





'''simple docstring'''


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
a__			:					Dict       						=						'src/transformers'
a__			:					Tuple       						=						'docs/source/en'
a__			:					str       						=						'.'
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any]			,	SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			List[str] )							->     str:

       """simple docstring"""


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

       UpperCAmelCase  =						start_index
       while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_ ):
              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 |
a__			:					List[Any]       						=						'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
a__			:					Tuple       						=						re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
a__			:					Optional[int]       						=						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.
a__			:					Union[str, Any]       						=						re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
a__			:					Optional[Any]       						=						direct_transformers_import(TRANSFORMERS_PATH)
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Tuple )							->     str:

       """simple docstring"""


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

       """simple docstring"""


       UpperCAmelCase  =						2 if text == '''✅''' or text == '''❌''' else len(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						(width - text_length) // 2
       UpperCAmelCase  =						width - text_length - left_indent
       return " " * left_indent + text + " " * right_indent
def       __snake_case		( )							->     Optional[int]:

       """simple docstring"""


       UpperCAmelCase  =						transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       UpperCAmelCase  =						{
           name: config_maping_names[code]
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if code in config_maping_names
       }
       UpperCAmelCase  =						{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.
       UpperCAmelCase  =						collections.defaultdict(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						collections.defaultdict(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						collections.defaultdict(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						collections.defaultdict(SCREAMING_SNAKE_CASE_ )
       UpperCAmelCase  =						collections.defaultdict(SCREAMING_SNAKE_CASE_ )

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

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

    # Let's build that table!
       UpperCAmelCase  =						list(model_name_to_config.keys() )
       model_names.sort(key=str.lower )
       UpperCAmelCase  =						['''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).
       UpperCAmelCase  =						[len(SCREAMING_SNAKE_CASE_ ) + 2 for c in columns]
       UpperCAmelCase  =						max([len(SCREAMING_SNAKE_CASE_ ) for name in model_names] ) + 2

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

       UpperCAmelCase  =						{True: '''✅''', False: '''❌'''}
       for name in model_names:
              UpperCAmelCase  =						model_name_to_prefix[name]
              UpperCAmelCase  =						[
                  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(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ ) for l, w in zip(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )] ) + "|\n"
       return table
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Dict=False )							->     Any:

       """simple docstring"""


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

       if current_table != new_table:
              if overwrite:
                     with open(os.path.join(SCREAMING_SNAKE_CASE_			,	'''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__":
    a__			:					Tuple       						=						argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    a__			:					Optional[int]       						=						parser.parse_args()

    check_model_table(args.fix_and_overwrite)










'''simple docstring'''
import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__			=      logging.get_logger(__name__)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
 UpperCamelCase__			=      argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 UpperCamelCase__			=      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




