






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

_snake_case          =   {
    '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',
}
_snake_case          =   [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for attribute in key.split("""."""				):
    _a  :       List[str]             =	getattr(UpperCamelCase__     ,   UpperCamelCase__				)

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

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

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Union[str, Any]             =	[]
  _a  :       Union[str, Any]             =	fairseq_model.state_dict()

  _a  :       int             =	hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    _a  :       Union[str, Any]             =	False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   hf_model.config.feat_extract_norm == """group"""     ,   )
      _a  :       List[str]             =	True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""				)[-1] == name.split("""."""				)[0]:
          _a  :       List[Any]             =	True
          if "*" in mapped_key:
            _a  :       Dict             =	name.split(UpperCamelCase__				)[0].split("""."""				)[-2]
            _a  :       str             =	mapped_key.replace("""*"""     ,   UpperCamelCase__				)
          if "weight_g" in name:
            _a  :       List[Any]             =	"""weight_g"""
          elif "weight_v" in name:
            _a  :       Optional[int]             =	"""weight_v"""
          elif "bias" in name and "relative_attention_bias" not in name:
            _a  :       Optional[Any]             =	"""bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            _a  :       str             =	"""weight"""
          else:
            _a  :       Dict             =	None

          set_recursively(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
        continue
    if not is_used:
      unused_weights.append(UpperCamelCase__				)

  logger.warning(F"""Unused weights: {unused_weights}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Tuple             =	full_name.split("""conv_layers."""				)[-1]
  _a  :       str             =	name.split("""."""				)
  _a  :       str             =	int(items[0]				)
  _a  :       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  :       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  :       Optional[int]             =	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  :       Union[str, Any]             =	value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""				)
  else:
    unused_weights.append(UpperCamelCase__				)

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=None				):



  '''simple docstring'''
  # load the pre-trained checkpoints
  _a  :       Optional[int]             =	torch.load(UpperCamelCase__				)
  _a  :       Union[str, Any]             =	WavLMConfigOrig(checkpoint["""cfg"""]				)
  _a  :       List[Any]             =	WavLMOrig(UpperCamelCase__				)
  model.load_state_dict(checkpoint["""model"""]				)
  model.eval()

  if config_path is not None:
    _a  :       Any             =	WavLMConfig.from_pretrained(UpperCamelCase__				)
  else:
    _a  :       Tuple             =	WavLMConfig()

  _a  :       Tuple             =	WavLMModel(UpperCamelCase__				)

  recursively_load_weights(UpperCamelCase__     ,   UpperCamelCase__				)

  hf_wavlm.save_pretrained(UpperCamelCase__				)


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








'''simple docstring'''







from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Dict						)	->	str:
				for param, grad_param in zip(model_a.parameters()    ,							model_b.parameters()						):
								if not param.requires_grad:
												continue
								if not did_step:
												# Grads should not be in sync
												assert (
												    torch.allclose(param.grad    ,							grad_param.grad						) is False
												), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
								else:
												# Grads should be in sync
												assert (
												    torch.allclose(param.grad    ,							grad_param.grad						) is True
												), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'






def  _lowerCamelCase				( lowercase						:      Optional[Any]    ,							lowercase						:      int    ,							lowercase						:      Tuple    ,							lowercase						:      Optional[int]    ,							lowercase						:      int=True						)	->	Any:
				model.train()
				_a								= model(lowercase						)
				_a								= F.mse_loss(lowercase    ,							target.to(output.device						)						)
				if not do_backward:
								loss /= accelerator.gradient_accumulation_steps
								loss.backward()
				else:
								accelerator.backward(lowercase						)






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      Tuple=False						)	->	List[str]:
				set_seed(42						)
				_a								= RegressionModel()
				_a								= deepcopy(lowercase						)
				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				model.to(accelerator.device						)
				if sched:
								_a								= AdamW(params=model.parameters()    ,							lr=1E-3						)
								_a								= AdamW(params=ddp_model.parameters()    ,							lr=1E-3						)
								_a								= LambdaLR(lowercase    ,							lr_lambda=lambda lowercase						: epoch**0.65						)
								_a								= LambdaLR(lowercase    ,							lr_lambda=lambda lowercase						: epoch**0.65						)
				# Make a copy of `model`
				if sched:
								_a	,					_a	,					_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
				else:
								_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase						)
				if sched:
								return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
				return model, ddp_model, dataloader






def  _lowerCamelCase				( lowercase						:      Optional[Any]						)	->	Optional[int]:
				# Test when on a single CPU or GPU that the context manager does nothing
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowercase						):
																step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												# Sync grads
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
								check_model_parameters(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								for param, ddp_param in zip(model.parameters()    ,							ddp_model.parameters()						):
												if not param.requires_grad:
																continue
												assert torch.allclose(
												    param.grad    ,							ddp_param.grad						), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      Tuple						)	->	Tuple:
				# Test on distributed setup that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				# Use a single batch
				_a	,					_a								= next(iter(lowercase						)						).values()
				for iteration in range(3						):
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								if iteration % 2 == 0:
												# Accumulate grads locally
												with accelerator.no_sync(lowercase						):
																step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								else:
												# Sync grads
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()    ,							ddp_model.parameters()						):
												if not param.requires_grad:
																continue
												if iteration % 2 == 0:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is False
																), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
												else:
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is True
																), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]






def  _lowerCamelCase				( lowercase						:      List[Any]=False    ,							lowercase						:      Optional[int]=False						)	->	Any:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a								= get_training_setup(lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_a	,					_a								= batch.values()
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Do "gradient accumulation" (noop)
								with accelerator.accumulate(lowercase						):
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)

