



import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


__snake_case       :	Any            =logging.getLogger(__name__)

__snake_case       :	List[str]            ='pytorch_model.bin'



@dataclasses.dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=dataclasses.field(
		    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models."""})
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co."""}   ,       )



@dataclasses.dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=dataclasses.field(metadata={"""help""": """A csv or a json file containing the training data."""})
		snake_case_			=dataclasses.field(metadata={"""help""": """A csv or a json file containing the data to predict on."""})
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """A csv or a json file containing the validation data."""})
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """The name of the task to train on."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """The list of labels for the task."""})



@dataclasses.dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=dataclasses.field(
		    metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""})
		snake_case_			=dataclasses.field(
		    default="""accuracy"""   ,       metadata={"""help""": """The evaluation metric used for the task."""})
		snake_case_			=dataclasses.field(
		    default="""no"""   ,       metadata={
		        """help""": """The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"""
		    }   ,       )
		snake_case_			=dataclasses.field(
		    default=10   ,       metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=0.0   ,       metadata={
		        """help""": """How much the specified evaluation metric must improve to satisfy early stopping conditions."""
		    }   ,       )
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """Whether to filter the pseudo-labeled data based on the confidence score."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """Whether to filter the pseudo-labeled data based on the validation performance."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """Whether to fine-tune on labeled data after pseudo training."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=0.0   ,       metadata={"""help""": """Confidence threshold for pseudo-labeled data filtering."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=100   ,       metadata={"""help""": """Number of evaluation calls with no improvement after which training will be stopped."""}   ,       )
		snake_case_			=dataclasses.field(
		    default=lowerCamelCase__   ,       metadata={"""help""": """Random seed for initialization."""}   ,       )






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Union[str, Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[int]				=  datasets.concatenate_datasets([infer_input, infer_output] ,axis=1)

				if args.do_filter_by_confidence:
								lowerCAmelCase__  :       Union[str, Any]				=  dataset.filter(lambda lowerCamelCase_: example["probability"] > args.confidence_threshold)

				if args.do_filter_by_val_performance:
								assert eval_result >= 0.0 and eval_result <= 1.0
								lowerCAmelCase__  :       List[str]				=  int(eval_result * len(lowerCamelCase_))
								print(lowerCamelCase_)
								lowerCAmelCase__  :       Any				=  dataset.sort('''probability''' ,reverse=lowerCamelCase_)
								lowerCAmelCase__  :       Any				=  dataset.select(range(lowerCamelCase_))

				lowerCAmelCase__  :       Tuple				=  dataset.remove_columns(['''label''', '''probability'''])
				lowerCAmelCase__  :       str				=  dataset.rename_column('''prediction''' ,'''label''')
				lowerCAmelCase__  :       List[str]				=  dataset.map(lambda lowerCamelCase_: {"label": idalabel[example["label"]]})
				lowerCAmelCase__  :       Dict				=  dataset.shuffle(seed=args.seed)

				lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(lowerCamelCase_ ,f"""train_pseudo.{args.data_file_extension}""")
				if args.data_file_extension == "csv":
								dataset.to_csv(lowerCamelCase_ ,index=lowerCamelCase_)
				else:
								dataset.to_json(lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	Any ,lowerCamelCase_	:	int ,**lowerCamelCase_	:	Any):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  Accelerator()
				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,datefmt='''%m/%d/%Y %H:%M:%S''' ,level=logging.INFO ,)
				logger.info(accelerator.state)

				# Setup logging, we only want one process per machine to log things on the
				# screen. accelerator.is_local_main_process is only True for one process per
				# machine.
				logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)

				if accelerator.is_local_main_process:
								datasets.utils.logging.set_verbosity_warning()
								transformers.utils.logging.set_verbosity_info()
				else:
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()

				lowerCAmelCase__  :       Tuple				=  STModelArguments(model_name_or_path=lowerCamelCase_)
				lowerCAmelCase__  :       Tuple				=  STDataArguments(train_file=lowerCamelCase_ ,infer_file=lowerCamelCase_)
				lowerCAmelCase__  :       Any				=  STTrainingArguments(output_dir=lowerCamelCase_)
				lowerCAmelCase__  :       Optional[int]				=  argparse.Namespace()

				for arg_class in (model_args, data_args, training_args):
								for key, value in vars(lowerCamelCase_).items():
												setattr(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				for key, value in kwargs.items():
								if hasattr(lowerCamelCase_ ,lowerCamelCase_):
												setattr(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

