,
    'roberta-large': 5_12,
    'roberta-large-mnli': 5_12,
    'distilroberta-base': 5_12,
    'roberta-base-openai-detector': 5_12,
    'roberta-large-openai-detector': 5_12,
}






class     __lowerCAmelCase (       _UpperCAmelCase ):
			snake_case_      : Any      =     VOCAB_FILES_NAMES
			snake_case_      : Tuple      =     PRETRAINED_VOCAB_FILES_MAP
			snake_case_      : List[str]      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case_      : List[Any]      =     ["input_ids", "attention_mask"]
			snake_case_      : Optional[Any]      =     RobertaTokenizer







			def __init__(       self    :			Any    ,    snake_case__    :			Dict=None    ,    snake_case__    :			int=None    ,    snake_case__    :			str=None    ,    snake_case__    :			Optional[int]="replace"    ,    snake_case__    :			Optional[Any]="<s>"    ,    snake_case__    :			List[Any]="</s>"    ,    snake_case__    :			List[str]="</s>"    ,    snake_case__    :			List[str]="<s>"    ,    snake_case__    :			List[str]="<unk>"    ,    snake_case__    :			Tuple="<pad>"    ,    snake_case__    :			Optional[Any]="<mask>"    ,    snake_case__    :			str=False    ,    snake_case__    :			Union[str, Any]=True    ,    **snake_case__    :			Dict    ,    ):




									"""simple docstring"""


									super().__init__(
									    snake_case__    ,    snake_case__    ,    tokenizer_file=snake_case__    ,    errors=snake_case__    ,    bos_token=snake_case__    ,    eos_token=snake_case__    ,    sep_token=snake_case__    ,    cls_token=snake_case__    ,    unk_token=snake_case__    ,    pad_token=snake_case__    ,    mask_token=snake_case__    ,    add_prefix_space=snake_case__    ,    trim_offsets=snake_case__    ,    **snake_case__    ,    )

									_UpperCAmelCase													=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
									if pre_tok_state.get("add_prefix_space"    ,    snake_case__      ) != add_prefix_space:
															_UpperCAmelCase													=     getattr(snake_case__    ,    pre_tok_state.pop("type"      )      )
															_UpperCAmelCase													=     add_prefix_space
															_UpperCAmelCase													=     pre_tok_class(**snake_case__      )

									_UpperCAmelCase													=     add_prefix_space

									_UpperCAmelCase													=     "post_processor"
									_UpperCAmelCase													=     getattr(self.backend_tokenizer    ,    snake_case__    ,    snake_case__      )
									if tokenizer_component_instance:
															_UpperCAmelCase													=     json.loads(tokenizer_component_instance.__getstate__()      )

															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
															if "sep" in state:
																					_UpperCAmelCase													=     tuple(state["sep"]      )
															if "cls" in state:
																					_UpperCAmelCase													=     tuple(state["cls"]      )

															_UpperCAmelCase													=     False

															if state.get("add_prefix_space"    ,    snake_case__      ) != add_prefix_space:
																					_UpperCAmelCase													=     add_prefix_space
																					_UpperCAmelCase													=     True

															if state.get("trim_offsets"    ,    snake_case__      ) != trim_offsets:
																					_UpperCAmelCase													=     trim_offsets
																					_UpperCAmelCase													=     True

															if changes_to_apply:
																					_UpperCAmelCase													=     getattr(snake_case__    ,    state.pop("type"      )      )
																					_UpperCAmelCase													=     component_class(**snake_case__      )
																					setattr(self.backend_tokenizer    ,    snake_case__    ,    snake_case__      )







			@property
			def UpperCamelCase			(       self    :			Optional[Any]      ):




									"""simple docstring"""


									if self._mask_token is None:
															if self.verbose:
																					logger.error("Using mask_token, but it is not set yet."      )
															return None
									return str(self._mask_token      )







			@mask_token.setter
			def UpperCamelCase			(       self    :			Dict    ,    snake_case__    :			Dict      ):




									"""simple docstring"""


									_UpperCAmelCase													=     AddedToken(snake_case__    ,    lstrip=snake_case__    ,    rstrip=snake_case__      ) if isinstance(snake_case__    ,    snake_case__      ) else value
									_UpperCAmelCase													=     value







			def UpperCamelCase			(       self    :			Tuple    ,    *snake_case__    :			Tuple    ,    **snake_case__    :			Tuple      ):




									"""simple docstring"""


