



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, 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""",
    """mask_emb""": """masked_spec_embed""",
}






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 for attribute in key.split("."  ):
  __SCREAMING_SNAKE_CASE							:     Any							=						getattr(__lowerCamelCase		,						__lowerCamelCase  )

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

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 __SCREAMING_SNAKE_CASE							:     str							=						fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __SCREAMING_SNAKE_CASE							:     str							=						False
  if "conv_layers" in name:
   load_conv_layer(
       __lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase		,						hf_model.config.feat_extract_norm == "group"		,						)
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						True
  else:
   for key, mapped_key in MAPPING.items():
    __SCREAMING_SNAKE_CASE							:     List[str]							=						mapped_key

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

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     int							=						full_name.split("conv_layers."  )[-1]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						name.split("."  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						int(items[0]  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						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."""
   )
   __SCREAMING_SNAKE_CASE							:     Dict							=						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."""
   )
   __SCREAMING_SNAKE_CASE							:     int							=						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."
   )
   __SCREAMING_SNAKE_CASE							:     Union[str, 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."""
   )
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
 else:
  unused_weights.append(__lowerCamelCase  )






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						HubertConfig()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model.config

 __SCREAMING_SNAKE_CASE							:     str							=						fs_config.activation_dropout
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						fs_config.attention_dropout
 __SCREAMING_SNAKE_CASE							:     List[str]							=						False
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						eval(fs_config.extractor_conv_feature_layers  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[x[0] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Any							=						[x[1] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Dict							=						[x[2] for x in conv_layers]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"gelu"
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"layer" if fs_config.extractor_mode == "layer_norm" else "group"
 __SCREAMING_SNAKE_CASE							:     Any							=						False
 __SCREAMING_SNAKE_CASE							:     Tuple							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						0.0
 __SCREAMING_SNAKE_CASE							:     Any							=						fs_config.activation_fn
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.dropout
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						fs_config.encoder_embed_dim
 __SCREAMING_SNAKE_CASE							:     int							=						0.02
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.encoder_ffn_embed_dim
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						1E-5
 __SCREAMING_SNAKE_CASE							:     Any							=						0.0
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_attention_heads
 __SCREAMING_SNAKE_CASE							:     Dict							=						fs_config.conv_pos_groups
 __SCREAMING_SNAKE_CASE							:     Tuple							=						fs_config.conv_pos
 __SCREAMING_SNAKE_CASE							:     Tuple							=						len(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						fs_config.encoder_layers

 return config






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[Any]=None  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						distilhubert().model.model

 if config_path is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertConfig.from_pretrained(__lowerCamelCase  )
 else:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						convert_config(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model.eval()

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavaVecaFeatureExtractor(
     feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=__lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						)
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertModel(__lowerCamelCase  )

 recursively_load_weights(__lowerCamelCase		,						__lowerCamelCase  )

 feature_extractor.save_pretrained(__lowerCamelCase  )
 hf_model.save_pretrained(__lowerCamelCase  )


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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      _lowerCamelCase         =	parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)











import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
      import numpy as np
      import torch

if is_vision_available():
      from PIL import Image






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  str=False		,						__lowerCamelCase :  Optional[Any]=False		,						__lowerCamelCase :  int=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     List[str]							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						image_processor_tester.min_resolution
   if getattr(__lowerCamelCase		,						"size_divisor"		,						__lowerCamelCase  ):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    __SCREAMING_SNAKE_CASE							:     str							=						max(image_processor_tester.size_divisor		,						__lowerCamelCase  )
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Tuple							=						np.random.choice(np.arange(__lowerCamelCase		,						image_processor_tester.max_resolution  )		,						2  )
  image_inputs.append(
      np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Dict							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for image in image_inputs]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     Dict							=						[torch.from_numpy(__lowerCamelCase  ) for image in image_inputs]

 return image_inputs






def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  List[Any]=10		,						__lowerCamelCase :  Any=10		,						__lowerCamelCase :  int=False		,						__lowerCamelCase :  List[str]=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 for i in range(image_processor_tester.num_frames  ):
  video.append(np.random.randint(255		,						size=(image_processor_tester.num_channels, width, height)		,						dtype=np.uinta  )  )

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  __SCREAMING_SNAKE_CASE							:     Optional[int]							=						[Image.fromarray(np.moveaxis(__lowerCamelCase		,						0		,						-1  )  ) for frame in video]

 if torchify:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						[torch.from_numpy(__lowerCamelCase  ) for frame in video]

 return video






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict=False		,						__lowerCamelCase :  Optional[int]=False		,						__lowerCamelCase :  Any=False  ):



 """simple docstring"""





 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 for i in range(image_processor_tester.batch_size  ):
  if equal_resolution:
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor_tester.max_resolution
  else:
   __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Dict							=						np.random.choice(
       np.arange(image_processor_tester.min_resolution		,						image_processor_tester.max_resolution  )		,						2  )
   __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						prepare_video(
       image_processor_tester=__lowerCamelCase		,						width=__lowerCamelCase		,						height=__lowerCamelCase		,						numpify=__lowerCamelCase		,						torchify=__lowerCamelCase		,						)
  video_inputs.append(__lowerCamelCase  )

 return video_inputs






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      None





     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
      __SCREAMING_SNAKE_CASE							:     str							=						json.loads(image_processor.to_json_string()					)
      for key, value in self.image_processor_dict.items():
       self.assertEqual(obj[key]  ,	UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Dict							=						os.path.join(UpperCamelCase  ,	"image_processor.json"					)
       image_processor_first.to_json_file(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class.from_json_file(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.image_processing_class(**self.image_processor_dict					)

      with tempfile.TemporaryDirectory() as tmpdirname:
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor_first.save_pretrained(UpperCamelCase					)[0]
       check_json_file_has_correct_format(UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.image_processing_class.from_pretrained(UpperCamelCase					)

      self.assertEqual(image_processor_second.to_dict()  ,	image_processor_first.to_dict()					)





     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.image_processing_class()
      self.assertIsNotNone(UpperCamelCase					)






     @require_torch
     @require_vision
     def 						__snake_case     (							self			:				Any					)->List[Any]:
      if self.test_cast_dtype is not None:
       # Initialize image_processor
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)

       # create random PyTorch tensors
       __SCREAMING_SNAKE_CASE							:     int							=						prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=UpperCamelCase  ,	torchify=UpperCamelCase					)

       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       # for layoutLM compatiblity
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.floataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)

       __SCREAMING_SNAKE_CASE							:     Dict							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to("cpu"  ,	torch.bfloataa					)
       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.bfloataa					)

       with self.assertRaises(UpperCamelCase					):
        __SCREAMING_SNAKE_CASE							:     List[Any]							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					).to(torch.bfloataa  ,	"cpu"					)

       # Try with text + image feature
       __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(UpperCamelCase  ,	return_tensors="pt"					)
       encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]					)}					)
       __SCREAMING_SNAKE_CASE							:     Optional[int]							=						encoding.to(torch.floataa					)

       self.assertEqual(encoding.pixel_values.device  ,	torch.device("cpu"					)					)
       self.assertEqual(encoding.pixel_values.dtype  ,	torch.floataa					)
       self.assertEqual(encoding.input_ids.dtype  ,	torch.long					)






