

'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCAmelCase__											=	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowerCAmelCase__											=	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 for attribute in key.split('''.'''      ):
  __lowercase							  = getattr(A__     ,				A__      )

 if weight_type is not None:
  __lowercase							  = getattr(A__     ,				A__      ).shape
 else:
  __lowercase							  = hf_pointer.shape

 if hf_shape != value.shape:
  raise ValueError(
      F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
      F" {value.shape} for {full_name}"      )

 if weight_type == "weight":
  __lowercase							  = value
 elif weight_type == "weight_g":
  __lowercase							  = value
 elif weight_type == "weight_v":
  __lowercase							  = value
 elif weight_type == "bias":
  __lowercase							  = value
 else:
  __lowercase							  = value

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."      )


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = []
 __lowercase							  = fairseq_model.state_dict()

 __lowercase							  = hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  __lowercase							  = False
  if "conv_layers" in name:
   load_conv_layer(
       A__     ,				A__     ,				A__     ,				A__     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
   __lowercase							  = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowercase							  = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split('''w2v_model.'''      )[-1] == name.split('''.'''      )[0]:
     if "layer_norm_for_extract" in name and (".".join(name.split('''.'''      )[:-1]      ) != key):
      # special case since naming is very similar
      continue
     __lowercase							  = True
     if "*" in mapped_key:
      __lowercase							  = name.split(A__      )[0].split('''.'''      )[-2]
      __lowercase							  = mapped_key.replace('''*'''     ,				A__      )
     if "weight_g" in name:
      __lowercase							  = '''weight_g'''
     elif "weight_v" in name:
      __lowercase							  = '''weight_v'''
     elif "bias" in name:
      __lowercase							  = '''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __lowercase							  = '''weight'''
     else:
      __lowercase							  = None
     set_recursively(A__     ,				A__     ,				A__     ,				A__     ,				A__      )
    continue
  if not is_used:
   unused_weights.append(A__      )

 logger.warning(F"Unused weights: {unused_weights}"      )


def 		_A     (    A__     ,				A__     ,				A__     ,				A__     ,				A__      ):






 """simple docstring"""



 __lowercase							  = full_name.split('''conv_layers.'''      )[-1]
 __lowercase							  = name.split('''.'''      )
 __lowercase							  = int(items[0]      )
 __lowercase							  = int(items[1]      )

 if type_id == 0:
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."      )
 elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
  if "bias" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
  elif "weight" in name:
   if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
    raise ValueError(
        F"{full_name} has size {value.shape}, but"
        F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."      )
   __lowercase							  = value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
 else:
  unused_weights.append(A__      )


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__=None     ,				A__=None     ,				A__=True      ):






 """simple docstring"""



 if config_path is not None:
  __lowercase							  = UniSpeechSatConfig.from_pretrained(A__      )
 else:
  __lowercase							  = UniSpeechSatConfig()

 __lowercase							  = ''''''

 if is_finetuned:
  __lowercase							  = UniSpeechSatForCTC(A__      )
 else:
  __lowercase							  = UniSpeechSatForPreTraining(A__      )

 __lowercase  ,	__lowercase  ,	__lowercase							  = fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]     ,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      )}      )
 __lowercase							  = model[0].eval()

 recursively_load_weights(A__     ,				A__      )

 hf_wavavec.save_pretrained(A__      )


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
 parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 parser.add_argument(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 lowerCAmelCase__											=	parser.parse_args()
 convert_unispeech_sat_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   logging.get_logger()

         # the current default level is logging.WARNING
         __snake_case							       =   logging.get_verbosity()

         logging.set_verbosity_error()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_warning()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_info()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         logging.set_verbosity_debug()
         self.assertEqual(logger.getEffectiveLevel()				,			logging.get_verbosity()      )

         # restore to the original level
         logging.set_verbosity(a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   logging.get_verbosity()

         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         __snake_case							       =   '''Testing 1, 2, 3'''

         # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
         if level_origin <= logging.WARNING:
            with CaptureLogger(a__      ) as cl:
               logger.warning(a__      )
            self.assertEqual(cl.out				,			msg + '''\n'''      )

         # this is setting the level for all of `transformers.*` loggers
         logging.set_verbosity_error()

         # should not be able to log warnings
         with CaptureLogger(a__      ) as cl:
            logger.warning(a__      )
         self.assertEqual(cl.out				,			''''''      )

         # should be able to log warnings again
         logging.set_verbosity_warning()
         with CaptureLogger(a__      ) as cl:
            logger.warning(a__      )
         self.assertEqual(cl.out				,			msg + '''\n'''      )

         # restore to the original level
         logging.set_verbosity(a__      )


      @mockenv(TRANSFORMERS_VERBOSITY='''error'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()
         # this action activates the env var
         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )

         __snake_case							       =   os.getenv('''TRANSFORMERS_VERBOSITY'''				,			a__      )
         __snake_case							       =   logging.log_levels[env_level_str]

         __snake_case							       =   logging.get_verbosity()
         self.assertEqual(
             a__				,			a__				,			f"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"""				,			)

         # restore to the original level
         __snake_case							       =   ''''''
         transformers.utils.logging._reset_library_root_logger()


      @mockenv(TRANSFORMERS_VERBOSITY='''super-error'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()
         __snake_case							       =   logging.logging.getLogger()
         with CaptureLogger(a__      ) as cl:
            # this action activates the env var
            logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error'''				,			cl.out      )

         # no need to restore as nothing was changed


      def    a (self       :      Any      ):





         """simple docstring"""



         transformers.utils.logging._reset_library_root_logger()

         __snake_case							       =   logging.get_logger('''transformers.models.bart.tokenization_bart'''      )
         __snake_case							       =   '''Testing 1, 2, 3'''

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1'''      ):
            # nothing should be logged as env var disables this method
            with CaptureLogger(a__      ) as cl:
               logger.warning_advice(a__      )
            self.assertEqual(cl.out				,			''''''      )

         with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''''''      ):
            # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
            with CaptureLogger(a__      ) as cl:
               logger.warning_advice(a__      )
            self.assertEqual(cl.out				,			msg + '''\n'''      )






def lowerCamelCase__    (							)      ->				str:
   disable_progress_bar()
   assert are_progress_bars_disabled()

   enable_progress_bar()
   assert not are_progress_bars_disabled()

