



'''simple docstring'''



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()
snake_case	 =       logging.get_logger(__name__)

snake_case	 =       {
    """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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							for attribute in key.split('.'					):
														__lowercase       	=getattr(_lowerCAmelCase  ,   _lowerCAmelCase					)

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

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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=[]
							__lowercase       	=fairseq_model.state_dict()

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

							for name, value in fairseq_dict.items():
														__lowercase       	=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   hf_model.config.feat_extract_norm == 'group'  ,   )
																					__lowercase       	=True
														else:
																					for key, mapped_key in MAPPING.items():
																												__lowercase       	="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):
																																			__lowercase       	=True
																																			if "*" in mapped_key:
																																										__lowercase       	=name.split(_lowerCAmelCase					)[0].split('.'					)[-2]
																																										__lowercase       	=mapped_key.replace('*'  ,   _lowerCAmelCase					)
																																			if "weight_g" in name:
																																										__lowercase       	="weight_g"
																																			elif "weight_v" in name:
																																										__lowercase       	="weight_v"
																																			elif "weight" in name:
																																										__lowercase       	="weight"
																																			elif "bias" in name:
																																										__lowercase       	="bias"
																																			else:
																																										__lowercase       	=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  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=full_name.split('conv_layers.'					)[-1]
							__lowercase       	=name.split('.'					)
							__lowercase       	=int(items[0]					)
							__lowercase       	=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."""
																					)
																					__lowercase       	=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."""
																					)
																					__lowercase       	=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."
																					)
																					__lowercase       	=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."""
																					)
																					__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase=True					):



							"""simple docstring"""



							if config_path is not None:
														__lowercase       	=HubertConfig.from_pretrained(_lowerCAmelCase					)
							else:
														__lowercase       	=HubertConfig()

							if is_finetuned:
														if dict_path:
																					__lowercase       	=Dictionary.load(_lowerCAmelCase					)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__lowercase       	=target_dict.pad_index
																					__lowercase       	=target_dict.bos_index
																					__lowercase       	=target_dict.eos_index
																					__lowercase       	=len(target_dict.symbols					)
																					__lowercase       	=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					)
																					with open(_lowerCAmelCase  ,   'w'  ,   encoding='utf-8'					) as vocab_handle:
																												json.dump(target_dict.indices  ,   _lowerCAmelCase					)
																					__lowercase       	=WavaVecaCTCTokenizer(
																					    _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  ,   )
																					__lowercase       	=True if config.feat_extract_norm == "layer" else False
																					__lowercase       	=WavaVecaFeatureExtractor(
																					    feature_size=1  ,   sampling_rate=16_000  ,   padding_value=0  ,   do_normalize=_lowerCAmelCase  ,   return_attention_mask=_lowerCAmelCase  ,   )
																					__lowercase       	=WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,   tokenizer=_lowerCAmelCase					)
																					processor.save_pretrained(_lowerCAmelCase					)

														__lowercase       	=HubertForCTC(_lowerCAmelCase					)
							else:
														__lowercase       	=HubertModel(_lowerCAmelCase					)

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

							__lowercase       	=model[0].eval()

							recursively_load_weights(_lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							hf_wavavec.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
		snake_case	 =       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	 =       parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)








'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


lowerCamelCase	 =       logging.get_logger(__name__)





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				def __init__(							self  :  Optional[int]  ,       *_lowerCAmelCase  :  Union[str, Any]  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											warnings.warn(
											    'The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
											    ' use DeiTImageProcessor instead.'  ,       _lowerCAmelCase  ,       )
											super().__init__(*_lowerCAmelCase  ,       **_lowerCAmelCase)



