
import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed

class 				_UpperCamelCase (    _A     ):






			'''simple docstring'''

			def __init__(				self       :				List[Any]  ,		snake_case_       :				List[Any]  ,		snake_case_       :				int  ,		snake_case_       :				Any=False  ,		snake_case_       :				Optional[Any]=1024  ,		snake_case_       :				Dict=1024  ,		snake_case_       :				List[str]=3.6  ,		snake_case_       :				Union[str, Any]=False  ,		):
							UpperCamelCase_:							Optional[int]		=  tokenizer
							UpperCamelCase_:							Dict		=  tokenizer.bos_token_id
							UpperCamelCase_:							Optional[Any]		=  dataset
							UpperCamelCase_:							int		=  seq_length
							UpperCamelCase_:							List[Any]		=  0
							UpperCamelCase_:							List[Any]		=  infinite
							UpperCamelCase_:							List[Any]		=  0
							UpperCamelCase_:							Union[str, Any]		=  tokenized

							if self.tokenized:
											UpperCamelCase_:							Union[str, Any]		=  seq_length * num_of_sequences
											UpperCamelCase_:							str		=  """input_ids"""
							else:
											UpperCamelCase_:							Tuple		=  seq_length * chars_per_token * num_of_sequences
											UpperCamelCase_:							Union[str, Any]		=  """content"""

			def __iter__(				self       :				Tuple					):
							UpperCamelCase_:							Union[str, Any]		=  iter(self.dataset					)
							UpperCamelCase_:							Optional[int]		=  True
							while more_examples:
											UpperCamelCase_,      UpperCamelCase_:							str		=  [], 0
											while True:
															if buffer_len >= self.max_buffer_size:
																			break
															try:
																			buffer.append(next(snake_case_					)[self.content_field]					)
																			buffer_len += len(buffer[-1]					)
															except StopIteration:
																			if self.infinite:
																							UpperCamelCase_:							Optional[Any]		=  iter(self.dataset					)
																							self.epoch += 1
																							logger.info(f'''Dataset epoch: {self.epoch}'''					)
																			else:
																							UpperCamelCase_:							List[Any]		=  False
																							break
											if self.tokenized:
															UpperCamelCase_:							List[str]		=  buffer
											else:
															UpperCamelCase_:							Union[str, Any]		=  self.tokenizer(snake_case_  ,		truncation=snake_case_					)["""input_ids"""]
											UpperCamelCase_:							Any		=  []
											for tokenized_input in tokenized_inputs:
															all_token_ids.extend(tokenized_input + [self.concat_token_id]					)
											for i in range(0  ,		len(snake_case_					)  ,		self.seq_length					):
															UpperCamelCase_:							Dict		=  all_token_ids[i : i + self.seq_length]
															if len(snake_case_					) == self.seq_length:
																			self.current_size += 1
																			yield torch.tensor(snake_case_					)




			def 					lowerCAmelCase__		(				self       :				Tuple  ,		snake_case_       :				Dict=1000					):
							return ShufflerIterDataPipe(self  ,		buffer_size=snake_case_					)







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				UpperCamelCase_:							List[Any]		=  args.model_ckpt.split("""/"""  )[-1]
				UpperCamelCase_:							List[str]		=  logging.getLogger(__name__  )
				UpperCamelCase_:							List[str]		=  Path(args.save_dir  ) / """log/"""
				log_dir.mkdir(exist_ok=lowerCamelCase  )
				UpperCamelCase_:							Optional[int]		=  F'''debug_{accelerator.process_index}.log'''
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""   ,			datefmt="""%m/%d/%Y %H:%M:%S"""   ,			level=logging.INFO   ,			handlers=[logging.FileHandler(log_dir / filename  ), logging.StreamHandler()]   ,			)
				if accelerator.is_main_process:  # we only want to setup logging once
								accelerator.init_trackers(lowerCamelCase   ,			vars(lowerCamelCase  )  )
								UpperCamelCase_:							Dict		=  accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO  )
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								UpperCamelCase_:							Optional[int]		=  """"""
								logger.setLevel(logging.ERROR  )
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name