    # Sanity checks
				lowerCAmelCase__  :       str				=  {}
				lowerCAmelCase__  :       Optional[int]				=  None

				# You need to provide the training data and the data to predict on
				assert args.train_file is not None
				assert args.infer_file is not None
				lowerCAmelCase__  :       List[Any]				=  args.train_file
				lowerCAmelCase__  :       int				=  args.infer_file

				if args.evaluation_strategy != IntervalStrategy.NO.value:
								assert args.eval_file is not None
								lowerCAmelCase__  :       str				=  args.eval_file

				for key in data_files:
								lowerCAmelCase__  :       List[Any]				=  data_files[key].split('''.''')[-1]
								assert extension in ["csv", "json"], f"""`{key}_file` should be a csv or a json file."""
								if args.data_file_extension is None:
												lowerCAmelCase__  :       Optional[int]				=  extension
								else:
												assert extension == args.data_file_extension, f"""`{key}_file` should be a {args.data_file_extension} file`."""

				assert (
				    args.eval_metric in datasets.list_metrics()
				), f"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

				# If passed along, set the training seed now.
				if args.seed is not None:
								set_seed(args.seed)

				logger.info('''Creating the initial data directory for self-training...''')
				lowerCAmelCase__  :       str				=  f"""{args.output_dir}/self-train_iter-{{}}""".format
				lowerCAmelCase__  :       Union[str, Any]				=  data_dir_format(0)

				if accelerator.is_main_process:
								if args.output_dir is not None:
												os.makedirs(args.output_dir ,exist_ok=lowerCamelCase_)
												os.makedirs(lowerCamelCase_ ,exist_ok=lowerCamelCase_)
				accelerator.wait_for_everyone()

				lowerCAmelCase__  :       List[Any]				=  None
				lowerCAmelCase__  :       Union[str, Any]				=  None
				lowerCAmelCase__  :       Tuple				=  0
				lowerCAmelCase__  :       int				=  False
				# Show the progress bar
				lowerCAmelCase__  :       Dict				=  tqdm(range(args.max_selftrain_iterations) ,disable=not accelerator.is_local_main_process)

				# Self-train
				for iteration in range(0 ,int(args.max_selftrain_iterations)):
								lowerCAmelCase__  :       int				=  data_dir_format(lowerCamelCase_)
								assert os.path.exists(lowerCamelCase_)

								# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
								# iteration > 0
								lowerCAmelCase__  :       Optional[int]				=  os.path.join(lowerCamelCase_ ,'''stage-1''')
								lowerCAmelCase__  :       List[Any]				=  {
								    '''accelerator''': accelerator,
								    '''model_name_or_path''': args.model_name_or_path,
								    '''cache_dir''': args.cache_dir,
								    '''do_train''': True,
								    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
								    '''do_eval''': True if args.eval_file is not None else False,
								    '''eval_file''': data_files['''eval'''],
								    '''do_predict''': True,
								    '''infer_file''': data_files['''infer'''],
								    '''task_name''': args.task_name,
								    '''label_list''': args.label_list,
								    '''output_dir''': current_output_dir,
								    '''eval_metric''': args.eval_metric,
								    '''evaluation_strategy''': args.evaluation_strategy,
								    '''early_stopping_patience''': args.early_stopping_patience,
								    '''early_stopping_threshold''': args.early_stopping_threshold,
								    '''seed''': args.seed,
								}
								# Add additional training arguments
								for key, value in kwargs.items():
												if key not in arguments_dict and not hasattr(lowerCamelCase_ ,lowerCamelCase_):
																arguments_dict.update({key: value})

								lowerCAmelCase__  :       Dict				=  os.path.join(lowerCamelCase_ ,'''best-checkpoint''' ,lowerCamelCase_)
								if os.path.exists(lowerCamelCase_):
												logger.info(
												    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''' ,lowerCamelCase_ ,lowerCamelCase_ ,)
								else:
												logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''' ,lowerCamelCase_)
												finetune(**lowerCamelCase_)
												accelerator.wait_for_everyone()
												assert os.path.exists(lowerCamelCase_)
												logger.info('''Self-training job completed: iteration: %d, stage: 1.''' ,lowerCamelCase_)

								if iteration > 0 and args.finetune_on_labeled_data:
												# Stage 2 (optional): fine-tuning on the original labeled data
												lowerCAmelCase__  :       str				=  os.path.join(lowerCamelCase_ ,'''best-checkpoint''')
												lowerCAmelCase__  :       List[str]				=  os.path.join(lowerCamelCase_ ,'''stage-2''')
												# Update arguments_dict
												lowerCAmelCase__  :       Optional[Any]				=  model_path
												lowerCAmelCase__  :       List[str]				=  data_files['''train''']
												lowerCAmelCase__  :       Tuple				=  current_output_dir

