




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

lowercase__     						=						{
    """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      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				for attribute in key.split('.'		):
								_lowerCamelCase     :			Optional[int]    =    getattr(lowercase__							,    lowercase__		)

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

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    []
				_lowerCamelCase     :			Optional[int]    =    fairseq_model.state_dict()

				_lowerCamelCase     :			List[Any]    =    hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								_lowerCamelCase     :			Any    =    False
								if "conv_layers" in name:
												load_conv_layer(
												    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    hf_model.config.feat_extract_norm == 'group'							,    )
												_lowerCamelCase     :			List[Any]    =    True
								else:
												for key, mapped_key in MAPPING.items():
																_lowerCamelCase     :			Dict    =    '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     :			Tuple    =    True
																				if "*" in mapped_key:
																								_lowerCamelCase     :			Optional[int]    =    name.split(lowercase__		)[0].split('.'		)[-2]
																								_lowerCamelCase     :			Union[str, Any]    =    mapped_key.replace('*'							,    lowercase__		)
																				if "weight_g" in name:
																								_lowerCamelCase     :			Optional[int]    =    'weight_g'
																				elif "weight_v" in name:
																								_lowerCamelCase     :			Tuple    =    'weight_v'
																				elif "weight" in name:
																								_lowerCamelCase     :			str    =    'weight'
																				elif "bias" in name:
																								_lowerCamelCase     :			Any    =    'bias'
																				else:
																								_lowerCamelCase     :			Tuple    =    None
																				set_recursively(lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)
																continue
								if not is_used:
												unused_weights.append(lowercase__		)

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







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			int    =    full_name.split('conv_layers.'		)[-1]
				_lowerCamelCase     :			List[str]    =    name.split('.'		)
				_lowerCamelCase     :			Tuple    =    int(items[0]		)
				_lowerCamelCase     :			Union[str, Any]    =    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[str]    =    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     :			int    =    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[Any]    =    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     :			List[str]    =    value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
				else:
								unused_weights.append(lowercase__		)







@torch.no_grad()
def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__=None							,    lowercase__=None							,    lowercase__=True		):
				if config_path is not None:
								_lowerCamelCase     :			Dict    =    HubertConfig.from_pretrained(lowercase__		)
				else:
								_lowerCamelCase     :			Dict    =    HubertConfig()

				if is_finetuned:
								if dict_path:
												_lowerCamelCase     :			Any    =    Dictionary.load(lowercase__		)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												_lowerCamelCase     :			Any    =    target_dict.pad_index
												_lowerCamelCase     :			int    =    target_dict.bos_index
												_lowerCamelCase     :			int    =    target_dict.eos_index
												_lowerCamelCase     :			str    =    len(target_dict.symbols		)
												_lowerCamelCase     :			str    =    os.path.join(lowercase__							,    'vocab.json'		)
												if not os.path.isdir(lowercase__		):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase__		)		)
																return
												os.makedirs(lowercase__							,    exist_ok=lowercase__		)
												with open(lowercase__							,    'w'							,    encoding='utf-8'		) as vocab_handle:
																json.dump(target_dict.indices							,    lowercase__		)
												_lowerCamelCase     :			Optional[Any]    =    WavaVecaCTCTokenizer(
												    lowercase__							,    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=lowercase__							,    )
												_lowerCamelCase     :			Tuple    =    True if config.feat_extract_norm == 'layer' else False
												_lowerCamelCase     :			Optional[Any]    =    WavaVecaFeatureExtractor(
												    feature_size=1							,    sampling_rate=16000							,    padding_value=0							,    do_normalize=lowercase__							,    return_attention_mask=lowercase__							,    )
												_lowerCamelCase     :			Tuple    =    WavaVecaProcessor(feature_extractor=lowercase__							,    tokenizer=lowercase__		)
												processor.save_pretrained(lowercase__		)

								_lowerCamelCase     :			str    =    HubertForCTC(lowercase__		)
				else:
								_lowerCamelCase     :			Optional[int]    =    HubertModel(lowercase__		)

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

				_lowerCamelCase     :			Any    =    model[0].eval()

				recursively_load_weights(lowercase__							,    lowercase__							,    lowercase__		)

				hf_wavavec.save_pretrained(lowercase__		)


if __name__ == "__main__":
			lowercase__     						=						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"""
			)
			lowercase__     						=						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"""






def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				if len(lowercase__		) == 1:
								return True
				_lowerCamelCase     :			List[Any]    =    series[1] - series[0]
				for index in range(len(lowercase__		) - 1		):
								if series[index + 1] - series[index] != common_diff:
												return False
				return True







def      _snake_case			(				lowercase__		):
				if not isinstance(lowercase__							,    lowercase__		):
								raise ValueError('Input series is not valid, valid series - [2, 4, 6]'		)
				if len(lowercase__		) == 0:
								raise ValueError('Input list must be a non empty list'		)
				_lowerCamelCase     :			Optional[int]    =    0
				for val in series:
								answer += val
				return answer / len(lowercase__		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()