

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

_SCREAMING_SNAKE_CASE    :      Tuple          =			{
    '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',
}
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Union[str, Any]							,						__UpperCamelCase :						Optional[Any]							,						__UpperCamelCase :						Any							,						__UpperCamelCase :						Dict							,						__UpperCamelCase :						Union[str, Any]       ) ->       int:

				"""simple docstring"""







				for attribute in key.split('''.'''       ):
								A__  :	int         =							getattr(lowerCAmelCase__							,						lowerCAmelCase__       )

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

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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						str       ) ->       Union[str, Any]:

				"""simple docstring"""







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

				A__  :	Optional[Any]         =							hf_model.feature_extractor

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

				"""simple docstring"""







				A__  :	Union[str, Any]         =							full_name.split('''conv_layers.'''       )[-1]
				A__  :	Tuple         =							name.split('''.'''       )
				A__  :	int         =							int(items[0]       )
				A__  :	List[str]         =							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."
												)
												A__  :	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."
												)
												A__  :	Tuple         =							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."
												)
												A__  :	Optional[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."
												)
												A__  :	Optional[int]         =							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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						int							,						__UpperCamelCase :						List[str]=None       ) ->       str:

				"""simple docstring"""







				A__  :	Dict         =							torch.load(lowerCAmelCase__       )
				A__  :	Optional[Any]         =							WavLMConfigOrig(checkpoint['''cfg''']       )
				A__  :	int         =							WavLMOrig(lowerCAmelCase__       )
				model.load_state_dict(checkpoint['''model''']       )
				model.eval()

				if config_path is not None:
								A__  :	Tuple         =							WavLMConfig.from_pretrained(lowerCAmelCase__       )
				else:
								A__  :	Dict         =							WavLMConfig()

				A__  :	Optional[int]         =							WavLMModel(lowerCAmelCase__       )

				recursively_load_weights(lowerCAmelCase__							,						lowerCAmelCase__       )

				hf_wavlm.save_pretrained(lowerCAmelCase__       )


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      Optional[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')
					_SCREAMING_SNAKE_CASE    :      Any          =			parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, 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







class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				None





	def   __snake_case				(			self		):
					A__  :	Dict         =							self.feature_extraction_class(**self.feat_extract_dict		)
					A__  :	Tuple         =							json.loads(feat_extract.to_json_string()		)
					for key, value in self.feat_extract_dict.items():
									self.assertEqual(obj[key]				,       UpperCamelCase__		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							os.path.join(UpperCamelCase__				,       '''feat_extract.json'''		)
									feat_extract_first.to_json_file(UpperCamelCase__		)
									A__  :	Dict         =							self.feature_extraction_class.from_json_file(UpperCamelCase__		)

					self.assertEqual(feat_extract_second.to_dict()				,       feat_extract_first.to_dict()		)





	def   __snake_case				(			self		):
					A__  :	Any         =							self.feature_extraction_class(**self.feat_extract_dict		)

					with tempfile.TemporaryDirectory() as tmpdirname:
									A__  :	Any         =							feat_extract_first.save_pretrained(UpperCamelCase__		)[0]
									check_json_file_has_correct_format(UpperCamelCase__		)
									A__  :	Optional[int]         =							self.feature_extraction_class.from_pretrained(UpperCamelCase__		)

					self.assertEqual(feat_extract_second.to_dict()				,       feat_extract_first.to_dict()		)





	def   __snake_case				(			self		):
					A__  :	str         =							self.feature_extraction_class()
					self.assertIsNotNone(UpperCamelCase__		)