												lowerCAmelCase__  :       List[str]				=  os.path.join(lowerCamelCase_ ,'''best-checkpoint''' ,lowerCamelCase_)
												if os.path.exists(lowerCamelCase_):
																logger.info(
																    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''' ,lowerCamelCase_ ,lowerCamelCase_ ,)
												else:
																logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''' ,lowerCamelCase_)
																finetune(**lowerCamelCase_)
																accelerator.wait_for_everyone()
																assert os.path.exists(lowerCamelCase_)
																logger.info('''Self-training job completed: iteration: %d, stage: 2.''' ,lowerCamelCase_)

								lowerCAmelCase__  :       List[str]				=  iteration
								lowerCAmelCase__  :       Dict				=  data_dir_format(iteration + 1)

								lowerCAmelCase__  :       Optional[Any]				=  AutoConfig.from_pretrained(os.path.join(lowerCamelCase_ ,'''best-checkpoint'''))
								lowerCAmelCase__  :       str				=  config.idalabel
								lowerCAmelCase__  :       int				=  os.path.join(lowerCamelCase_ ,'''eval_results_best-checkpoint.json''')
								lowerCAmelCase__  :       Any				=  os.path.join(lowerCamelCase_ ,'''test_results_best-checkpoint.json''')
								assert os.path.exists(lowerCamelCase_)

								with open(lowerCamelCase_ ,'''r''') as f:
												lowerCAmelCase__  :       List[str]				=  float(json.load(lowerCamelCase_)[args.eval_metric])
								lowerCAmelCase__  :       Optional[Any]				=  os.path.join(lowerCamelCase_ ,'''infer_output_best-checkpoint.csv''')
								assert os.path.exists(lowerCamelCase_)
								# Loading the dataset from local csv or json files.
								lowerCAmelCase__  :       Optional[int]				=  load_dataset(args.data_file_extension ,data_files={'''data''': data_files['''infer''']})['''data''']
								lowerCAmelCase__  :       Tuple				=  load_dataset('''csv''' ,data_files={'''data''': infer_output_file})['''data''']

								if accelerator.is_main_process:
												os.makedirs(lowerCamelCase_ ,exist_ok=lowerCamelCase_)
												shutil.copy(lowerCamelCase_ ,os.path.join(lowerCamelCase_ ,f"""eval_results_iter-{iteration}.json"""))
												if os.path.exists(lowerCamelCase_):
																shutil.copy(lowerCamelCase_ ,os.path.join(lowerCamelCase_ ,f"""test_results_iter-{iteration}.json"""))
												create_pseudo_labeled_data(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)
								accelerator.wait_for_everyone()

								lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(lowerCamelCase_ ,f"""train_pseudo.{args.data_file_extension}""")

								if args.evaluation_strategy != IntervalStrategy.NO.value:
												lowerCAmelCase__  :       Optional[Any]				=  eval_result

												if best_iteration is None:
																lowerCAmelCase__  :       Dict				=  new_iteration
																lowerCAmelCase__  :       Union[str, Any]				=  new_eval_result
												else:
																if new_eval_result - best_eval_result > args.early_stopping_threshold:
																				lowerCAmelCase__  :       List[str]				=  new_iteration
																				lowerCAmelCase__  :       Union[str, Any]				=  new_eval_result
																				lowerCAmelCase__  :       Optional[int]				=  0
																else:
																				if new_eval_result == best_eval_result:
																								lowerCAmelCase__  :       Dict				=  new_iteration
																								lowerCAmelCase__  :       Any				=  new_eval_result
																				early_stopping_patience_counter += 1

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				lowerCAmelCase__  :       Union[str, Any]				=  True

								progress_bar.update(1)

								if should_training_stop:
												break

				if best_iteration is not None:
								# Save the best iteration
								logger.info('''Best iteration: %d''' ,lowerCamelCase_)
								logger.info('''Best evaluation result: %s = %f''' ,args.eval_metric ,lowerCamelCase_)
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(lowerCamelCase_ ,f"""eval_results_iter-{iteration}.json""") ,os.path.join(lowerCamelCase_ ,'''eval_results_best-iteration.json''') ,)
				else:
								# Assume that the last iteration is the best
								logger.info('''Best iteration: %d''' ,args.max_selftrain_iterations - 1)
								logger.info('''Best evaluation result: %s = %f''' ,args.eval_metric ,lowerCamelCase_)
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(lowerCamelCase_ ,f"""eval_results_iter-{args.max_selftrain_iterations - 1}.json""") ,os.path.join(lowerCamelCase_ ,'''eval_results_best-iteration.json''') ,)






