
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    A (      lowercase					, lowercase					, lowercase					, lowercase				)     ->						str:
  '''simple docstring'''

  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    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase=True				)     ->						List[Any]:
  '''simple docstring'''

  model.train()
  UpperCamelCase						=  model(lowercase				)
  UpperCamelCase						=  F.mse_loss(lowercase					, target.to(output.device				)				)
  if not do_backward:
    loss /= accelerator.gradient_accumulation_steps
    loss.backward()
  else:
    accelerator.backward(lowercase				)


def    A (      lowercase					, lowercase=False				)     ->						Union[str, Any]:
  '''simple docstring'''

  set_seed(42				)
  UpperCamelCase						=  RegressionModel()
  UpperCamelCase						=  deepcopy(lowercase				)
  UpperCamelCase						=  RegressionDataset(length=80				)
  UpperCamelCase						=  DataLoader(lowercase					, batch_size=16				)
  model.to(accelerator.device				)
  if sched:
    UpperCamelCase						=  AdamW(params=model.parameters()					, lr=1e-3				)
    UpperCamelCase						=  AdamW(params=ddp_model.parameters()					, lr=1e-3				)
    UpperCamelCase						=  LambdaLR(lowercase					, lr_lambda=lambda lowercase				: epoch**0.6_5				)
    UpperCamelCase						=  LambdaLR(lowercase					, lr_lambda=lambda lowercase				: epoch**0.6_5				)
  # Make a copy of `model`
  if sched:
    UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  accelerator.prepare(lowercase					, lowercase					, lowercase					, lowercase				)
  else:
    UpperCamelCase			,						UpperCamelCase						=  accelerator.prepare(lowercase					, lowercase				)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader


def    A (      lowercase				)     ->						str:
  '''simple docstring'''

  UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  get_training_setup(lowercase				)
  # Use a single batch
  UpperCamelCase			,						UpperCamelCase						=  next(iter(lowercase				)				).values()
  for iteration in range(3				):
    # Gather the distributed inputs and targs for the base model
    UpperCamelCase			,						UpperCamelCase						=  accelerator.gather((ddp_input, ddp_target)				)
    UpperCamelCase			,						UpperCamelCase						=  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(1_337 + iteration				)
    UpperCamelCase						=  ddp_input[torch.randperm(len(lowercase				)				)]


def    A (      lowercase				)     ->						List[Any]:
  '''simple docstring'''

  UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  get_training_setup(lowercase				)
  # Use a single batch
  UpperCamelCase			,						UpperCamelCase						=  next(iter(lowercase				)				).values()
  for iteration in range(3				):
    # Gather the distributed inputs and targs for the base model
    UpperCamelCase			,						UpperCamelCase						=  accelerator.gather((ddp_input, ddp_target)				)
    UpperCamelCase			,						UpperCamelCase						=  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(1_337 + iteration				)
    UpperCamelCase						=  ddp_input[torch.randperm(len(lowercase				)				)]


def    A (      lowercase=False					, lowercase=False				)     ->						str:
  '''simple docstring'''

  UpperCamelCase						=  Accelerator(
      split_batches=lowercase					, dispatch_batches=lowercase					, gradient_accumulation_steps=2				)
  # Test that context manager behaves properly
  UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  get_training_setup(lowercase				)
  for iteration, batch in enumerate(lowercase				):
    UpperCamelCase			,						UpperCamelCase						=  batch.values()
    # Gather the distributed inputs and targs for the base model
    UpperCamelCase			,						UpperCamelCase						=  accelerator.gather((ddp_input, ddp_target)				)
    UpperCamelCase			,						UpperCamelCase						=  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(1_337 + iteration				)
    UpperCamelCase						=  ddp_input[torch.randperm(len(lowercase				)				)]
  GradientState._reset_state()


def    A (      lowercase=False					, lowercase=False				)     ->						List[str]:
  '''simple docstring'''

  UpperCamelCase						=  Accelerator(
      split_batches=lowercase					, dispatch_batches=lowercase					, gradient_accumulation_steps=2				)
  # Test that context manager behaves properly
  UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  get_training_setup(lowercase					, lowercase				)
  for iteration, batch in enumerate(lowercase				):
    UpperCamelCase			,						UpperCamelCase						=  batch.values()
    # Gather the distributed inputs and targs for the base model
    UpperCamelCase			,						UpperCamelCase						=  accelerator.gather((ddp_input, ddp_target)				)
    UpperCamelCase			,						UpperCamelCase						=  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'''
    UpperCamelCase						=  (((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(1_337 + iteration				)
  GradientState._reset_state()


def    A (      )     ->						int:
  '''simple docstring'''

  UpperCamelCase						=  Accelerator()

  UpperCamelCase						=  RegressionDataset(length=80				)
  UpperCamelCase						=  DataLoader(lowercase					, batch_size=16				)
  UpperCamelCase						=  RegressionDataset(length=96				)
  UpperCamelCase						=  DataLoader(lowercase					, batch_size=16				)
  UpperCamelCase			,						UpperCamelCase						=  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    A (      )     ->						Optional[int]:
  '''simple docstring'''

  UpperCamelCase						=  Accelerator()
  UpperCamelCase						=  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    A (      lowercase				)     ->						Any:
  '''simple docstring'''

  main()


if __name__ == "__main__":
     main()


