





"""simple docstring"""
import json
import sys




def       __snake_case    (   __A			,__A     )		->					Union[str, Any]:




   with open(_UpperCamelCase			,encoding="""utf-8"""     ) as f:
      lowercase     :      int	=							json.load(_UpperCamelCase     )

   lowercase     :      Tuple	=							["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

   for benchmark_name in sorted(_UpperCamelCase     ):
      lowercase     :      List[Any]	=							results[benchmark_name]

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

      lowercase     :      List[str]	=							"""| metric |"""
      lowercase     :      List[Any]	=							"""|--------|"""
      lowercase     :      List[str]	=							"""| new / old (diff) |"""
      for metric_name in sorted(_UpperCamelCase     ):
         lowercase     :      Optional[Any]	=							benchmark_res[metric_name]
         lowercase     :      str	=							metric_vals["""new"""]
         lowercase     :      Tuple	=							metric_vals.get("""old"""			,_UpperCamelCase     )
         lowercase     :      Any	=							metric_vals.get("""diff"""			,_UpperCamelCase     )

         lowercase     :      Optional[int]	=							F''' {new_val:f}''' if isinstance(_UpperCamelCase			,(int, float)     ) else """None"""

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

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

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

   output_md.append("""</details>"""     )

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


if __name__ == "__main__":
   lowerCAmelCase:						Dict				   =sys.argv[1]
   lowerCAmelCase:						Tuple				   =sys.argv[2]

   format_json_to_md(input_json_file, output_md_file)






"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 ="""Speech2TextFeatureExtractor"""
							__UpperCAmelCase		:       Union[str, Any]							 ="""Speech2TextTokenizer"""

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__(__a       ,  __a      )
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False

							def __call__(	self       ,  *__a       ,  **__a      ):
										# For backward compatibility
										if self._in_target_context_manager:
													return self.current_processor(*__a       ,  **__a      )

										if "raw_speech" in kwargs:
													warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."      )
													__lowerCAmelCase							       =     kwargs.pop("raw_speech"      )
										else:
													__lowerCAmelCase							       =     kwargs.pop("audio"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("sampling_rate"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("text"       ,  __a      )
										if len(__a      ) > 0:
													__lowerCAmelCase							       =     args[0]
													__lowerCAmelCase							       =     args[1:]

										if audio is None and text is None:
													raise ValueError("You need to specify either an `audio` or `text` input to process."      )

										if audio is not None:
													__lowerCAmelCase							       =     self.feature_extractor(__a       ,  *__a       ,  sampling_rate=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.tokenizer(__a       ,  **__a      )

										if text is None:
													return inputs
										elif audio is None:
													return encodings
										else:
													__lowerCAmelCase							       =     encodings["input_ids"]
													return inputs

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.batch_decode(*__a       ,  **__a      )

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.decode(*__a       ,  **__a      )



							@contextmanager
							def      snake_case							(	self      ):
										warnings.warn(
										    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
										    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
										    "your audio inputs, or in a separate call."      )
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     self.tokenizer
										yield
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False




