






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

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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	for attribute in key.split("""."""       ):
		A_		:    Tuple						     = getattr(snake_case__ , snake_case__       )

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

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






def       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	A_		:    str						     = []
	A_		:    str						     = fairseq_model.state_dict()

	A_		:    int						     = hf_model.feature_extractor

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

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

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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Tuple						     = full_name.split("""conv_layers."""       )[-1]
	A_		:    Optional[int]						     = name.split("""."""       )
	A_		:    List[Any]						     = int(items[0]       )
	A_		:    List[str]						     = 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_		:    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."""
			)
			A_		:    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_		:    List[str]						     = 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(snake_case__       )






@torch.no_grad()
def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__=None       ):
	# load the pre-trained checkpoints
	A_		:    List[Any]						     = torch.load(snake_case__       )
	A_		:    Dict						     = WavLMConfigOrig(checkpoint["""cfg"""]       )
	A_		:    Union[str, Any]						     = WavLMOrig(snake_case__       )
	model.load_state_dict(checkpoint["""model"""]       )
	model.eval()

	if config_path is not None:
		A_		:    List[str]						     = WavLMConfig.from_pretrained(snake_case__       )
	else:
		A_		:    Union[str, Any]						     = WavLMConfig()

	A_		:    Tuple						     = WavLMModel(snake_case__       )

	recursively_load_weights(snake_case__ , snake_case__       )

	hf_wavlm.save_pretrained(snake_case__       )


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









"""simple docstring"""
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


_lowerCAmelCase       =				logging.getLogger(__name__)

_lowerCAmelCase       =				"pytorch_model.bin"



@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






@dataclasses.dataclass
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




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






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       ):
	A_		:    Tuple						     = datasets.concatenate_datasets([infer_input, infer_output] , axis=1       )

	if args.do_filter_by_confidence:
		A_		:    Tuple						     = dataset.filter(lambda snake_case__       : example["probability"] > args.confidence_threshold       )

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

	A_		:    int						     = dataset.remove_columns(["""label""", """probability"""]       )
	A_		:    Any						     = dataset.rename_column("""prediction""" , """label"""       )
	A_		:    str						     = dataset.map(lambda snake_case__       : {"label": idalabel[example["label"]]}       )
	A_		:    Optional[Any]						     = dataset.shuffle(seed=args.seed       )

	A_		:    Dict						     = os.path.join(snake_case__ , F"""train_pseudo.{args.data_file_extension}"""       )
	if args.data_file_extension == "csv":
		dataset.to_csv(snake_case__ , index=snake_case__       )
	else:
		dataset.to_json(snake_case__       )






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__       ):
	A_		:    Tuple						     = 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()

	A_		:    Optional[int]						     = STModelArguments(model_name_or_path=snake_case__       )
	A_		:    int						     = STDataArguments(train_file=snake_case__ , infer_file=snake_case__       )
	A_		:    Dict						     = STTrainingArguments(output_dir=snake_case__       )
	A_		:    Any						     = argparse.Namespace()

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

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

    # Sanity checks
	A_		:    Any						     = {}
	A_		:    List[Any]						     = 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
	A_		:    Any						     = args.train_file
	A_		:    Optional[Any]						     = args.infer_file

	if args.evaluation_strategy != IntervalStrategy.NO.value:
		assert args.eval_file is not None
		A_		:    Optional[Any]						     = args.eval_file

	for key in data_files:
		A_		:    Union[str, 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:
			A_		:    str						     = 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..."""       )
	A_		:    Tuple						     = F"""{args.output_dir}/self-train_iter-{{}}""".format
	A_		:    Optional[Any]						     = data_dir_format(0       )

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

	A_		:    Dict						     = None
	A_		:    Optional[int]						     = None
	A_		:    List[Any]						     = 0
	A_		:    List[Any]						     = False
	# Show the progress bar
	A_		:    Optional[int]						     = 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       )       ):
		A_		:    int						     = data_dir_format(snake_case__       )
		assert os.path.exists(snake_case__       )

		# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
		# iteration > 0
		A_		:    str						     = os.path.join(snake_case__ , """stage-1"""       )
		A_		:    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(snake_case__ , snake_case__       ):
				arguments_dict.update({key: value}       )

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

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

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

		A_		:    Optional[Any]						     = iteration
		A_		:    Optional[Any]						     = data_dir_format(iteration + 1       )

		A_		:    Optional[int]						     = AutoConfig.from_pretrained(os.path.join(snake_case__ , """best-checkpoint"""       )       )
		A_		:    str						     = config.idalabel
		A_		:    List[str]						     = os.path.join(snake_case__ , """eval_results_best-checkpoint.json"""       )
		A_		:    Dict						     = os.path.join(snake_case__ , """test_results_best-checkpoint.json"""       )
		assert os.path.exists(snake_case__       )

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

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

		A_		:    str						     = os.path.join(snake_case__ , F"""train_pseudo.{args.data_file_extension}"""       )

		if args.evaluation_strategy != IntervalStrategy.NO.value:
			A_		:    List[str]						     = eval_result

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

				if early_stopping_patience_counter >= args.early_stopping_patience:
					A_		:    str						     = 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""" , snake_case__       )
		logger.info("""Best evaluation result: %s = %f""" , args.eval_metric , snake_case__       )
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(snake_case__ , F"""eval_results_iter-{iteration}.json"""       ) , os.path.join(snake_case__ , """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 , snake_case__       )
		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
			shutil.copy(
			    os.path.join(snake_case__ , F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""       ) , os.path.join(snake_case__ , """eval_results_best-iteration.json"""       ) , )

