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

lowerCAmelCase__  :  List[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',
}
lowerCAmelCase__  :  Union[str, Any]						     =	[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				for attribute in key.split('.'       ):
								UpperCAmelCase__             =							getattr(lowerCamelCase					,			lowerCamelCase       )

				if weight_type is not None:
								UpperCAmelCase__             =							getattr(lowerCamelCase					,			lowerCamelCase       ).shape
				else:
								UpperCAmelCase__             =							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__             =							value
				elif weight_type == "weight_g":
								UpperCAmelCase__             =							value
				elif weight_type == "weight_v":
								UpperCAmelCase__             =							value
				elif weight_type == "bias":
								UpperCAmelCase__             =							value
				else:
								UpperCAmelCase__             =							value

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



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							[]
				UpperCAmelCase__             =							fairseq_model.state_dict()

				UpperCAmelCase__             =							hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								UpperCAmelCase__             =							False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			hf_model.config.feat_extract_norm == 'group'					,			)
												UpperCAmelCase__             =							True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
																				UpperCAmelCase__             =							True
																				if "*" in mapped_key:
																								UpperCAmelCase__             =							name.split(lowerCamelCase       )[0].split('.'       )[-2]
																								UpperCAmelCase__             =							mapped_key.replace('*'					,			lowerCamelCase       )
																				if "weight_g" in name:
																								UpperCAmelCase__             =							'weight_g'
																				elif "weight_v" in name:
																								UpperCAmelCase__             =							'weight_v'
																				elif "bias" in name and "relative_attention_bias" not in name:
																								UpperCAmelCase__             =							'bias'
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCAmelCase__             =							'weight'
																				else:
																								UpperCAmelCase__             =							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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							full_name.split('conv_layers.'       )[-1]
				UpperCAmelCase__             =							name.split('.'       )
				UpperCAmelCase__             =							int(items[0]       )
				UpperCAmelCase__             =							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__             =							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__             =							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__             =							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__             =							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   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=None       ):
				# load the pre-trained checkpoints
				UpperCAmelCase__             =							torch.load(lowerCamelCase       )
				UpperCAmelCase__             =							WavLMConfigOrig(checkpoint['cfg']       )
				UpperCAmelCase__             =							WavLMOrig(lowerCamelCase       )
				model.load_state_dict(checkpoint['model']       )
				model.eval()

				if config_path is not None:
								UpperCAmelCase__             =							WavLMConfig.from_pretrained(lowerCamelCase       )
				else:
								UpperCAmelCase__             =							WavLMConfig()

				UpperCAmelCase__             =							WavLMModel(lowerCamelCase       )

				recursively_load_weights(lowerCamelCase					,			lowerCamelCase       )

				hf_wavlm.save_pretrained(lowerCamelCase       )


if __name__ == "__main__":
						lowerCAmelCase__  :  Union[str, 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')
						lowerCAmelCase__  :  Optional[int]						     =	parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__  :  Tuple						     =	{'configuration_vit_mae': ['VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMAEConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  List[Any]						     =	[
						    'VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ViTMAEForPreTraining',
						    'ViTMAELayer',
						    'ViTMAEModel',
						    'ViTMAEPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__  :  int						     =	[
						    'TFViTMAEForPreTraining',
						    'TFViTMAEModel',
						    'TFViTMAEPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

						lowerCAmelCase__  :  int						     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



