


'''simple docstring'''







import json
import sys






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[Any]       ):
       with open(lowerCamelCase  ,encoding='utf-8'       ) as f:
              _A					:     Optional[Any]     = json.load(lowerCamelCase       )

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

       for benchmark_name in sorted(lowerCamelCase       ):
              _A					:     Optional[Any]     = results[benchmark_name]

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

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

                     _A					:     Optional[Any]     = F' {new_val:f}' if isinstance(lowerCamelCase  ,(int, float)       ) else 'None'

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

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

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

       output_md.append('</details>'       )

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


if __name__ == "__main__":
   A				:					str    						=			sys.argv[1]
   A				:					List[str]    						=			sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)










'''simple docstring'''







import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
   import tensorflow as tf

if is_torch_available():
   import torch


A				:					Any    						=			logging.get_logger(__name__)


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str = None				,							SCREAMING_SNAKE_CASE      :							uuid.UUID = None				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None):
           if not conversation_id:
                  _A					:     str     = uuid.uuida()
           if past_user_inputs is None:
                  _A					:     List[Any]     = []
           if generated_responses is None:
                  _A					:     Union[str, Any]     = []

           _A					:     uuid.UUID     = conversation_id
           _A					:     List[str]     = past_user_inputs
           _A					:     List[str]     = generated_responses
           _A					:     Optional[str]     = text


    def __eq__(  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  return False
           if self.uuid == other.uuid:
                  return True
           return (
               self.new_user_input == other.new_user_input
               and self.past_user_inputs == other.past_user_inputs
               and self.generated_responses == other.generated_responses
           )


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							bool = False):
           if self.new_user_input:
                  if overwrite:
                         logger.warning(
                             F'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
                             F'with: "{text}".')
                         _A					:     Optional[Any]     = text
                  else:
                         logger.warning(
                             F'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
                             F'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input')
           else:
                  _A					:     Optional[Any]     = text


    def        A   (  self      :							Optional[int]):
           if self.new_user_input:
                  self.past_user_inputs.append(self.new_user_input)
           _A					:     Optional[Any]     = None


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							str):
           self.generated_responses.append(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           for user_input, generated_response in zip(self.past_user_inputs				,							self.generated_responses):
                  yield True, user_input
                  yield False, generated_response
           if self.new_user_input:
                  yield True, self.new_user_input


    def __repr__(  self      :							Dict):
           _A					:     Any     = F'Conversation id: {self.uuid} \n'
           for is_user, text in self.iter_texts():
                  _A					:     Optional[int]     = 'user' if is_user else 'bot'
                  output += F'{name} >> {text} \n'
           return output




@add_end_docstrings(
    a_    , R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "    , )
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[int]				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							str):
           super().__init__(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if self.tokenizer.pad_token_id is None:
                  _A					:     Any     = self.tokenizer.eos_token


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							**SCREAMING_SNAKE_CASE      :							str):
           _A					:     str     = {}
           _A					:     Union[str, Any]     = {}
           _A					:     List[str]     = {}

           if min_length_for_response is not None:
                  _A					:     Optional[Any]     = min_length_for_response
           if minimum_tokens is not None:
                  _A					:     Tuple     = minimum_tokens

           if "max_length" in generate_kwargs:
                  _A					:     List[Any]     = generate_kwargs['max_length']
                  # self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
           if clean_up_tokenization_spaces is not None:
                  _A					:     List[str]     = clean_up_tokenization_spaces

           if generate_kwargs:
                  forward_params.update(SCREAMING_SNAKE_CASE)
           return preprocess_params, forward_params, postprocess_params


    def __call__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[Conversation, List[Conversation]]				,							SCREAMING_SNAKE_CASE      :							Any=0				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Any     = super().__call__(SCREAMING_SNAKE_CASE				,							num_workers=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE) and len(SCREAMING_SNAKE_CASE) == 1:
                  return outputs[0]
           return outputs


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Conversation				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]=32):
           if not isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  raise ValueError('ConversationalPipeline, expects Conversation as inputs')
           if conversation.new_user_input is None:
                  raise ValueError(
                      F'Conversation with UUID {type(conversation.uuid)} does not contain new user input to process. '
                      'Add user inputs with the conversation\'s `add_user_input` method')
           if hasattr(self.tokenizer				,							'_build_conversation_input_ids'):
                  _A					:     Optional[Any]     = self.tokenizer._build_conversation_input_ids(SCREAMING_SNAKE_CASE)
           else:
                  # If the tokenizer cannot handle conversations, we default to only the old version
                  _A					:     Dict     = self._legacy_parse_and_tokenize(SCREAMING_SNAKE_CASE)

           if self.framework == "pt":
                  _A					:     Union[str, Any]     = torch.LongTensor([input_ids])
           elif self.framework == "tf":
                  _A					:     Optional[Any]     = tf.constant([input_ids])
           return {"input_ids": input_ids, "conversation": conversation}


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=10				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     str     = generate_kwargs.get('max_length'				,							self.model.config.max_length)

           _A					:     Any     = model_inputs['input_ids'].shape[1]
           if max_length - minimum_tokens < n:
                  logger.warning(F'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})')
                  _A					:     Dict     = max_length - minimum_tokens
                  _A					:     int     = model_inputs['input_ids'][:, -trim:]
                  if "attention_mask" in model_inputs:
                         _A					:     str     = model_inputs['attention_mask'][:, -trim:]
           _A					:     Any     = model_inputs.pop('conversation')
           _A					:     Optional[Any]     = max_length
           _A					:     int     = self.model.generate(**SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)
           if self.model.config.is_encoder_decoder:
                  _A					:     int     = 1
           else:
                  _A					:     List[Any]     = n
           return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[Any]=True):
           _A					:     Optional[Any]     = model_outputs['output_ids']
           _A					:     Optional[int]     = self.tokenizer.decode(
               output_ids[0]				,							skip_special_tokens=SCREAMING_SNAKE_CASE				,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE				,							)
           _A					:     Any     = model_outputs['conversation']
           conversation.mark_processed()
           conversation.append_response(SCREAMING_SNAKE_CASE)
           return conversation


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							Conversation):
           _A					:     Optional[Any]     = self.tokenizer.eos_token_id
           _A					:     List[Any]     = []
           for is_user, text in conversation.iter_texts():
                  if eos_token_id is not None:
                         input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE) + [eos_token_id])
                  else:
                         input_ids.extend(self.tokenizer.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE))

           if len(SCREAMING_SNAKE_CASE) > self.tokenizer.model_max_length:
                  _A					:     Dict     = input_ids[-self.tokenizer.model_max_length :]
           return input_ids






