



'''simple docstring'''






import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
A_							: Optional[int]						 =	logging.get_logger(__name__)

A_							: Dict						 =	{
    '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.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    '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',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
A_							: List[str]						 =	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Tuple:





       '''simple docstring'''


       for attribute in key.split("""."""		):
              _UpperCAmelCase  :						str			     = getattr(_lowercase       ,   _lowercase		)

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

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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						List[str]			     = []
       _UpperCAmelCase  :						Optional[int]			     = fairseq_model.state_dict()

       _UpperCAmelCase  :						Dict			     = hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _UpperCAmelCase  :						str			     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase       ,   hf_model.config.feat_extract_norm == """group"""       ,   )
                     _UpperCAmelCase  :						List[Any]			     = True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
                                   _UpperCAmelCase  :						Optional[Any]			     = True
                                   if "*" in mapped_key:
                                          _UpperCAmelCase  :						Tuple			     = name.split(_lowercase		)[0].split("""."""		)[-2]
                                          _UpperCAmelCase  :						Tuple			     = mapped_key.replace("""*"""       ,   _lowercase		)
                                   if "weight_g" in name:
                                          _UpperCAmelCase  :						Any			     = '''weight_g'''
                                   elif "weight_v" in name:
                                          _UpperCAmelCase  :						Optional[int]			     = '''weight_v'''
                                   elif "bias" in name and "relative_attention_bias" not in name:
                                          _UpperCAmelCase  :						List[str]			     = '''bias'''
                                   elif "weight" in name:
                                          # TODO: don't match quantizer.weight_proj
                                          _UpperCAmelCase  :						Optional[int]			     = '''weight'''
                                   else:
                                          _UpperCAmelCase  :						Optional[Any]			     = None

                                   set_recursively(_lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase       ,   _lowercase		)
                            continue
              if not is_used:
                     unused_weights.append(_lowercase		)

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




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


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




@torch.no_grad()
def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_=None		)->		List[Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = torch.load(_lowercase		)
       _UpperCAmelCase  :						int			     = WavLMConfigOrig(checkpoint["""cfg"""]		)
       _UpperCAmelCase  :						Tuple			     = WavLMOrig(_lowercase		)
       model.load_state_dict(checkpoint["""model"""]		)
       model.eval()

       if config_path is not None:
              _UpperCAmelCase  :						Optional[int]			     = WavLMConfig.from_pretrained(_lowercase		)
       else:
              _UpperCAmelCase  :						Optional[Any]			     = WavLMConfig()

       _UpperCAmelCase  :						Dict			     = WavLMModel(_lowercase		)

       recursively_load_weights(_lowercase       ,   _lowercase		)

       hf_wavlm.save_pretrained(_lowercase		)


if __name__ == "__main__":
   A_							: Optional[int]						 =	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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   A_							: Optional[Any]						 =	parser.parse_args()
   convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,  [None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,  ['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						if input_in_memory_max_size != "default":
												monkeypatch.setattr(datasets.config		,  '''IN_MEMORY_MAX_SIZE'''		,  _lowercase						)
						SCREAMING_SNAKE_CASE							:    Tuple							=							datasets.config.IN_MEMORY_MAX_SIZE
						if input_in_memory_max_size == "default":
												assert in_memory_max_size == 0
						else:
												assert in_memory_max_size == input_in_memory_max_size
						if dataset_size and in_memory_max_size:
												SCREAMING_SNAKE_CASE							:    str							=							dataset_size < in_memory_max_size
						else:
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							False
						SCREAMING_SNAKE_CASE							:    Any							=							is_small_dataset(_lowercase						)
						assert result == expected





