





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__  :     str   			=logging.get_logger(__name__)

UpperCAmelCase__  :     Optional[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',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Union[str, Any]:
				for attribute in key.split("""."""			):
								lowerCamelCase			=getattr(_A     , _A			)

				if weight_type is not None:
								lowerCamelCase			=getattr(_A     , _A			).shape
				else:
								lowerCamelCase			=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			=value
				elif weight_type == "weight_g":
								lowerCamelCase			=value
				elif weight_type == "weight_v":
								lowerCamelCase			=value
				elif weight_type == "bias":
								lowerCamelCase			=value
				else:
								lowerCamelCase			=value

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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Tuple:
				lowerCamelCase			=[]
				lowerCamelCase			=fairseq_model.state_dict()

				lowerCamelCase			=hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCamelCase			=False
								if "conv_layers" in name:
												load_conv_layer(
												    _A     , _A     , _A     , _A     , hf_model.config.feat_extract_norm == """group"""     , )
												lowerCamelCase			=True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCamelCase			='hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																if key in name or (key.split("""w2v_model."""			)[-1] == name.split("""."""			)[0] and not is_finetuned):
																				lowerCamelCase			=True
																				if "*" in mapped_key:
																								lowerCamelCase			=name.split(_A			)[0].split("""."""			)[-2]
																								lowerCamelCase			=mapped_key.replace("""*"""     , _A			)
																				if "weight_g" in name:
																								lowerCamelCase			='weight_g'
																				elif "weight_v" in name:
																								lowerCamelCase			='weight_v'
																				elif "weight" in name:
																								lowerCamelCase			='weight'
																				elif "bias" in name:
																								lowerCamelCase			='bias'
																				else:
																								lowerCamelCase			=None
																				set_recursively(_A     , _A     , _A     , _A     , _A			)
																continue
								if not is_used:
												unused_weights.append(_A			)

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





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				lowerCamelCase			=full_name.split("""conv_layers."""			)[-1]
				lowerCamelCase			=name.split("""."""			)
				lowerCamelCase			=int(items[0]			)
				lowerCamelCase			=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			=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			=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			=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			=value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
				else:
								unused_weights.append(_A			)





@torch.no_grad()
def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase=None     , _UpperCAmelCase=None     , _UpperCAmelCase=True			)  ->					str:
				if config_path is not None:
								lowerCamelCase			=HubertConfig.from_pretrained(_A			)
				else:
								lowerCamelCase			=HubertConfig()

				if is_finetuned:
								if dict_path:
												lowerCamelCase			=Dictionary.load(_A			)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCamelCase			=target_dict.pad_index
												lowerCamelCase			=target_dict.bos_index
												lowerCamelCase			=target_dict.eos_index
												lowerCamelCase			=len(target_dict.symbols			)
												lowerCamelCase			=os.path.join(_A     , """vocab.json"""			)
												if not os.path.isdir(_A			):
																logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_A			)			)
																return
												os.makedirs(_A     , exist_ok=_A			)
												with open(_A     , """w"""     , encoding="""utf-8"""			) as vocab_handle:
																json.dump(target_dict.indices     , _A			)
												lowerCamelCase			=WavaVecaCTCTokenizer(
												    _A     , 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=_A     , )
												lowerCamelCase			=True if config.feat_extract_norm == 'layer' else False
												lowerCamelCase			=WavaVecaFeatureExtractor(
												    feature_size=1     , sampling_rate=1_60_00     , padding_value=0     , do_normalize=_A     , return_attention_mask=_A     , )
												lowerCamelCase			=WavaVecaProcessor(feature_extractor=_A     , tokenizer=_A			)
												processor.save_pretrained(_A			)

								lowerCamelCase			=HubertForCTC(_A			)
				else:
								lowerCamelCase			=HubertModel(_A			)

				if is_finetuned:
								lowerCamelCase			=fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]     , arg_overrides={"""data""": """/""".join(dict_path.split("""/"""			)[:-1]			)}			)
				else:
								lowerCamelCase			=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

				lowerCamelCase			=model[0].eval()

				recursively_load_weights(_A     , _A     , _A			)

				hf_wavavec.save_pretrained(_A			)


if __name__ == "__main__":
		UpperCAmelCase__  :     Optional[int]   			=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__  :     Tuple   			=parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)












import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__  :     Union[str, Any]   			=logging.getLogger(__name__)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:
				return (preds == labels).mean()

