





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 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[Any]:







       for attribute in key.split('''.'''  ):
              _snake_case      :							Dict     				=						getattr(_UpperCamelCase      ,		_UpperCamelCase  )

       if weight_type is not None:
              _snake_case      :							List[str]     				=						getattr(_UpperCamelCase      ,		_UpperCamelCase  ).shape
       else:
              _snake_case      :							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":
              _snake_case      :							Tuple     				=						value
       elif weight_type == "weight_g":
              _snake_case      :							Optional[Any]     				=						value
       elif weight_type == "weight_v":
              _snake_case      :							Union[str, Any]     				=						value
       elif weight_type == "bias":
              _snake_case      :							List[Any]     				=						value
       else:
              _snake_case      :							Optional[Any]     				=						value

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




def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[Any]:
       _snake_case      :							List[Any]     				=						[]
       _snake_case      :							Tuple     				=						fairseq_model.state_dict()

       _snake_case      :							List[str]     				=						hf_model.feature_extractor

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

                            if key in name:
                                   _snake_case      :							Dict     				=						True
                                   if "*" in mapped_key:
                                          _snake_case      :							Dict     				=						name.split(_UpperCamelCase  )[0].split('''.'''  )[-2]
                                          _snake_case      :							int     				=						mapped_key.replace('''*'''      ,		_UpperCamelCase  )
                                   if "weight_g" in name:
                                          _snake_case      :							Union[str, Any]     				=						'''weight_g'''
                                   elif "weight_v" in name:
                                          _snake_case      :							Dict     				=						'''weight_v'''
                                   elif "weight" in name:
                                          _snake_case      :							List[str]     				=						'''weight'''
                                   elif "bias" in name:
                                          _snake_case      :							str     				=						'''bias'''
                                   else:
                                          _snake_case      :							List[Any]     				=						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 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						Any:
       _snake_case      :							str     				=						full_name.split('''conv_layers.'''  )[-1]
       _snake_case      :							Optional[int]     				=						name.split('''.'''  )
       _snake_case      :							Union[str, Any]     				=						int(items[0]  )
       _snake_case      :							Optional[int]     				=						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.'''
                     )
                     _snake_case      :							List[str]     				=						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.'''
                     )
                     _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."
                     )
                     _snake_case      :							List[str]     				=						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.'''
                     )
                     _snake_case      :							str     				=						value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
       else:
              unused_weights.append(_UpperCamelCase  )




def 						A_			(  lowercase_  )			->						Any:
       _snake_case      :							List[Any]     				=						HubertConfig()
       _snake_case      :							Optional[int]     				=						model.config

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

       return config




@torch.no_grad()
def 						A_			(  lowercase_      ,		lowercase_=None  )			->						Optional[int]:
       _snake_case      :							Union[str, Any]     				=						distilhubert().model.model

       if config_path is not None:
              _snake_case      :							Optional[int]     				=						HubertConfig.from_pretrained(_UpperCamelCase  )
       else:
              _snake_case      :							List[str]     				=						convert_config(_UpperCamelCase  )
       _snake_case      :							str     				=						model.eval()

       _snake_case      :							Dict     				=						WavaVecaFeatureExtractor(
           feature_size=1      ,		sampling_rate=16000      ,		padding_value=0      ,		do_normalize=_UpperCamelCase      ,		return_attention_mask=_UpperCamelCase      ,		)
       _snake_case      :							str     				=						HubertModel(_UpperCamelCase  )

       recursively_load_weights(_UpperCamelCase      ,		_UpperCamelCase  )

       feature_extractor.save_pretrained(_UpperCamelCase  )
       hf_model.save_pretrained(_UpperCamelCase  )


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 unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ConvNextImageProcessor

class 				A    (unittest.TestCase				):



      def __init__(					self     ,  lowercase_     ,  lowercase_=7     ,  lowercase_=3     ,  lowercase_=18     ,  lowercase_=30     ,  lowercase_=400     ,  lowercase_=True     ,  lowercase_=None     ,  lowercase_=0.875     ,  lowercase_=True     ,  lowercase_=[0.5, 0.5, 0.5]     ,  lowercase_=[0.5, 0.5, 0.5]     ,  )      ->   List[str]:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						size if size is not None else {'''shortest_edge''': 20}
             _snake_case      :							List[Any]     				=						parent
             _snake_case      :							Optional[int]     				=						batch_size
             _snake_case      :							str     				=						num_channels
             _snake_case      :							Union[str, Any]     				=						image_size
             _snake_case      :							Optional[Any]     				=						min_resolution
             _snake_case      :							Union[str, Any]     				=						max_resolution
             _snake_case      :							Tuple     				=						do_resize
             _snake_case      :							List[Any]     				=						size
             _snake_case      :							Optional[int]     				=						crop_pct
             _snake_case      :							Any     				=						do_normalize
             _snake_case      :							Optional[int]     				=						image_mean
             _snake_case      :							Union[str, Any]     				=						image_std



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_pct": self.crop_pct,
             }


@require_torch
@require_vision
class 				A    (__UpperCAmelCase       ,unittest.TestCase				):
      _SCREAMING_SNAKE_CASE					=							ConvNextImageProcessor if is_vision_available() else None



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						ConvNextImageProcessingTester(self					)



      @property
      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def    __a (					self					)      ->   str:



             '''simple docstring'''

             _snake_case      :							List[Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_resize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''size'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''crop_pct'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''do_normalize'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_mean'''					)					)
             self.assertTrue(hasattr(lowercase_     ,  '''image_std'''					)					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             _snake_case      :							int     				=						self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 20}					)

             _snake_case      :							Tuple     				=						self.image_processing_class.from_dict(self.image_processor_dict     ,  size=42					)
             self.assertEqual(image_processor.size     ,  {'''shortest_edge''': 42}					)



      def    __a (					self					)      ->   List[Any]:



             '''simple docstring'''

             pass



      def    __a (					self					)      ->   int:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _snake_case      :							List[str]     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							int     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )



      def    __a (					self					)      ->   Dict:



             '''simple docstring'''

             _snake_case      :							Tuple     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _snake_case      :							Tuple     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _snake_case      :							List[str]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							Union[str, Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )



      def    __a (					self					)      ->   Union[str, Any]:



             '''simple docstring'''

             _snake_case      :							Union[str, Any]     				=						self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _snake_case      :							Dict     				=						prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _snake_case      :							Union[str, Any]     				=						image_processing(image_inputs[0]     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )

             # Test batched
             _snake_case      :							List[Any]     				=						image_processing(lowercase_     ,  return_tensors='''pt'''					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size['''shortest_edge'''],
                     self.image_processor_tester.size['''shortest_edge'''],
                 )     ,  )