def 				A__    (  lowerCamelCase  )     ->		List[str]:
				UpperCamelCase_:							Union[str, Any]		=  {"""streaming""": True}
				UpperCamelCase_:							int		=  load_dataset(args.dataset_name_train   ,			split="""train"""   ,			**lowerCamelCase  )
				UpperCamelCase_:							int		=  train_data.shuffle(buffer_size=args.shuffle_buffer   ,			seed=args.seed  )
				UpperCamelCase_:							List[Any]		=  load_dataset(args.dataset_name_valid   ,			split="""train"""   ,			**lowerCamelCase  )
				UpperCamelCase_:							Optional[int]		=  ConstantLengthDataset(
				    lowerCamelCase   ,			lowerCamelCase   ,			infinite=lowerCamelCase   ,			seq_length=args.seq_length   ,			tokenized=args.tokenized  )
				UpperCamelCase_:							int		=  ConstantLengthDataset(
				    lowerCamelCase   ,			lowerCamelCase   ,			infinite=lowerCamelCase   ,			seq_length=args.seq_length   ,			tokenized=args.tokenized  )
				UpperCamelCase_:							Union[str, Any]		=  train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
				UpperCamelCase_:							List[Any]		=  DataLoader(lowerCamelCase   ,			batch_size=args.train_batch_size   ,			shuffle=lowerCamelCase  )
				UpperCamelCase_:							int		=  DataLoader(lowerCamelCase   ,			batch_size=args.valid_batch_size  )
				return train_dataloader, eval_dataloader







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  )     ->		Dict:
				UpperCamelCase_,      UpperCamelCase_:							List[Any]		=  [], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay  ):
												params_without_wd.append(lowerCamelCase  )
								else:
												params_with_wd.append(lowerCamelCase  )
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]







def 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[Any]:
				logger.info(F'''Step {step}: {metrics}'''  )
				if accelerator.is_main_process:
								accelerator.log(lowerCamelCase   ,			lowerCamelCase  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
				UpperCamelCase_:							Tuple		=  accelerator.unwrap_model(lowerCamelCase  ).config
				UpperCamelCase_:							List[str]		=  4 if args.gradient_checkpointing else 3
				UpperCamelCase_:							List[Any]		=  args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				UpperCamelCase_:							Optional[int]		=  24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				UpperCamelCase_:							Optional[Any]		=  factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
				)
				UpperCamelCase_:							List[Any]		=  flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops







def 				A__    (  lowerCamelCase  )     ->		Union[str, Any]:
				model.eval()
				UpperCamelCase_:							Union[str, Any]		=  []
				for step, batch in enumerate(lowerCamelCase  ):
								with torch.no_grad():
												UpperCamelCase_:							Tuple		=  model(lowerCamelCase   ,			labels=lowerCamelCase  )
								UpperCamelCase_:							Any		=  outputs.loss.repeat(args.valid_batch_size  )
								losses.append(accelerator.gather(lowerCamelCase  )  )
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				UpperCamelCase_:							List[str]		=  torch.cat(lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  losses[: eval_dataloader.dataset.current_size].mean()
				try:
								UpperCamelCase_:							Union[str, Any]		=  torch.exp(lowerCamelCase  )
				except OverflowError:
								UpperCamelCase_:							List[Any]		=  float("""inf"""  )
				return loss.item(), perplexity.item()


# Settings
lowerCamelCase_  :       Dict					      =     HfArgumentParser(TrainingArguments)
lowerCamelCase_  :       int					      =     parser.parse_args()

