


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


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

UpperCAmelCase_				:			str   =	{'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_				:			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 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Optional[int]					       =						VOCAB_FILES_NAMES
      snake_case__    :					Optional[int]					       =						PRETRAINED_VOCAB_FILES_MAP
      snake_case__    :					Tuple					       =						['''input_ids''', '''attention_mask''']
      snake_case__    :					Any					       =						None
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=None					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=None					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Tuple="<unk>"					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]="<s>"					,							SCREAMING_SNAKE_CASE__    :	List[str]="</s>"					,							SCREAMING_SNAKE_CASE__    :	List[Any]="<pad>"					,							SCREAMING_SNAKE_CASE__    :	int=False					,							SCREAMING_SNAKE_CASE__    :	Tuple=False					,							**SCREAMING_SNAKE_CASE__    :	str					,							)		->      List[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__					,							)
         a_ :		Union[str, Any]							=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
         if pre_tok_state.get('add_prefix_space'					,							SCREAMING_SNAKE_CASE__					) != add_prefix_space:
            a_ :		Optional[Any]							=  getattr(SCREAMING_SNAKE_CASE__					,							pre_tok_state.pop('type'					)					)
            a_ :		Dict							=  add_prefix_space
            a_ :		List[Any]							=  pre_tok_class(**SCREAMING_SNAKE_CASE__					)

         a_ :		Optional[Any]							=  add_prefix_space
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							*SCREAMING_SNAKE_CASE__    :	str					,							**SCREAMING_SNAKE_CASE__    :	int					)		->      BatchEncoding:
         a_ :		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    :	Optional[int]					,							*SCREAMING_SNAKE_CASE__    :	Tuple					,							**SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      BatchEncoding:
         a_ :		int							=  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    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	str					,							SCREAMING_SNAKE_CASE__    :	Optional[str] = None					)		->      Tuple[str]:
         a_ :		List[Any]							=  self._tokenizer.model.save(SCREAMING_SNAKE_CASE__					,							name=SCREAMING_SNAKE_CASE__					)
         return tuple(SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	"Conversation"					)		->      List[int]:
         a_ :		List[str]							=  []
         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:
            a_ :		Union[str, Any]							=  input_ids[-self.model_max_length :]
         return input_ids






import json
import sys







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      List[str]    )     ->		Tuple:

       """simple docstring"""

       with open(__A  ,   encoding='utf-8'    ) as f:
              a_ :		Union[str, Any]							=  json.load(__A    )

       a_ :		Any							=  ['<details>', '<summary>Show updated benchmarks!</summary>', ' ']

       for benchmark_name in sorted(__A    ):
              a_ :		List[str]							=  results[benchmark_name]

              a_ :		int							=  benchmark_name.split('/'    )[-1]
              output_md.append(F"""### Benchmark: {benchmark_file_name}"""    )

              a_ :		Any							=  '| metric |'
              a_ :		Optional[Any]							=  '|--------|'
              a_ :		int							=  '| new / old (diff) |'
              for metric_name in sorted(__A    ):
                     a_ :		List[Any]							=  benchmark_res[metric_name]
                     a_ :		int							=  metric_vals['new']
                     a_ :		Union[str, Any]							=  metric_vals.get('old'  ,   __A    )
                     a_ :		Optional[int]							=  metric_vals.get('diff'  ,   __A    )

                     a_ :		str							=  F""" {new_val:f}""" if isinstance(__A  ,   (int, float)    ) else 'None'

                     if old_val is not None:
                            val_str += F""" / {old_val:f}""" if isinstance(__A  ,   (int, float)    ) else "None"
                     if dif_val is not None:
                            val_str += F""" ({dif_val:f})""" if isinstance(__A  ,   (int, float)    ) else "None"

                     title += " " + metric_name + " |"
                     lines += "---|"
                     value += val_str + " |"

              output_md += [title, lines, value, " "]

       output_md.append('</details>'    )

       with open(__A  ,   'w'  ,   encoding='utf-8'    ) as f:
              f.writelines('\n'.join(__A    )    )


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	sys.argv[1]
   UpperCAmelCase_				:			Any   =	sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)