									_UpperCAmelCase													=     kwargs.get("is_split_into_words"    ,    snake_case__      )
									assert self.add_prefix_space or not is_split_into_words, (
									    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
									    "to use it with pretokenized inputs."
									)

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







			def UpperCamelCase			(       self    :			Optional[int]    ,    *snake_case__    :			Dict    ,    **snake_case__    :			List[str]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     kwargs.get("is_split_into_words"    ,    snake_case__      )

									assert self.add_prefix_space or not is_split_into_words, (
									    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
									    "to use it with pretokenized inputs."
									)

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







			def UpperCamelCase			(       self    :			Tuple    ,    snake_case__    :			Dict    ,    snake_case__    :			Tuple = None      ):




									"""simple docstring"""


									_UpperCAmelCase													=     self._tokenizer.model.save(snake_case__    ,    name=snake_case__      )
									return tuple(snake_case__      )







			def UpperCamelCase			(       self    :			List[str]    ,    snake_case__    :			List[Any]    ,    snake_case__    :			Dict=None      ):




									"""simple docstring"""


									_UpperCAmelCase													=     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
															return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







			def UpperCamelCase			(       self    :			Any    ,    snake_case__    :			Tuple    ,    snake_case__    :			int = None      ):




									"""simple docstring"""


									_UpperCAmelCase													=     [self.sep_token_id]
									_UpperCAmelCase													=     [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]











"""simple docstring"""





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)  # pylint: disable=invalid-name


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(SCREAMING_SNAKE_CASE			):
                  return ext

      raise Exception(
          F'Unable to determine file format from file extension {path}. '
          F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'			)


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      lowerCAmelCase  						=       pipeline(
          task=args.task							,						model=args.model if args.model else None							,						config=args.config							,						tokenizer=args.tokenizer							,						device=args.device							,						)
      lowerCAmelCase  						=       try_infer_format_from_ext(args.input			) if args.format == """infer""" else args.format
      lowerCAmelCase  						=       PipelineDataFormat.from_str(
          format=SCREAMING_SNAKE_CASE							,						output_path=args.output							,						input_path=args.input							,						column=args.column if args.column else nlp.default_input_names							,						overwrite=args.overwrite							,						)
      return RunCommand(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE			)






class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase )		->     Union[str, Any]:
            lowerCAmelCase  						=       nlp
            lowerCAmelCase  						=       reader





      @staticmethod
      def     _snake_case			(       lowercase )		->     Optional[int]:
            lowerCAmelCase  						=       parser.add_parser("""run"""      ,						help="""Run a pipeline through the CLI""" )
            run_parser.add_argument("""--task"""      ,						choices=get_supported_tasks()      ,						help="""Task to run""" )
            run_parser.add_argument("""--input"""      ,						type=lowercase      ,						help="""Path to the file to use for inference""" )
            run_parser.add_argument("""--output"""      ,						type=lowercase      ,						help="""Path to the file that will be used post to write results.""" )
            run_parser.add_argument("""--model"""      ,						type=lowercase      ,						help="""Name or path to the model to instantiate.""" )
            run_parser.add_argument("""--config"""      ,						type=lowercase      ,						help="""Name or path to the model's config to instantiate.""" )
            run_parser.add_argument(
                """--tokenizer"""      ,						type=lowercase      ,						help="""Name of the tokenizer to use. (default: same as the model name)""" )
            run_parser.add_argument(
                """--column"""      ,						type=lowercase      ,						help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)"""      ,						)
            run_parser.add_argument(
                """--format"""      ,						type=lowercase      ,						default="""infer"""      ,						choices=PipelineDataFormat.SUPPORTED_FORMATS      ,						help="""Input format to read from"""      ,						)
            run_parser.add_argument(
                """--device"""      ,						type=lowercase      ,						default=-1      ,						help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"""      ,						)
            run_parser.add_argument("""--overwrite"""      ,						action="""store_true"""      ,						help="""Allow overwriting the output file.""" )
            run_parser.set_defaults(func=lowercase )





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase						,		lowerCAmelCase  						=       self._nlp, []

            for entry in self._reader:
                  lowerCAmelCase  						=       nlp(**lowercase ) if self._reader.is_multi_columns else nlp(lowercase )
                  if isinstance(lowercase      ,						lowercase ):
                        outputs.append(lowercase )
                  else:
                        outputs += output

        # Saving data
            if self._nlp.binary_output:
                  lowerCAmelCase  						=       self._reader.save_binary(lowercase )
                  logger.warning(f'Current pipeline requires output to be in binary format, saving at {binary_path}' )
            else:
                  self._reader.save(lowercase )