# Accelerator
lowerCamelCase_  :       str					      =     ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
lowerCamelCase_  :       Dict					      =     Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
lowerCamelCase_  :       List[Any]					      =     {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

lowerCamelCase_  :       Tuple					      =     Namespace(**vars(args), **acc_state)
lowerCamelCase_  :       int					      =     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
							lowerCamelCase_  :       Tuple					      =     Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
lowerCamelCase_      ,				lowerCamelCase_  :       Dict					      =     setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
							hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
lowerCamelCase_  :       Tuple					      =     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
							model.gradient_checkpointing_enable()
lowerCamelCase_  :       List[Any]					      =     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase_      ,				lowerCamelCase_  :       List[Any]					      =     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase_  :       Optional[Any]					      =     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase_  :       int					      =     get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)







def 				A__    (  )     ->		Optional[Any]:
				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_      ,				lowerCamelCase_  :       Union[str, Any]					      =     accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
							if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
														accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}""")
														accelerator.load_state(args.resume_from_checkpoint)
														lowerCamelCase_  :       str					      =     os.path.basename(args.resume_from_checkpoint)
							else:
														# Get the most recent checkpoint
														lowerCamelCase_  :       Union[str, Any]					      =     [f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
														dirs.sort(key=os.path.getctime)
														lowerCamelCase_  :       int					      =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
							# Extract the step of the checkpoint to continue from there
							lowerCamelCase_  :       List[Any]					      =     os.path.splitext(path)[0]
							lowerCamelCase_  :       Dict					      =     int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
lowerCamelCase_  :       str					      =     0
lowerCamelCase_  :       Optional[Any]					      =     time.time()
lowerCamelCase_  :       Optional[int]					      =     0
for step, batch in enumerate(train_dataloader, start=1):
							if args.resume_from_checkpoint and step < resume_step:
														continue  # we need to skip steps until we reach the resumed step
							lowerCamelCase_  :       List[Any]					      =     model(batch, labels=batch, use_cache=False).loss
							lowerCamelCase_  :       Dict					      =     accelerator.gather(loss.repeat(args.train_batch_size)).mean()
							loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
							log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
							lowerCamelCase_  :       int					      =     loss / args.gradient_accumulation_steps
							if step % args.gradient_accumulation_steps != 0:
														# Prevent backward from doing gradient all_reduce in every step
														if accelerator.distributed_type == DistributedType.MULTI_GPU:
																					with model.no_sync():
																												accelerator.backward(loss)
														else:
																					accelerator.backward(loss)
							else:
														lowerCamelCase_  :       Optional[int]					      =     get_lr()
														accelerator.backward(loss)
														accelerator.clip_grad_norm_(model.parameters(), 1.0)
														optimizer.step()
														lr_scheduler.step()
														optimizer.zero_grad()
														lowerCamelCase_  :       int					      =     time.time() - t_start
														lowerCamelCase_  :       Union[str, Any]					      =     compute_tflops(elapsed_time, accelerator, args)
														log_metrics(
														    step,
														    {
														        """steps""": completed_steps,
														        """loss/train""": loss_tracking,
														        """lr""": lr,
														        """tflops""": tflops,
														        """time_per_iteration""": elapsed_time,
														    },
														)
														lowerCamelCase_  :       Optional[Any]					      =     time.time()
														lowerCamelCase_  :       Union[str, Any]					      =     0
														completed_steps += 1
							if step % args.save_checkpoint_steps == 0:
														logger.info("""Evaluating and saving model checkpoint""")
														lowerCamelCase_      ,				lowerCamelCase_  :       Any					      =     evaluate(args)
														log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
														accelerator.wait_for_everyone()
														lowerCamelCase_  :       str					      =     os.path.join(args.save_dir, F"""step_{step}""")
														accelerator.save_state(save_dir)
														if accelerator.is_main_process:
																					hf_repo.push_to_hub(commit_message=F"""step {step}""")
														model.train()
							if completed_steps >= args.max_train_steps:
														break

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCamelCase_      ,				lowerCamelCase_  :       Optional[int]					      =     evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
lowerCamelCase_  :       Any					      =     accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
lowerCamelCase_  :       List[str]					      =     os.path.join(args.save_dir, F"""step_{step}""")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
							hf_repo.push_to_hub(commit_message="""final model""")








import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
lowerCamelCase_  :       int					      =     logging.get_logger(__name__)

lowerCamelCase_  :       Optional[int]					      =     {
    """post_extract_proj""": """feature_projection.projection""",
    """models.0.layer_norm""": """feature_projection.layer_norm""",
    """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_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""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCamelCase_  :       Any					      =     [
    """lm_head""",
]







