





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

snake_case 				=       {
    """post_extract_proj""": """feature_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.upsample.0""": """encoder.upsample.projection""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """layer_norm""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]			,							snake_case__  :Optional[int]			,							snake_case__  :Union[str, Any]			,							snake_case__  :int			,							snake_case__  :int     )      ->						int:
  for attribute in key.split('.'     ):
    _lowercase							= getattr(snake_case__			,							snake_case__     )

  if weight_type is not None:
    _lowercase							= getattr(snake_case__			,							snake_case__     ).shape
  else:
    _lowercase							= hf_pointer.shape

  assert hf_shape == value.shape, (
      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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Union[str, Any]			,							snake_case__  :List[Any]     )      ->						Tuple:
  _lowercase							= []
  _lowercase							= fairseq_model.state_dict()

  _lowercase							= hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _lowercase							= False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__			,							snake_case__			,							snake_case__			,							snake_case__			,							hf_model.config.feat_extract_norm == 'group'			,							)
      _lowercase							= True
    else:
      for key, mapped_key in MAPPING.items():
        _lowercase							= 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

        if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
          _lowercase							= True
          if "*" in mapped_key:
            _lowercase							= name.split(snake_case__     )[0].split('.'     )[-2]
            _lowercase							= mapped_key.replace('*'			,							snake_case__     )
          if "weight_g" in name:
            _lowercase							= 'weight_g'
          elif "weight_v" in name:
            _lowercase							= 'weight_v'
          elif "weight" in name:
            _lowercase							= 'weight'
          elif "bias" in name:
            _lowercase							= 'bias'
          else:
            _lowercase							= None
          set_recursively(snake_case__			,							snake_case__			,							snake_case__			,							snake_case__			,							snake_case__     )
        continue
    if not is_used:
      unused_weights.append(snake_case__     )

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[Any]			,							snake_case__  :int			,							snake_case__  :Optional[Any]			,							snake_case__  :Optional[Any]			,							snake_case__  :Any     )      ->						Union[str, Any]:
  _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:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          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:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          F"""{full_name} has size {value.shape}, but {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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          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(snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :str			,							snake_case__  :Optional[int]     )      ->						Dict:
  _lowercase							= SEWConfig()
  if is_finetuned:
    _lowercase							= model.wav_encoder.wav_model.cfg
  else:
    _lowercase							= model.cfg

  _lowercase							= fs_config.conv_bias
  _lowercase							= eval(fs_config.conv_feature_layers     )
  _lowercase							= [x[0] for x in conv_layers]
  _lowercase							= [x[1] for x in conv_layers]
  _lowercase							= [x[2] for x in conv_layers]
  _lowercase							= 'gelu'
  _lowercase							= 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  _lowercase							= 0.0
  _lowercase							= fs_config.activation_fn.name
  _lowercase							= fs_config.encoder_embed_dim
  _lowercase							= 0.02
  _lowercase							= fs_config.encoder_ffn_embed_dim
  _lowercase							= 1E-5
  _lowercase							= fs_config.encoder_layerdrop
  _lowercase							= fs_config.encoder_attention_heads
  _lowercase							= fs_config.conv_pos_groups
  _lowercase							= fs_config.conv_pos
  _lowercase							= len(snake_case__     )
  _lowercase							= fs_config.encoder_layers
  _lowercase							= fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    _lowercase							= model.cfg
    _lowercase							= fs_config.final_dropout
    _lowercase							= fs_config.layerdrop
  _lowercase							= fs_config.activation_dropout
  _lowercase							= fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  _lowercase							= fs_config.attention_dropout
  _lowercase							= fs_config.dropout_input
  _lowercase							= fs_config.dropout
  _lowercase							= fs_config.mask_channel_length
  _lowercase							= fs_config.mask_channel_prob
  _lowercase							= fs_config.mask_length
  _lowercase							= fs_config.mask_prob