@dataclass
class  __A       :
				__A								=			field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,   )




@dataclass
class  __A       :
				__A								=			field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()      )}      )
				__A								=			field(metadata={"""help""": """Should contain the data files for the task."""}      )
				__A								=			field(
				    default=1_28      ,   metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }      ,   )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )





def        _lowercase   (  )  ->					Optional[int]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowerCamelCase			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir			)
				    and os.listdir(training_args.output_dir			)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    """ --overwrite_output_dir to overcome."""			)

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     , datefmt="""%m/%d/%Y %H:%M:%S"""     , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     , )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""     , training_args.local_rank     , training_args.device     , training_args.n_gpu     , bool(training_args.local_rank != -1			)     , training_args.fpaa     , )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank			):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""     , _UpperCAmelCase			)

				# Set seed
				set_seed(training_args.seed			)

				try:
								lowerCamelCase			=processors[data_args.task_name]()
								lowerCamelCase			=processor.get_labels()
								lowerCamelCase			=len(_UpperCAmelCase			)
				except KeyError:
								raise ValueError("""Task not found: %s""" % (data_args.task_name)			)

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowerCamelCase			=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path     , num_labels=_UpperCAmelCase     , finetuning_task=data_args.task_name     , cache_dir=model_args.cache_dir     , )
				lowerCamelCase			=AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , )
				lowerCamelCase			=AutoModelForMultipleChoice.from_pretrained(
				    model_args.model_name_or_path     , from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)     , config=_UpperCAmelCase     , cache_dir=model_args.cache_dir     , )

				# Get datasets
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , task=data_args.task_name     , max_seq_length=data_args.max_seq_length     , overwrite_cache=data_args.overwrite_cache     , mode=Split.train     , )
				    if training_args.do_train
				    else None
				)
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , task=data_args.task_name     , max_seq_length=data_args.max_seq_length     , overwrite_cache=data_args.overwrite_cache     , mode=Split.dev     , )
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(_UpperCAmelCase			) -> Dict:
								lowerCamelCase			=np.argmax(p.predictions     , axis=1			)
								return {"acc": simple_accuracy(_UpperCAmelCase     , p.label_ids			)}

				# Data collator
				lowerCamelCase			=DataCollatorWithPadding(_UpperCAmelCase     , pad_to_multiple_of=8			) if training_args.fpaa else None

				# Initialize our Trainer
				lowerCamelCase			=Trainer(
				    model=_UpperCAmelCase     , args=_UpperCAmelCase     , train_dataset=_UpperCAmelCase     , eval_dataset=_UpperCAmelCase     , compute_metrics=_UpperCAmelCase     , data_collator=_UpperCAmelCase     , )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
				lowerCamelCase			={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""			)

								lowerCamelCase			=trainer.evaluate()

								lowerCamelCase			=os.path.join(training_args.output_dir     , """eval_results.txt"""			)
								if trainer.is_world_master():
												with open(_UpperCAmelCase     , """w"""			) as writer:
																logger.info("""***** Eval results *****"""			)
																for key, value in result.items():
																				logger.info("""  %s = %s"""     , _UpperCAmelCase     , _UpperCAmelCase			)
																				writer.write("""%s = %s\n""" % (key, value)			)

																results.update(_UpperCAmelCase			)

				return results





def        _lowercase   (  _UpperCAmelCase			)  ->					Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()





