






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()
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_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		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       List[Any]   ):
				for attribute in key.split('.'   ):
								A	 =      getattr(UpperCAmelCase,						UpperCAmelCase   )

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

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







def      snake_case		(							UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Optional[int]   ):
				A	 =      []
				A	 =      fairseq_model.state_dict()

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

				for name, value in fairseq_dict.items():
								A	 =      False
								if "conv_layers" in name:
												load_conv_layer(
												    UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						hf_model.config.feat_extract_norm == 'group',						)
												A	 =      True
								else:
												for key, mapped_key in MAPPING.items():
																A	 =      '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):
																				A	 =      True
																				if "*" in mapped_key:
																								A	 =      name.split(UpperCAmelCase   )[0].split('.'   )[-2]
																								A	 =      mapped_key.replace('*',						UpperCAmelCase   )
																				if "weight_g" in name:
																								A	 =      'weight_g'
																				elif "weight_v" in name:
																								A	 =      'weight_v'
																				elif "weight" in name:
																								A	 =      'weight'
																				elif "bias" in name:
																								A	 =      'bias'
																				else:
																								A	 =      None
																				set_recursively(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )
																continue
								if not is_used:
												unused_weights.append(UpperCAmelCase   )

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







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







@torch.no_grad()
def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Optional[int]=None,						UpperCAmelCase	:       int=None,						UpperCAmelCase	:       Union[str, Any]=True   ):
				if config_path is not None:
								A	 =      HubertConfig.from_pretrained(UpperCAmelCase   )
				else:
								A	 =      HubertConfig()

				if is_finetuned:
								if dict_path:
												A	 =      Dictionary.load(UpperCAmelCase   )

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

								A	 =      HubertForCTC(UpperCAmelCase   )
				else:
								A	 =      HubertModel(UpperCAmelCase   )

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

				A	 =      model[0].eval()

				recursively_load_weights(UpperCAmelCase,						UpperCAmelCase,						UpperCAmelCase   )

				hf_wavavec.save_pretrained(UpperCAmelCase   )


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('--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'
					)
					lowerCAmelCase_           = 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 os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										lowerCAmelCase_           = '\n'.join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')