  _lowercase							= 'Wav2Vec2FeatureExtractor'
  _lowercase							= 'Wav2Vec2CTCTokenizer'

  return config

@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Optional[Any]			,							snake_case__  :str=None			,							snake_case__  :str=None			,							snake_case__  :Any=True     )      ->						List[str]:

  if is_finetuned:
    _lowercase						,		_lowercase						,		_lowercase							= fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]			,							arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
  else:
    _lowercase						,		_lowercase						,		_lowercase							= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

  if config_path is not None:
    _lowercase							= SEWConfig.from_pretrained(snake_case__     )
  else:
    _lowercase							= convert_config(model[0]			,							snake_case__     )
  _lowercase							= model[0].eval()

  _lowercase							= True if config.feat_extract_norm == 'layer' else False
  _lowercase							= WavaVecaFeatureExtractor(
      feature_size=1			,							sampling_rate=1_6000			,							padding_value=0			,							do_normalize=snake_case__			,							return_attention_mask=snake_case__			,							)

  if is_finetuned:
    if dict_path:
      _lowercase							= Dictionary.load(snake_case__     )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowercase							= target_dict.pad_index
      _lowercase							= target_dict.bos_index
      _lowercase							= target_dict.pad_index
      _lowercase							= target_dict.bos_index
      _lowercase							= target_dict.eos_index
      _lowercase							= len(target_dict.symbols     )
      _lowercase							= os.path.join(snake_case__			,							'vocab.json'     )
      if not os.path.isdir(snake_case__     ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case__     )     )
        return
      os.makedirs(snake_case__			,							exist_ok=snake_case__     )
      with open(snake_case__			,							'w'			,							encoding='utf-8'     ) as vocab_handle:
        json.dump(target_dict.indices			,							snake_case__     )
      _lowercase							= WavaVecaCTCTokenizer(
          snake_case__			,							unk_token=target_dict.unk_word			,							pad_token=target_dict.pad_word			,							bos_token=target_dict.bos_word			,							eos_token=target_dict.eos_word			,							word_delimiter_token='|'			,							do_lower_case=snake_case__			,							)
      _lowercase							= WavaVecaProcessor(feature_extractor=snake_case__			,							tokenizer=snake_case__     )
      processor.save_pretrained(snake_case__     )

    _lowercase							= SEWForCTC(snake_case__     )
  else:
    _lowercase							= SEWModel(snake_case__     )
    feature_extractor.save_pretrained(snake_case__     )

  recursively_load_weights(snake_case__			,							snake_case__			,							snake_case__     )

  hf_model.save_pretrained(snake_case__     )


if __name__ == "__main__":
      snake_case 				=       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(
          """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      snake_case 				=       parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : int      =  (DEISMultistepScheduler,)
       SCREAMING_SNAKE_CASE_      : List[Any]      =  (('''num_inference_steps''', 2_5),)







       def    __UpperCAmelCase (    self	:	List[Any]						,**__A	:	int  )						->					List[Any]:
         _lowercase							= {
             'num_train_timesteps': 1000,
             'beta_start': 0.0001,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'solver_order': 2,
         }

         config.update(**__A  )
         return config







       def    __UpperCAmelCase (    self	:	Dict						,__A	:	str=0						,**__A	:	Optional[int]  )						->					Tuple:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )
           # copy over dummy past residuals
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= scheduler_class.from_pretrained(__A  )
             new_scheduler.set_timesteps(__A  )
             # copy over dummy past residuals
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase						,		_lowercase							= sample, sample
           for t in range(__A						,time_step + scheduler.config.solver_order + 1  ):
             _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
             _lowercase							= new_scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

             assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         pass







       def    __UpperCAmelCase (    self	:	int						,__A	:	Dict=0						,**__A	:	Optional[Any]  )						->					Dict:
         _lowercase							= dict(self.forward_default_kwargs  )
         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )
         _lowercase							= self.dummy_sample
         _lowercase							= 0.1 * sample
         _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )
           scheduler.set_timesteps(__A  )

           # copy over dummy past residuals (must be after setting timesteps)
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(__A  )
             _lowercase							= scheduler_class.from_pretrained(__A  )
             # copy over dummy past residuals
             new_scheduler.set_timesteps(__A  )

             # copy over dummy past residual (must be after setting timesteps)
             _lowercase							= dummy_past_residuals[: new_scheduler.config.solver_order]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= new_scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

           assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Tuple=None						,**__A	:	Optional[Any]  )						->					Tuple:
         if scheduler is None:
           _lowercase							= self.scheduler_classes[0]
           _lowercase							= self.get_scheduler_config(**__A  )
           _lowercase							= scheduler_class(**__A  )

