import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE   		=			{
    """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""",
    """mask_emb""": """masked_spec_embed""",
}





def  lowercase(  lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase					)		->  Union[str, Any]:







 '''simple docstring'''
 for attribute in key.split("""."""					):
  UpperCamelCase      =			getattr(UpperCamelCase_       ,				UpperCamelCase_					)

 if weight_type is not None:
  UpperCamelCase      =			getattr(UpperCamelCase_       ,				UpperCamelCase_					).shape
 else:
  UpperCamelCase      =			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":
  UpperCamelCase      =			value
 elif weight_type == "weight_g":
  UpperCamelCase      =			value
 elif weight_type == "weight_v":
  UpperCamelCase      =			value
 elif weight_type == "bias":
  UpperCamelCase      =			value
 else:
  UpperCamelCase      =			value

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





def  lowercase(  lowerCamelCase       ,				lowerCamelCase					)		->  int:







 '''simple docstring'''
 UpperCamelCase      =			[]
 UpperCamelCase      =			fairseq_model.state_dict()

 UpperCamelCase      =			hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase      =			False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				UpperCamelCase_       ,				hf_model.config.feat_extract_norm == """group"""       ,				)
   UpperCamelCase      =			True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase      =			mapped_key

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

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





def  lowercase(  lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase					)		->  List[str]:







 '''simple docstring'''
 UpperCamelCase      =			full_name.split("""conv_layers."""					)[-1]
 UpperCamelCase      =			name.split("""."""					)
 UpperCamelCase      =			int(items[0]					)
 UpperCamelCase      =			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."""
   )
   UpperCamelCase      =			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."""
   )
   UpperCamelCase      =			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."
   )
   UpperCamelCase      =			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."""
   )
   UpperCamelCase      =			value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
 else:
  unused_weights.append(UpperCamelCase_					)





def  lowercase(  lowerCamelCase					)		->  Optional[int]:







 '''simple docstring'''
 UpperCamelCase      =			HubertConfig()
 UpperCamelCase      =			model.config

 UpperCamelCase      =			fs_config.activation_dropout
 UpperCamelCase      =			False
 UpperCamelCase      =			fs_config.attention_dropout
 UpperCamelCase      =			False
 UpperCamelCase      =			eval(fs_config.extractor_conv_feature_layers					)
 UpperCamelCase      =			[x[0] for x in conv_layers]
 UpperCamelCase      =			[x[1] for x in conv_layers]
 UpperCamelCase      =			[x[2] for x in conv_layers]
 UpperCamelCase      =			"""gelu"""
 UpperCamelCase      =			"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
 UpperCamelCase      =			False
 UpperCamelCase      =			0.0
 UpperCamelCase      =			0.0
 UpperCamelCase      =			fs_config.activation_fn
 UpperCamelCase      =			fs_config.dropout
 UpperCamelCase      =			fs_config.encoder_embed_dim
 UpperCamelCase      =			0.0_2
 UpperCamelCase      =			fs_config.encoder_ffn_embed_dim
 UpperCamelCase      =			1E-5
 UpperCamelCase      =			0.0
 UpperCamelCase      =			fs_config.encoder_attention_heads
 UpperCamelCase      =			fs_config.conv_pos_groups
 UpperCamelCase      =			fs_config.conv_pos
 UpperCamelCase      =			len(UpperCamelCase_					)
 UpperCamelCase      =			fs_config.encoder_layers

 return config





@torch.no_grad()
def  lowercase(  lowerCamelCase       ,				lowerCamelCase=None					)		->  Tuple:







 '''simple docstring'''
 UpperCamelCase      =			distilhubert().model.model

 if config_path is not None:
  UpperCamelCase      =			HubertConfig.from_pretrained(UpperCamelCase_					)
 else:
  UpperCamelCase      =			convert_config(UpperCamelCase_					)
 UpperCamelCase      =			model.eval()

 UpperCamelCase      =			WavaVecaFeatureExtractor(
     feature_size=1       ,				sampling_rate=16000       ,				padding_value=0       ,				do_normalize=UpperCamelCase_       ,				return_attention_mask=UpperCamelCase_       ,				)
 UpperCamelCase      =			HubertModel(UpperCamelCase_					)

 recursively_load_weights(UpperCamelCase_       ,				UpperCamelCase_					)

 feature_extractor.save_pretrained(UpperCamelCase_					)
 hf_model.save_pretrained(UpperCamelCase_					)


if __name__ == "__main__":
     _SCREAMING_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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     _SCREAMING_SNAKE_CASE   		=			parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE   		=			{
    """configuration_align""": [
        """ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AlignConfig""",
        """AlignTextConfig""",
        """AlignVisionConfig""",
    ],
    """processing_align""": ["""AlignProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE   		=			[
         """ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AlignModel""",
         """AlignPreTrainedModel""",
         """AlignTextModel""",
         """AlignVisionModel""",
     ]

if TYPE_CHECKING:
     from .configuration_align import (
         ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AlignConfig,
         AlignTextConfig,
         AlignVisionConfig,
     )
     from .processing_align import AlignProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_align import (
              ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
              AlignModel,
              AlignPreTrainedModel,
              AlignTextModel,
              AlignVisionModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