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		List[Any]:
				for attribute in key.split("""."""  ):
								UpperCamelCase_:							List[str]		=  getattr(lowerCamelCase   ,			lowerCamelCase  )

				if weight_type is not None:
								UpperCamelCase_:							Tuple		=  getattr(lowerCamelCase   ,			lowerCamelCase  ).shape
				else:
								UpperCamelCase_:							List[Any]		=  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_:							str		=  value
				elif weight_type == "weight_g":
								UpperCamelCase_:							List[str]		=  value
				elif weight_type == "weight_v":
								UpperCamelCase_:							Tuple		=  value
				elif weight_type == "bias":
								UpperCamelCase_:							Any		=  value
				else:
								UpperCamelCase_:							Dict		=  value

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







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Union[str, Any]:
				UpperCamelCase_:							Optional[int]		=  []
				UpperCamelCase_:							Tuple		=  fairseq_model.state_dict()

				if not is_headless:
								UpperCamelCase_:							Dict		=  hf_model.dataavec_audio.feature_extractor
								UpperCamelCase_:							Tuple		=  hf_model.dataavec_audio.encoder.pos_conv_embed

				else:
								UpperCamelCase_:							Tuple		=  hf_model.feature_extractor
								UpperCamelCase_:							int		=  hf_model.encoder.pos_conv_embed

				for name, value in fairseq_dict.items():
								UpperCamelCase_:							Union[str, Any]		=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								elif "pos_conv" in name:
												load_pos_conv_layer(
												    lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			)
												UpperCamelCase_:							Optional[Any]		=  True
								else:
												for key, mapped_key in MAPPING.items():
																if not is_headless:
																				UpperCamelCase_:							List[str]		=  """data2vec_audio.""" + 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_:							str		=  True
																				if "*" in mapped_key:
																								UpperCamelCase_:							Any		=  name.split(lowerCamelCase  )[0].split("""."""  )[-2]
																								UpperCamelCase_:							int		=  mapped_key.replace("""*"""   ,			lowerCamelCase  )
																				if "weight_g" in name:
																								UpperCamelCase_:							Tuple		=  """weight_g"""
																				elif "weight_v" in name:
																								UpperCamelCase_:							List[Any]		=  """weight_v"""
																				elif "bias" in name:
																								UpperCamelCase_:							Union[str, Any]		=  """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								UpperCamelCase_:							str		=  """weight"""
																				else:
																								UpperCamelCase_:							Tuple		=  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 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				UpperCamelCase_:							Union[str, Any]		=  path.split("""."""  )
				return reduce(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		str:
				UpperCamelCase_:							Union[str, Any]		=  access_by_string(lowerCamelCase   ,			lowerCamelCase  )
				UpperCamelCase_:							str		=  hf_weight.data

				if fsq_value.shape != hf_value.shape:
								raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
				UpperCamelCase_:							Optional[int]		=  fsq_value
				logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[int]:
				UpperCamelCase_:							str		=  full_name.split("""conv_layers."""  )[-1]
				UpperCamelCase_:							Union[str, Any]		=  name.split("""."""  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[0]  )
				UpperCamelCase_:							Union[str, Any]		=  int(items[1]  )

				UpperCamelCase_:							Tuple		=  name.split("""."""  )[-1]
				if type_id == 0:
								UpperCamelCase_:							List[Any]		=  """conv"""
				elif type_id == 2:
								UpperCamelCase_:							Tuple		=  """layer_norm"""
				else:
								unused_weights.append(lowerCamelCase  )
								return

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )







def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  full_name.split("""pos_conv."""  )[-1]
				UpperCamelCase_:							Any		=  name.split("""."""  )
				UpperCamelCase_:							List[str]		=  int(items[0]  )
				UpperCamelCase_:							str		=  int(items[1]  )

				UpperCamelCase_:							List[str]		=  name.split("""."""  )[-1]
				if type_id != 0:
								unused_weights.append(lowerCamelCase  )
								return
				else:
								UpperCamelCase_:							Tuple		=  """conv"""

				set_weights(lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			F'''layers.{layer_id}.{layer_type}.{weight_type}'''  )







