

import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowercase_   							=  'src/transformers'
lowercase_   							=  'docs/source/en'
lowercase_   							=  '.'
def 	a (     A__      :  Any ,  A__      :  Union[str, Any] ,  A__      :  Optional[int]	)       -> str:





      """simple docstring"""



      with open(A__ ,  'r' ,  encoding='utf-8' ,  newline='\n'	) as f:
            _lowercase      			=f.readlines()
      # Find the start prompt.
      _lowercase      			=0
      while not lines[start_index].startswith(A__	):
            start_index += 1
      start_index += 1

      _lowercase      			=start_index
      while not lines[end_index].startswith(A__	):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]	) <= 1:
            start_index += 1
      while len(lines[end_index]	) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]	), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowercase_   							=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
lowercase_   							=  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowercase_   							=  re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase_   							=  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
lowercase_   							=  direct_transformers_import(TRANSFORMERS_PATH)
def 	a (     A__      :  str	)       -> List[Any]:





      """simple docstring"""



      _lowercase      			=re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,  A__	)
      return [m.group(0	) for m in matches]
def 	a (     A__      :  Tuple ,  A__      :  Union[str, Any]	)       -> Tuple:





      """simple docstring"""



      _lowercase      			=2 if text == '✅' or text == '❌' else len(A__	)
      _lowercase      			=(width - text_length) // 2
      _lowercase      			=width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent
def 	a (     )       -> Tuple:





      """simple docstring"""



      _lowercase      			=transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _lowercase      			={
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      _lowercase      			={name: config.replace('Config' ,  ''	) for name, config in model_name_to_config.items()}

      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      _lowercase      			=collections.defaultdict(A__	)
      _lowercase      			=collections.defaultdict(A__	)
      _lowercase      			=collections.defaultdict(A__	)
      _lowercase      			=collections.defaultdict(A__	)
      _lowercase      			=collections.defaultdict(A__	)

      # Let's lookup through all transformers object (once).
      for attr_name in dir(A__	):
            _lowercase      			=None
            if attr_name.endswith('Tokenizer'	):
                  _lowercase      			=slow_tokenizers
                  _lowercase      			=attr_name[:-9]
            elif attr_name.endswith('TokenizerFast'	):
                  _lowercase      			=fast_tokenizers
                  _lowercase      			=attr_name[:-13]
            elif _re_tf_models.match(A__	) is not None:
                  _lowercase      			=tf_models
                  _lowercase      			=_re_tf_models.match(A__	).groups()[0]
            elif _re_flax_models.match(A__	) is not None:
                  _lowercase      			=flax_models
                  _lowercase      			=_re_flax_models.match(A__	).groups()[0]
            elif _re_pt_models.match(A__	) is not None:
                  _lowercase      			=pt_models
                  _lowercase      			=_re_pt_models.match(A__	).groups()[0]

            if lookup_dict is not None:
                  while len(A__	) > 0:
                        if attr_name in model_name_to_prefix.values():
                              _lowercase      			=True
                              break
                        # Try again after removing the last word in the name
                        _lowercase      			=''.join(camel_case_split(A__	)[:-1]	)

    # Let's build that table!
      _lowercase      			=list(model_name_to_config.keys()	)
      model_names.sort(key=str.lower	)
      _lowercase      			=['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      _lowercase      			=[len(A__	) + 2 for c in columns]
      _lowercase      			=max([len(A__	) for name in model_names]	) + 2

      # Build the table per se
      _lowercase      			='|' + '|'.join([_center_text(A__ ,  A__	) for c, w in zip(A__ ,  A__	)]	) + '|\n'
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]	) + "|\n"

      _lowercase      			={True: '✅', False: '❌'}
      for name in model_names:
            _lowercase      			=model_name_to_prefix[name]
            _lowercase      			=[
                name,
                check[slow_tokenizers[prefix]],
                check[fast_tokenizers[prefix]],
                check[pt_models[prefix]],
                check[tf_models[prefix]],
                check[flax_models[prefix]],
            ]
            table += "|" + "|".join([_center_text(A__ ,  A__	) for l, w in zip(A__ ,  A__	)]	) + "|\n"
      return table
def 	a (     A__      :  int=False	)       -> Dict:





      """simple docstring"""



      _lowercase			,			_lowercase			,			_lowercase			,			_lowercase      			=_find_text_in_file(
          filename=os.path.join(A__ ,  'index.md'	) ,  start_prompt='<!--This table is updated automatically from the auto modules' ,  end_prompt='<!-- End table-->' ,  )
      _lowercase      			=get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(A__ ,  'index.md'	) ,  'w' ,  encoding='utf-8' ,  newline='\n'	) as f:
                        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]	)
            else:
                  raise ValueError(
                      'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'	)


if __name__ == "__main__":
  lowercase_   							=  argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  lowercase_   							=  parser.parse_args()

  check_model_table(args.fix_and_overwrite)





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


lowercase_   							=  logging.get_logger(__name__)





@add_end_docstrings(SCREAMING_SNAKE_CASE				)
class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  **lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

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




  def __call__(							self				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					int:
        '''simple docstring'''






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




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






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

        return preprocess_params, {}, {}




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase="This is a sound of {}."   )    ->					Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              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
                    _lowercase      			=requests.get(lowerCAmelCase   ).content
              else:
                    with open(lowerCAmelCase				,  'rb'   ) as f:
                          _lowercase      			=f.read()

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              _lowercase      			=ffmpeg_read(lowerCAmelCase				,  self.feature_extractor.sampling_rate   )

        if not isinstance(lowerCAmelCase				,  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'   )

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




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=model_inputs.pop('candidate_labels'   )
        _lowercase      			=model_inputs.pop('text_inputs'   )
        if isinstance(text_inputs[0]				,  lowerCAmelCase   ):
              _lowercase      			=text_inputs[0]
        else:
              # Batching case.
              _lowercase      			=text_inputs[0][0]

        _lowercase      			=self.model(**lowerCAmelCase				,  **lowerCAmelCase   )

        _lowercase      			={
            'candidate_labels': candidate_labels,
            'logits': outputs.logits_per_audio,
        }
        return model_outputs




  def     A__	(							self				,  lowerCAmelCase   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=model_outputs.pop('candidate_labels'   )
        _lowercase      			=model_outputs['logits'][0]

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

        _lowercase      			=[
            {'score': score, 'label': candidate_label}
            for score, candidate_label in sorted(zip(lowerCAmelCase				,  lowerCAmelCase   )				,  key=lambda lowerCAmelCase   : -x[0]   )
        ]
        return result


