




"""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   :  Union[str, Any]					=      logging.get_logger(__name__)

__A   :  int					=      {
    '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   :  Dict					=      [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		for attribute in key.split("."				):
				__lowercase				:		Optional[int]												=				getattr(_lowerCamelCase,  _lowerCamelCase				)

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






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				[]
		__lowercase				:		Tuple												=				fairseq_model.state_dict()

		__lowercase				:		int												=				hf_model.feature_extractor

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






		"""simple docstring"""


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






		"""simple docstring"""


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

		if config_path is not None:
				__lowercase				:		List[str]												=				WavLMConfig.from_pretrained(_lowerCamelCase				)
		else:
				__lowercase				:		int												=				WavLMConfig()

		__lowercase				:		Tuple												=				WavLMModel(_lowerCamelCase				)

		recursively_load_weights(_lowerCamelCase,  _lowerCamelCase				)

		hf_wavlm.save_pretrained(_lowerCamelCase				)


if __name__ == "__main__":
							__A   :  str					=      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   :  List[str]					=      parser.parse_args()
							convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






"""simple docstring"""







from __future__ import annotations

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = None				)			->list[list[str]]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				word_bank or []
		# create a table
		__lowercase				:		int												=				len(_lowerCamelCase				) + 1

		__lowercase				:		list[list[list[str]]]												=				[]
		for _ in range(_lowerCamelCase				):
				table.append([]				)
		# seed value
		__lowercase				:		Any												=				[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(_lowerCamelCase				):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(_lowerCamelCase				)] == word:
										__lowercase				:		list[list[str]]												=				[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(_lowerCamelCase				)] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(_lowerCamelCase				)]:
				combination.reverse()

		return table[len(_lowerCamelCase				)]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)
