
"""simple docstring"""




import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_ta import TaTokenizer
else:
   _A     :			Tuple  						=			None


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_A     :			Any  						=			{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   __lowerCAmelCase				=							TaTokenizer

   __lowerCAmelCase				=							[]
   def __init__(     self      ,       _a=None      ,       _a=None      ,       _a="</s>"      ,       _a="<unk>"      ,       _a="<pad>"      ,       _a=100      ,       _a=None      ,       **_a      ,       ):
          # Add extra_ids to the special token list
          if extra_ids > 0 and additional_special_tokens is None:
                 lowercase       :							str									=				[f"""<extra_id_{i}>""" for i in range(_a							)]
          elif extra_ids > 0 and additional_special_tokens is not None:
                 # Check that we have the right number of extra special tokens
                 lowercase       :							Optional[Any]									=				len(set(filter(lambda _a							: bool("extra_id_" in str(_a							)							)      ,       _a							)							)							)
                 if extra_tokens != extra_ids:
                        raise ValueError(
                            f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                            " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                            " tokens"							)

          super().__init__(
              _a      ,       tokenizer_file=_a      ,       eos_token=_a      ,       unk_token=_a      ,       pad_token=_a      ,       extra_ids=_a      ,       additional_special_tokens=_a      ,       **_a      ,       )

          lowercase       :							List[Any]									=				vocab_file
          lowercase       :							str									=				False if not self.vocab_file else True
          lowercase       :							int									=				extra_ids
   @staticmethod
   def      __magic_name__     (     _a      ,       _a      ,       _a							):
          if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
                 lowercase       :							Dict									=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
                 if init_max_model_length is not None and init_max_model_length != max_model_length:
                        return init_max_model_length
                 elif init_max_model_length is None:
                        warnings.warn(
                            "This tokenizer was incorrectly instantiated with a model max length of"
                            f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
                            " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                            " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                            f""" {pretrained_model_name_or_path} automatically truncating your input to"""
                            f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
                            f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
                            " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                            " instantiate this tokenizer with `model_max_length` set to your preferred value."      ,       _a      ,       )

          return max_model_length
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                     "tokenizer."							)

          if not os.path.isdir(_a							):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
                 return
          lowercase       :							Dict									=				os.path.join(
              _a      ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(_a							):
                 copyfile(self.vocab_file      ,       _a							)
                 logger.info(f"""Copy vocab file to {out_vocab_file}"""							)

          return (out_vocab_file,)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Tuple									=				token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a
          else:
                 lowercase       :							Union[str, Any]									=				token_ids_a + [self.eos_token_id]
                 return self.prefix_tokens + token_ids_a + token_ids_a
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Any									=				[self.eos_token_id]

          if token_ids_a is None:
                 return len(token_ids_a + eos							) * [0]
          return len(token_ids_a + eos + token_ids_a + eos							) * [0]
   def      __magic_name__     (     self							):
          return list(
              set(filter(lambda _a							: bool(re.search(R"<extra_id_\d+>"      ,       _a							)							) is not None      ,       self.additional_special_tokens							)							)							)

   def      __magic_name__     (     self							):
          return [self.convert_tokens_to_ids(_a							) for token in self.get_sentinel_tokens()]




"""simple docstring"""




from argparse import ArgumentParser

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


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__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     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__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(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				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=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       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=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

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

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


