


"""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()
UpperCAmelCase    :     List[str]   						=		logging.get_logger(__name__)

UpperCAmelCase    :     Optional[Any]   						=		{
    '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',
}
UpperCAmelCase    :     Union[str, Any]   						=		[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Any       ,			_UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      int							)		->       Union[str, Any]:

							'''simple docstring'''



							for attribute in key.split("""."""							):
														__UpperCAmelCase   :						int      							=					getattr(__lowerCamelCase       ,			__lowerCamelCase							)

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

							logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)
def      lowerCamelCase       (     _UpperCamelCase		:      int       ,			_UpperCamelCase		:      Any							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						List[Any]      							=					fairseq_model.state_dict()

							__UpperCAmelCase   :						str      							=					hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														__UpperCAmelCase   :						Tuple      							=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCamelCase       ,			__lowerCamelCase       ,			__lowerCamelCase       ,			__lowerCamelCase       ,			hf_model.config.feat_extract_norm == """group"""       ,			)
																					__UpperCAmelCase   :						Optional[Any]      							=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
																																			__UpperCAmelCase   :						Dict      							=					True
																																			if "*" in mapped_key:
																																										__UpperCAmelCase   :						Union[str, Any]      							=					name.split(__lowerCamelCase							)[0].split("""."""							)[-2]
																																										__UpperCAmelCase   :						Dict      							=					mapped_key.replace("""*"""       ,			__lowerCamelCase							)
																																			if "weight_g" in name:
																																										__UpperCAmelCase   :						int      							=					"weight_g"
																																			elif "weight_v" in name:
																																										__UpperCAmelCase   :						Dict      							=					"weight_v"
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										__UpperCAmelCase   :						Dict      							=					"bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__UpperCAmelCase   :						List[Any]      							=					"weight"
																																			else:
																																										__UpperCAmelCase   :						List[str]      							=					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       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Tuple       ,			_UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Any							)		->       Dict:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					full_name.split("""conv_layers."""							)[-1]
							__UpperCAmelCase   :						List[Any]      							=					name.split("""."""							)
							__UpperCAmelCase   :						Tuple      							=					int(items[0]							)
							__UpperCAmelCase   :						List[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   :						List[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   :						List[str]      							=					value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
							else:
														unused_weights.append(__lowerCamelCase							)
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      List[str]       ,			_UpperCamelCase		:      Union[str, Any]=None							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					torch.load(__lowerCamelCase							)
							__UpperCAmelCase   :						List[Any]      							=					WavLMConfigOrig(checkpoint["""cfg"""]							)
							__UpperCAmelCase   :						Optional[int]      							=					WavLMOrig(__lowerCamelCase							)
							model.load_state_dict(checkpoint["""model"""]							)
							model.eval()

							if config_path is not None:
														__UpperCAmelCase   :						str      							=					WavLMConfig.from_pretrained(__lowerCamelCase							)
							else:
														__UpperCAmelCase   :						Optional[int]      							=					WavLMConfig()

							__UpperCAmelCase   :						Any      							=					WavLMModel(__lowerCamelCase							)

							recursively_load_weights(__lowerCamelCase       ,			__lowerCamelCase							)

							hf_wavlm.save_pretrained(__lowerCamelCase							)


if __name__ == "__main__":
							UpperCAmelCase    :     Any   						=		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')
							UpperCAmelCase    :     Dict   						=		parser.parse_args()
							convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)


from typing import TYPE_CHECKING

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


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

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





