



"""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()
lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "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",
}
lowercase__			   =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Tuple  ,       _lowerCamelCase				: Optional[Any]					):
							for attribute in key.split("""."""					):
														__a		:		Union[str, Any]									=       getattr(_lowerCamelCase  ,       _lowerCamelCase					)

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

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





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Any					):
							__a		:		Union[str, Any]									=       []
							__a		:		Optional[int]									=       fairseq_model.state_dict()

							__a		:		Union[str, Any]									=       hf_model.feature_extractor

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

							if config_path is not None:
														__a		:		Optional[int]									=       WavLMConfig.from_pretrained(_lowerCamelCase					)
							else:
														__a		:		Dict									=       WavLMConfig()

							__a		:		Dict									=       WavLMModel(_lowerCamelCase					)

							recursively_load_weights(_lowerCamelCase  ,       _lowerCamelCase					)

							hf_wavlm.save_pretrained(_lowerCamelCase					)


if __name__ == "__main__":
	lowercase__			   =  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")
	lowercase__			   =  parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






"""simple docstring"""

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowercase__			   =  logging.get_logger(__name__)




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"linear"
						_lowerCAmelCase							=					"cosine"
						_lowerCAmelCase							=					"cosine_with_restarts"
						_lowerCAmelCase							=					"polynomial"
						_lowerCAmelCase							=					"constant"
						_lowerCAmelCase							=					"constant_with_warmup"
						_lowerCAmelCase							=					"piecewise_constant"







def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int = -1					):
							return LambdaLR(_lowerCamelCase  ,       lambda _lowerCamelCase					: 1  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1.0  ,       _lowerCamelCase					)					)
														return 1.0

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int = -1					):
							__a		:		Optional[int]									=       {}
							__a		:		Any									=       step_rules.split(""","""					)
							for rule_str in rule_list[:-1]:
														__a							,     __a		:		int									=       rule_str.split(""":"""					)
														__a		:		Optional[int]									=       int(_lowerCamelCase					)
														__a		:		str									=       float(_lowerCamelCase					)
														__a		:		int									=       value
							__a		:		Dict									=       float(rule_list[-1]					)

							def create_rules_function(_lowerCamelCase				: str  ,       _lowerCamelCase				: Tuple					):
														def rule_func(_lowerCamelCase				: int					) -> float:
																					__a		:		Optional[Any]									=       sorted(rules_dict.keys()					)
																					for i, sorted_step in enumerate(_lowerCamelCase					):
																												if steps < sorted_step:
																																			return rules_dict[sorted_steps[i]]
																					return last_lr_multiple

														return rule_func

							__a		:		Optional[int]									=       create_rules_function(_lowerCamelCase  ,       _lowerCamelCase					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       last_epoch=_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: str=-1					):

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														return max(
														    0.0  ,       float(num_training_steps - current_step					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: float = 0.5  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Any					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       float(current_step - num_warmup_steps					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)
														return max(0.0  ,       0.5 * (1.0 + math.cos(math.pi * float(_lowerCamelCase					) * 2.0 * progress					))					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: int = -1					):

							def lr_lambda(_lowerCamelCase				: Optional[int]					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														__a		:		Dict									=       float(current_step - num_warmup_steps					) / float(max(1  ,       num_training_steps - num_warmup_steps					)					)
														if progress >= 1.0:
																					return 0.0
														return max(0.0  ,       0.5 * (1.0 + math.cos(math.pi * ((float(_lowerCamelCase					) * progress) % 1.0)					))					)

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: Optional[Any]=1E-7  ,       _lowerCamelCase				: Optional[int]=1.0  ,       _lowerCamelCase				: Optional[int]=-1					):
							__a		:		Union[str, Any]									=       optimizer.defaults["""lr"""]
							if not (lr_init > lr_end):
														raise ValueError(F'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'''					)

							def lr_lambda(_lowerCamelCase				: int					):
														if current_step < num_warmup_steps:
																					return float(_lowerCamelCase					) / float(max(1  ,       _lowerCamelCase					)					)
														elif current_step > num_training_steps:
																					return lr_end / lr_init  # as LambdaLR multiplies by lr_init
														else:
																					__a		:		Tuple									=       lr_init - lr_end
																					__a		:		int									=       num_training_steps - num_warmup_steps
																					__a		:		Optional[int]									=       1 - (current_step - num_warmup_steps) / decay_steps
																					__a		:		List[str]									=       lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)


lowercase__			   =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def       __magic_name__    (   _lowerCamelCase				: Union[str, SchedulerType]  ,       _lowerCamelCase				: Optimizer  ,       _lowerCamelCase				: Optional[str] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: Optional[int] = None  ,       _lowerCamelCase				: int = 1  ,       _lowerCamelCase				: float = 1.0  ,       _lowerCamelCase				: int = -1  ,       ):
							__a		:		int									=       SchedulerType(_lowerCamelCase					)
							__a		:		Optional[int]									=       TYPE_TO_SCHEDULER_FUNCTION[name]
							if name == SchedulerType.CONSTANT:
														return schedule_func(_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							if name == SchedulerType.PIECEWISE_CONSTANT:
														return schedule_func(_lowerCamelCase  ,       step_rules=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							# All other schedulers require `num_warmup_steps`
							if num_warmup_steps is None:
														raise ValueError(F'''{name} requires `num_warmup_steps`, please provide that argument.'''					)

							if name == SchedulerType.CONSTANT_WITH_WARMUP:
														return schedule_func(_lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

							# All other schedulers require `num_training_steps`
							if num_training_steps is None:
														raise ValueError(F'''{name} requires `num_training_steps`, please provide that argument.'''					)

							if name == SchedulerType.COSINE_WITH_RESTARTS:
														return schedule_func(
														    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       num_cycles=_lowerCamelCase  ,       last_epoch=_lowerCamelCase  ,       )

							if name == SchedulerType.POLYNOMIAL:
														return schedule_func(
														    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       power=_lowerCamelCase  ,       last_epoch=_lowerCamelCase  ,       )

							return schedule_func(
							    _lowerCamelCase  ,       num_warmup_steps=_lowerCamelCase  ,       num_training_steps=_lowerCamelCase  ,       last_epoch=_lowerCamelCase					)