@torch.no_grad()
def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase=None   ,			lowerCamelCase=None   ,			lowerCamelCase=True  )     ->		Dict:
				if config_path is not None:
								UpperCamelCase_:							str		=  DataaVecAudioConfig.from_pretrained(lowerCamelCase  )
				else:
								UpperCamelCase_:							Tuple		=  DataaVecAudioConfig()

				if not is_finetuned:
								# Modify final_proj layer name
								UpperCamelCase_:							Union[str, Any]		=  DataaVecAudioModel(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  os.path.dirname(lowerCamelCase  )

								UpperCamelCase_:							Optional[Any]		=  torch.load(lowerCamelCase  )
								UpperCamelCase_:							List[str]		=  state_dict["""model"""].pop("""final_proj.0.weight"""  )
								UpperCamelCase_:							int		=  state_dict["""model"""].pop("""final_proj.0.bias"""  )
								UpperCamelCase_:							Optional[int]		=  os.path.join(lowerCamelCase   ,			"""converted.pt"""  )
								torch.save(lowerCamelCase   ,			lowerCamelCase  )
				else:
								UpperCamelCase_:							int		=  DataaVecAudioForCTC(lowerCamelCase  )
								UpperCamelCase_:							Optional[int]		=  checkpoint_path

				def load_dataavec(lowerCamelCase  ):
								UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
								return model[0].eval()

				UpperCamelCase_:							List[Any]		=  load_dataavec(lowerCamelCase  )

				recursively_load_weights(lowerCamelCase   ,			lowerCamelCase   ,			not is_finetuned  )

				UpperCamelCase_:							Tuple		=  WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

				UpperCamelCase_:							Dict		=  load_dataset("""patrickvonplaten/librispeech_asr_dummy"""   ,			"""clean"""   ,			split="""validation"""  )
				UpperCamelCase_:							Optional[Any]		=  [x["""array"""] for x in ds[:4]["""audio"""]]

				UpperCamelCase_:							List[Any]		=  processor(lowerCamelCase   ,			return_tensors="""pt"""   ,			padding=lowerCamelCase  )

				UpperCamelCase_:							str		=  inputs.input_values
				UpperCamelCase_:							Dict		=  inputs.attention_mask
				#    input_values = inputs.input_values[:, :-1]
				#    attention_mask = inputs.attention_mask[:, :-1]

				hf_wavavec.eval()
				model.eval()
				if is_finetuned:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """encoder_out"""
								].transpose(0   ,			1  )
								UpperCamelCase_:							Tuple		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""logits"""]

								UpperCamelCase_:							Optional[Any]		=  torch.argmax(lowerCamelCase   ,			dim=-1  )
								UpperCamelCase_:							Any		=  processor.batch_decode(lowerCamelCase  )

								print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
				else:
								UpperCamelCase_:							Optional[Any]		=  model(source=lowerCamelCase   ,			padding_mask=(1 - attention_mask)   ,			mask=lowerCamelCase   ,			features_only=lowerCamelCase  )[
								    """layer_results"""
								][-1][0].transpose(0   ,			1  )
								UpperCamelCase_:							List[str]		=  hf_wavavec(lowerCamelCase   ,			attention_mask=lowerCamelCase  )["""last_hidden_state"""]

				print(our_output.shape   ,			their_output.shape  )
				UpperCamelCase_:							Optional[Any]		=  torch.max(torch.abs(our_output - their_output  )  ).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
				UpperCamelCase_:							List[str]		=  torch.allclose(lowerCamelCase   ,			lowerCamelCase   ,			atol=1E-3  )
				print("""Do both models output the same tensors?"""   ,			"""🔥""" if success else """💩"""  )
				if not success:
								raise Exception("""Something went wRoNg"""  )

				hf_wavavec.save_pretrained(lowerCamelCase  )

				if is_finetuned:
								processor.save_pretrained(lowerCamelCase  )
				else:
								processor.feature_extractor.save_pretrained(lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :       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("""--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"""
							)
							lowerCamelCase_  :       Dict					      =     parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)