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase      :				Tuple          = logging.get_logger(__name__)

_UpperCAmelCase      :				Tuple          = {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "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": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_UpperCAmelCase      :				Any          = [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Dict:
		'''simple docstring'''

		for attribute in key.split('.'				):
				UpperCamelCase						=  getattr(lowercase					, lowercase				)

		if weight_type is not None:
				UpperCamelCase						=  getattr(lowercase					, lowercase				).shape
		else:
				UpperCamelCase						=  hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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
		elif weight_type == "running_mean":
				UpperCamelCase						=  value
		elif weight_type == "running_var":
				UpperCamelCase						=  value
		elif weight_type == "num_batches_tracked":
				UpperCamelCase						=  value
		elif weight_type == "inv_freq":
				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 (      lowercase					, lowercase					, lowercase				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  []
		UpperCamelCase						=  fairseq_model.state_dict()

		UpperCamelCase						=  hf_model.wavaveca_conformer.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase						=  False
				if "conv_layers" in name:
						load_conv_layer(
						    lowercase					, lowercase					, lowercase					, lowercase					, hf_model.config.feat_extract_norm == 'group'					, )
						UpperCamelCase						=  True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase						=  'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.'				)[-1] == name.split('.'				)[0]:
										UpperCamelCase						=  True
										if "*" in mapped_key:
												UpperCamelCase						=  name.split(lowercase				)[0].split('.'				)[-2]
												UpperCamelCase						=  mapped_key.replace('*'					, lowercase				)
										if "pos_bias_u" in name:
												UpperCamelCase						=  None
										elif "pos_bias_v" in name:
												UpperCamelCase						=  None
										elif "weight_g" in name:
												UpperCamelCase						=  'weight_g'
										elif "weight_v" in name:
												UpperCamelCase						=  'weight_v'
										elif "bias" in name:
												UpperCamelCase						=  'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCamelCase						=  'weight'
										elif "running_mean" in name:
												UpperCamelCase						=  'running_mean'
										elif "inv_freq" in name:
												UpperCamelCase						=  'inv_freq'
										elif "running_var" in name:
												UpperCamelCase						=  'running_var'
										elif "num_batches_tracked" in name:
												UpperCamelCase						=  'num_batches_tracked'
										else:
												UpperCamelCase						=  None
										set_recursively(lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)
								continue
				if not is_used:
						unused_weights.append(lowercase				)

		logger.warning(f'''Unused weights: {unused_weights}'''				)


def    A (      lowercase					, lowercase					, lowercase					, lowercase					, lowercase				)     ->						Optional[int]:
		'''simple docstring'''

		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:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    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:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[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(lowercase				)


@torch.no_grad()
def    A (      lowercase					, lowercase					, lowercase=None					, lowercase=None					, lowercase=True				)     ->						int:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  WavaVecaConformerConfig.from_pretrained(lowercase					, hidden_act='swish'				)
		else:
				UpperCamelCase						=  WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				UpperCamelCase						=  'rotary'

		if is_finetuned:
				if dict_path:
						UpperCamelCase						=  Dictionary.load(lowercase				)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						UpperCamelCase						=  target_dict.pad_index
						UpperCamelCase						=  target_dict.bos_index
						UpperCamelCase						=  target_dict.eos_index
						UpperCamelCase						=  len(target_dict.symbols				)
						UpperCamelCase						=  os.path.join(lowercase					, 'vocab.json'				)
						if not os.path.isdir(lowercase				):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase				)				)
								return
						os.makedirs(lowercase					, exist_ok=lowercase				)
						UpperCamelCase						=  target_dict.indices

						# fairseq has the <pad> and <s> switched
						UpperCamelCase						=  0
						UpperCamelCase						=  1
						with open(lowercase					, 'w'					, encoding='utf-8'				) as vocab_handle:
								json.dump(lowercase					, lowercase				)
						UpperCamelCase						=  WavaVecaCTCTokenizer(
						    lowercase					, unk_token=target_dict.unk_word					, pad_token=target_dict.pad_word					, bos_token=target_dict.bos_word					, eos_token=target_dict.eos_word					, word_delimiter_token='|'					, do_lower_case=lowercase					, )
						UpperCamelCase						=  True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase						=  WavaVecaFeatureExtractor(
						    feature_size=1					, sampling_rate=16_000					, padding_value=0					, do_normalize=lowercase					, return_attention_mask=lowercase					, )
						UpperCamelCase						=  WavaVecaProcessor(feature_extractor=lowercase					, tokenizer=lowercase				)
						processor.save_pretrained(lowercase				)

				UpperCamelCase						=  WavaVecaConformerForCTC(lowercase				)
		else:
				UpperCamelCase						=  WavaVecaConformerForPreTraining(lowercase				)

		if is_finetuned:
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]					, arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				)}				)
		else:
				UpperCamelCase						=  argparse.Namespace(task='audio_pretraining'				)
				UpperCamelCase						=  fairseq.tasks.setup_task(lowercase				)

				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					, task=lowercase				)

		UpperCamelCase						=  model[0].eval()

		recursively_load_weights(lowercase					, lowercase					, not is_finetuned				)

		hf_wavavec.save_pretrained(lowercase				)


if __name__ == "__main__":
					_UpperCAmelCase      :				Tuple          = 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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					_UpperCAmelCase      :				Dict          = parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)