         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(**__A  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter
         scheduler.set_timesteps(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= model(__A						,__A  )
           _lowercase							= scheduler.step(__A						,__A						,__A  ).prev_sample

         return sample







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= dict(self.forward_default_kwargs  )

         _lowercase							= kwargs.pop('num_inference_steps'						,__A  )

         for scheduler_class in self.scheduler_classes:
           _lowercase							= self.get_scheduler_config()
           _lowercase							= scheduler_class(**__A  )

           _lowercase							= self.dummy_sample
           _lowercase							= 0.1 * sample

           if num_inference_steps is not None and hasattr(__A						,'set_timesteps'  ):
             scheduler.set_timesteps(__A  )
           elif num_inference_steps is not None and not hasattr(__A						,'set_timesteps'  ):
             _lowercase							= num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           _lowercase							= [residual + 0.2, residual + 0.15, residual + 0.10]
           _lowercase							= dummy_past_residuals[: scheduler.config.solver_order]

           _lowercase							= scheduler.timesteps[5]
           _lowercase							= scheduler.timesteps[6]

           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample
           _lowercase							= scheduler.step(__A						,__A						,__A						,**__A  ).prev_sample

           self.assertEqual(output_a.shape						,sample.shape  )
           self.assertEqual(output_a.shape						,output_a.shape  )







       def    __UpperCAmelCase (    self	:	int  )						->					Optional[Any]:
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         _lowercase							= DEISMultistepScheduler(**self.get_scheduler_config()  )
         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3

         _lowercase							= DPMSolverSinglestepScheduler.from_config(scheduler.config  )
         _lowercase							= DPMSolverMultistepScheduler.from_config(scheduler.config  )
         _lowercase							= UniPCMultistepScheduler.from_config(scheduler.config  )
         _lowercase							= DEISMultistepScheduler.from_config(scheduler.config  )

         _lowercase							= self.full_loop(scheduler=__A  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[int]:
         for timesteps in [25, 50, 100, 999, 1000]:
           self.check_over_configs(num_train_timesteps=__A  )







       def    __UpperCAmelCase (    self	:	int  )						->					str:
         self.check_over_configs(thresholding=__A  )
         for order in [1, 2, 3]:
           for solver_type in ["logrho"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=__A						,prediction_type=__A						,sample_max_value=__A						,algorithm_type='deis'						,solver_order=__A						,solver_type=__A						,)







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Tuple:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__A  )







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[int]:
         for algorithm_type in ["deis"]:
           for solver_type in ["logrho"]:
             for order in [1, 2, 3]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 _lowercase							= self.full_loop(
                     solver_order=__A						,solver_type=__A						,prediction_type=__A						,algorithm_type=__A						,)
                 assert not torch.isnan(__A  ).any(), "Samples have nan numbers"







       def    __UpperCAmelCase (    self	:	Dict  )						->					int:
         self.check_over_configs(lower_order_final=__A  )
         self.check_over_configs(lower_order_final=__A  )







       def    __UpperCAmelCase (    self	:	str  )						->					Tuple:
         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
           self.check_over_forward(num_inference_steps=__A						,time_step=0  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Any:
         _lowercase							= self.full_loop()
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.23916  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.full_loop(prediction_type='v_prediction'  )
         _lowercase							= torch.mean(torch.abs(__A  )  )

         assert abs(result_mean.item() - 0.091  ) < 1e-3







       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					List[Any]:
         _lowercase							= self.scheduler_classes[0]
         _lowercase							= self.get_scheduler_config(thresholding=__A						,dynamic_thresholding_ratio=0  )
         _lowercase							= scheduler_class(**__A  )

         _lowercase							= 10
         _lowercase							= self.dummy_model()
         _lowercase							= self.dummy_sample_deter.half()
         scheduler.set_timesteps(__A  )

         for i, t in enumerate(scheduler.timesteps  ):
           _lowercase							= model(__A						,__A  )
           _lowercase							= scheduler.step(__A						,__A						,__A  ).prev_sample

         assert sample.dtype == torch.floataa