



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_      			=  {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  ["DeformableDetrFeatureExtractor"]
							UpperCamelCase_      			=  ["DeformableDetrImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_      			=  [
							    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DeformableDetrForObjectDetection",
							    "DeformableDetrModel",
							    "DeformableDetrPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
														from .image_processing_deformable_detr import DeformableDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deformable_detr import (
														    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DeformableDetrForObjectDetection,
														    DeformableDetrModel,
														    DeformableDetrPreTrainedModel,
														)

else:
							import sys

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








import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_a									=  {
    '''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 _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 if weight_type is not None:
  __lowerCAmelCase:  Dict        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						).shape
 else:
  __lowerCAmelCase:  str        = 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":
  __lowerCAmelCase:  str        = value
 elif weight_type == "weight_g":
  __lowerCAmelCase:  List[str]        = value
 elif weight_type == "weight_v":
  __lowerCAmelCase:  List[Any]        = value
 elif weight_type == "bias":
  __lowerCAmelCase:  Optional[int]        = value
 else:
  __lowerCAmelCase:  int        = value

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







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

    if key in name:
     __lowerCAmelCase:  List[Any]        = True
     if "*" in mapped_key:
      __lowerCAmelCase:  Any        = name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
      __lowerCAmelCase:  Dict        = mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
     if "weight_g" in name:
      __lowerCAmelCase:  str        = 'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:  Tuple        = 'weight_v'
     elif "weight" in name:
      __lowerCAmelCase:  Tuple        = 'weight'
     elif "bias" in name:
      __lowerCAmelCase:  Optional[Any]        = 'bias'
     else:
      __lowerCAmelCase:  Optional[Any]        = None
     set_recursively(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE						)

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







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = full_name.split('conv_layers.'						)[-1]
 __lowerCAmelCase:  Optional[Any]        = name.split('.'						)
 __lowerCAmelCase:  List[str]        = int(items[0]						)
 __lowerCAmelCase:  str        = 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.'''
   )
   __lowerCAmelCase:  Optional[int]        = 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.'''
   )
   __lowerCAmelCase:  Tuple        = 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."
   )
   __lowerCAmelCase:  List[Any]        = 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.'''
   )
   __lowerCAmelCase:  Any        = value
   logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
 else:
  unused_weights.append(SCREAMING_SNAKE_CASE						)







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

 __lowerCAmelCase:  str        = fs_config.activation_dropout
 __lowerCAmelCase:  Optional[Any]        = False
 __lowerCAmelCase:  Tuple        = fs_config.attention_dropout
 __lowerCAmelCase:  Tuple        = False
 __lowerCAmelCase:  Tuple        = eval(fs_config.extractor_conv_feature_layers						)
 __lowerCAmelCase:  Optional[int]        = [x[0] for x in conv_layers]
 __lowerCAmelCase:  Dict        = [x[1] for x in conv_layers]
 __lowerCAmelCase:  List[str]        = [x[2] for x in conv_layers]
 __lowerCAmelCase:  Dict        = 'gelu'
 __lowerCAmelCase:  List[str]        = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCAmelCase:  int        = False
 __lowerCAmelCase:  Dict        = 0.0
 __lowerCAmelCase:  str        = 0.0
 __lowerCAmelCase:  List[Any]        = fs_config.activation_fn
 __lowerCAmelCase:  str        = fs_config.dropout
 __lowerCAmelCase:  List[str]        = fs_config.encoder_embed_dim
 __lowerCAmelCase:  str        = 0.0_2
 __lowerCAmelCase:  List[Any]        = fs_config.encoder_ffn_embed_dim
 __lowerCAmelCase:  List[str]        = 1E-5
 __lowerCAmelCase:  Optional[Any]        = 0.0
 __lowerCAmelCase:  int        = fs_config.encoder_attention_heads
 __lowerCAmelCase:  Any        = fs_config.conv_pos_groups
 __lowerCAmelCase:  str        = fs_config.conv_pos
 __lowerCAmelCase:  Optional[int]        = len(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = fs_config.encoder_layers

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a									=  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''')
  _a									=  parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