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case       :	Dict            =logging.get_logger(__name__)

__snake_case       :	Union[str, Any]            ={
    '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_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       :	Tuple            =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	int ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				for attribute in key.split('''.'''):
								if is_finetuned:
												if attribute in ["quantizer", "project_q", "project_hid"]:
																# those layers are only relevant for pretraining and should be dropped
																return

												if attribute == "ctc_proj":
																# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																lowerCAmelCase__  :       str				=  '''lm_head'''

								lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,lowerCamelCase_)

				if weight_type is not None:
								lowerCAmelCase__  :       Union[str, Any]				=  getattr(lowerCamelCase_ ,lowerCamelCase_).shape
				else:
								lowerCAmelCase__  :       Union[str, Any]				=  hf_pointer.shape

				assert hf_shape == value.shape, (
				    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    f""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								lowerCAmelCase__  :       str				=  value
				elif weight_type == "weight_g":
								lowerCAmelCase__  :       Any				=  value
				elif weight_type == "weight_v":
								lowerCAmelCase__  :       Tuple				=  value
				elif weight_type == "bias":
								lowerCAmelCase__  :       int				=  value
				else:
								lowerCAmelCase__  :       Tuple				=  value

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

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

				lowerCAmelCase__  :       str				=  hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCAmelCase__  :       Union[str, Any]				=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,hf_model.config.feat_extract_norm == '''group''' ,)
												lowerCAmelCase__  :       Tuple				=  True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCAmelCase__  :       List[str]				=  '''unispeech.''' + 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]:
																				lowerCAmelCase__  :       Dict				=  True
																				if "*" in mapped_key:
																								lowerCAmelCase__  :       Tuple				=  name.split(lowerCamelCase_)[0].split('''.''')[-2]
																								lowerCAmelCase__  :       str				=  mapped_key.replace('''*''' ,lowerCamelCase_)
																				if "weight_g" in name:
																								lowerCAmelCase__  :       List[str]				=  '''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase__  :       Tuple				=  '''weight_v'''
																				elif "bias" in name:
																								lowerCAmelCase__  :       Any				=  '''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCAmelCase__  :       Union[str, Any]				=  '''weight'''
																				else:
																								lowerCAmelCase__  :       Dict				=  None
																				set_recursively(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase_)

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






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  full_name.split('''conv_layers.''')[-1]
				lowerCAmelCase__  :       Tuple				=  name.split('''.''')
				lowerCAmelCase__  :       Any				=  int(items[0])
				lowerCAmelCase__  :       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."""
												)
												lowerCAmelCase__  :       List[Any]				=  value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												lowerCAmelCase__  :       List[Any]				=  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."
												)
												lowerCAmelCase__  :       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."""
												)
												lowerCAmelCase__  :       str				=  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 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	str=None ,lowerCamelCase_	:	List[Any]=None ,lowerCamelCase_	:	List[str]=True):





				'''simple docstring'''

				if config_path is not None:
								lowerCAmelCase__  :       Optional[Any]				=  UniSpeechConfig.from_pretrained(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[str]				=  UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowerCAmelCase__  :       Dict				=  Dictionary.load_from_json(lowerCamelCase_)

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

												# fairseq has the <pad> and <s> switched
												lowerCAmelCase__  :       Optional[int]				=  42
												lowerCAmelCase__  :       List[Any]				=  43
												with open(lowerCamelCase_ ,'''w''' ,encoding='''utf-8''') as vocab_handle:
																json.dump(lowerCamelCase_ ,lowerCamelCase_)
												lowerCAmelCase__  :       Dict				=  WavaVecaPhonemeCTCTokenizer(
												    lowerCamelCase_ ,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=lowerCamelCase_ ,)
												lowerCAmelCase__  :       Optional[Any]				=  True if config.feat_extract_norm == '''layer''' else False
												lowerCAmelCase__  :       Tuple				=  WavaVecaFeatureExtractor(
												    feature_size=1 ,sampling_rate=16000 ,padding_value=0 ,do_normalize=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,)
												lowerCAmelCase__  :       Any				=  WavaVecaProcessor(feature_extractor=lowerCamelCase_ ,tokenizer=lowerCamelCase_)
												processor.save_pretrained(lowerCamelCase_)

								lowerCAmelCase__  :       Union[str, Any]				=  UniSpeechForCTC(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[Any]				=  UniSpeechForPreTraining(lowerCamelCase_)

				if is_finetuned:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
				else:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

				lowerCAmelCase__  :       Dict				=  model[0].eval()

				recursively_load_weights(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				hf_unispeech.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	Optional[Any]            =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'
							)
							__snake_case       :	List[Any]            =parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)

