
from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


_UpperCAmelCase        =				logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase_     )
class 							_UpperCamelCase					(	lowerCAmelCase_     ):

       def __init__(		self: int					,   **_SCREAMING_SNAKE_CASE: List[str]		)       -> Dict:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE		)

              if self.framework != "pt":
                     raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''		)
              # No specific FOR_XXX available yet

       def __call__(		self: List[str]					,   _SCREAMING_SNAKE_CASE: Union[np.ndarray, bytes, str]					,   **_SCREAMING_SNAKE_CASE: List[str]		)       -> Optional[int]:




              """simple docstring"""


              return super().__call__(_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Optional[Any]					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  {}
              if "candidate_labels" in kwargs:
                     UpperCamelCase_          =  kwargs["candidate_labels"]
              if "hypothesis_template" in kwargs:
                     UpperCamelCase_          =  kwargs["hypothesis_template"]

              return preprocess_params, {}, {}

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]=None					,   _SCREAMING_SNAKE_CASE: str="This is a sound of {}."		)       -> Tuple:




              """simple docstring"""


              if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     if audio.startswith("http://"		) or audio.startswith("https://"		):
                            # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                            # like http_huggingface_co.png
                            UpperCamelCase_          =  requests.get(_SCREAMING_SNAKE_CASE		).content
                     else:
                            with open(_SCREAMING_SNAKE_CASE					,   "rb"		) as f:
                                   UpperCamelCase_          =  f.read()

              if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  ffmpeg_read(_SCREAMING_SNAKE_CASE					,   self.feature_extractor.sampling_rate		)

              if not isinstance(_SCREAMING_SNAKE_CASE					,   np.ndarray		):
                     raise ValueError("We expect a numpy ndarray as input"		)
              if len(audio.shape		) != 1:
                     raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline"		)

              UpperCamelCase_          =  self.feature_extractor(
                  [audio]					,   sampling_rate=self.feature_extractor.sampling_rate					,   return_tensors="pt"		)
              UpperCamelCase_          =  candidate_labels
              UpperCamelCase_          =  [hypothesis_template.format(_SCREAMING_SNAKE_CASE		) for x in candidate_labels]
              UpperCamelCase_          =  self.tokenizer(_SCREAMING_SNAKE_CASE					,   return_tensors=self.framework					,   padding=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  [text_inputs]
              return inputs

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  model_inputs.pop("candidate_labels"		)
              UpperCamelCase_          =  model_inputs.pop("text_inputs"		)
              if isinstance(text_inputs[0]					,   _SCREAMING_SNAKE_CASE		):
                     UpperCamelCase_          =  text_inputs[0]
              else:
                     # Batching case.
                     UpperCamelCase_          =  text_inputs[0][0]

              UpperCamelCase_          =  self.model(**_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  {
                  "candidate_labels": candidate_labels,
                  "logits": outputs.logits_per_audio,
              }
              return model_outputs



       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: Any		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  model_outputs.pop("candidate_labels"		)
              UpperCamelCase_          =  model_outputs["logits"][0]

              if self.framework == "pt":
                     UpperCamelCase_          =  logits.softmax(dim=0		)
                     UpperCamelCase_          =  probs.tolist()
              else:
                     raise ValueError("`tf` framework not supported."		)

              UpperCamelCase_          =  [
                  {"score": score, "label": candidate_label}
                  for score, candidate_label in sorted(zip(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   key=lambda _SCREAMING_SNAKE_CASE		: -x[0]		)
              ]
              return result








import argparse
import os
import re


_UpperCAmelCase        =				'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase        =				re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_UpperCAmelCase        =				re.compile(r'\s*\(\s*"(\S[^"]+)"')




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = False  )     ->		Union[str, Any]:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       UpperCamelCase_          =  content.split("\n"  )
       UpperCamelCase_          =  []
       UpperCamelCase_          =  0
       while line_idx < len(UpperCamelCase_  ):
              if _re_intro_mapping.search(lines[line_idx]  ) is not None:
                     UpperCamelCase_          =  len(re.search(r"^(\s*)\S"					,		lines[line_idx]  ).groups()[0]  ) + 8
                     # Start of a new mapping!
                     while not lines[line_idx].startswith(" " * indent + "("  ):
                            new_lines.append(lines[line_idx]  )
                            line_idx += 1

                     UpperCamelCase_          =  []
                     while lines[line_idx].strip() != "]":
                            # Blocks either fit in one line or not
                            if lines[line_idx].strip() == "(":
                                   UpperCamelCase_          =  line_idx
                                   while not lines[line_idx].startswith(" " * indent + ")"  ):
                                          line_idx += 1
                                   blocks.append("\n".join(lines[start_idx : line_idx + 1]  )  )
                            else:
                                   blocks.append(lines[line_idx]  )
                            line_idx += 1

                     # Sort blocks by their identifiers
                     UpperCamelCase_          =  sorted(UpperCamelCase_					,		key=lambda UpperCamelCase_  : _re_identifier.search(UpperCamelCase_  ).groups()[0]  )
                     new_lines += blocks
              else:
                     new_lines.append(lines[line_idx]  )
                     line_idx += 1

       if overwrite:
              with open(UpperCamelCase_					,		"w"					,		encoding="utf-8"  ) as f:
                     f.write("\n".join(UpperCamelCase_  )  )
       elif "\n".join(UpperCamelCase_  ) != content:
              return True




def  lowerCAmelCase_ (							UpperCamelCase_ = False  )     ->		List[Any]:
       UpperCamelCase_          =  [os.path.join(UpperCamelCase_					,		UpperCamelCase_  ) for f in os.listdir(UpperCamelCase_  ) if f.endswith(".py"  )]
       UpperCamelCase_          =  [sort_auto_mapping(UpperCamelCase_					,		overwrite=UpperCamelCase_  ) for fname in fnames]

       if not overwrite and any(UpperCamelCase_  ):
              UpperCamelCase_          =  [f for f, d in zip(UpperCamelCase_					,		UpperCamelCase_  ) if d]
              raise ValueError(
                  F'''The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_  )}. Run `make style` to fix'''
                  " this."  )


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
     _UpperCAmelCase        =				parser.parse_args()

     sort_all_auto_mappings(not args.check_only)