								# DDP model and model should only be in sync when not (iteration % 2 == 0)
								for param, ddp_param in zip(model.parameters()    ,							ddp_model.parameters()						):
												if not param.requires_grad:
																continue
												if ((iteration + 1) % 2 == 0) or (iteration == len(lowercase						) - 1):
																# Grads should be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is True
																), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
												else:
																# Grads should not be in sync
																assert (
																    torch.allclose(param.grad    ,							ddp_param.grad						) is False
																), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
								_a								= ddp_input[torch.randperm(len(lowercase						)						)]
				GradientState._reset_state()






def  _lowerCamelCase				( lowercase						:      int=False    ,							lowercase						:      int=False						)	->	Dict:
				_a								= Accelerator(
				    split_batches=lowercase    ,							dispatch_batches=lowercase    ,							gradient_accumulation_steps=2						)
				# Test that context manager behaves properly
				_a	,					_a	,					_a	,					_a	,					_a	,					_a	,					_a								= get_training_setup(lowercase    ,							lowercase						)
				for iteration, batch in enumerate(lowercase						):
								_a	,					_a								= batch.values()
								# Gather the distributed inputs and targs for the base model
								_a	,					_a								= accelerator.gather((ddp_input, ddp_target)						)
								_a	,					_a								= input.to(accelerator.device						), target.to(accelerator.device						)
								# Perform our initial ground truth step in non "DDP"
								model.train()
								ddp_model.train()
								step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								opt.step()

								if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase						)):
												if split_batches:
																sched.step()
												else:
																for _ in range(accelerator.num_processes						):
																				sched.step()
								opt.zero_grad()
								# Perform gradient accumulation under wrapper
								with accelerator.accumulate(lowercase						):
												step_model(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
												ddp_opt.step()
												ddp_sched.step()
												ddp_opt.zero_grad()

								# Learning rates should be the same
								assert (
								    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
								), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
								_a								= (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase						))
								if accelerator.num_processes > 1:
												check_model_parameters(lowercase    ,							lowercase    ,							lowercase    ,							lowercase						)
								# Shuffle ddp_input on each iteration
								torch.manual_seed(1337 + iteration						)
				GradientState._reset_state()






def  _lowerCamelCase				( )	->	Any:
				_a								= Accelerator()

				_a								= RegressionDataset(length=80						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a								= RegressionDataset(length=96						)
				_a								= DataLoader(lowercase    ,							batch_size=16						)
				_a	,					_a								= accelerator.prepare(lowercase    ,							lowercase						)
				assert accelerator.gradient_state.active_dataloader is None
				for iteration, _ in enumerate(lowercase						):
								assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase						)
								if iteration < len(lowercase						) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
												if iteration == 1:
																for batch_num, _ in enumerate(lowercase						):
																				assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase						)
																				if batch_num < len(lowercase						) - 1:
																								assert not accelerator.gradient_state.end_of_dataloader
																				else:
																								assert accelerator.gradient_state.end_of_dataloader
								else:
												assert accelerator.gradient_state.end_of_dataloader
				assert accelerator.gradient_state.active_dataloader is None






def  _lowerCamelCase				( )	->	Optional[Any]:
				_a								= Accelerator()
				_a								= accelerator.state
				if state.local_process_index == 0:
								print("**Test `accumulate` gradient accumulation with dataloader break**"						)
				test_dataloader_break()
				if state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print("**Test NOOP `no_sync` context manager**"						)
								test_noop_sync(lowercase						)
				if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
								if state.local_process_index == 0:
												print("**Test Distributed `no_sync` context manager**"						)
								test_distributed_sync(lowercase						)
				if state.distributed_type == DistributedType.MULTI_GPU:
								for split_batch in [True, False]:
												for dispatch_batches in [True, False]:
																if state.local_process_index == 0:
																				print(
																				    "**Test `accumulate` gradient accumulation, "    ,							F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    ,							)
																test_gradient_accumulation(lowercase    ,							lowercase						)

    # Currently will break on torch 2.0 +, need to investigate why
				if is_torch_version("<"    ,							"2.0"						) or state.distributed_type == DistributedType.NO:
								if state.local_process_index == 0:
												print(
												    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "    ,							"`split_batches=False`, `dispatch_batches=False`**"    ,							)
								test_gradient_accumulation_with_opt_and_scheduler()
								if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																for dispatch_batches in [True, False]:
																				if not split_batch and not dispatch_batches:
																								continue
																				if state.local_process_index == 0:
																								print(
																								    "**Test `accumulate` gradient accumulation with optimizer and scheduler, "    ,							F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'    ,							)
																				test_gradient_accumulation_with_opt_and_scheduler(lowercase    ,							lowercase						)






def  _lowerCamelCase				( lowercase						:      Any						)	->	Tuple:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
					main()
