





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()
SCREAMING_SNAKE_CASE_   :      List[Any]			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Dict			=   {
    '''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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		for attribute in key.split('.'							):
				__lowercase											=     getattr(snake_case						,   snake_case							)

		if weight_type is not None:
				__lowercase											=     getattr(snake_case						,   snake_case							).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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		__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(
						    snake_case						,   snake_case						,   snake_case						,   snake_case						,   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(snake_case							)[0].split('.'							)[-2]
												__lowercase											=     mapped_key.replace('*'						,   snake_case							)
										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(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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    str:
		__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(snake_case							)



@torch.no_grad()
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case=None						,   snake_case=None						,   snake_case=True							)		->    Dict:
		if config_path is not None:
				__lowercase											=     HubertConfig.from_pretrained(snake_case							)
		else:
				__lowercase											=     HubertConfig()

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

						# 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(snake_case						,   'vocab.json'							)
						if not os.path.isdir(snake_case							):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case							)							)
								return
						os.makedirs(snake_case						,   exist_ok=snake_case							)
						with open(snake_case						,   'w'						,   encoding='utf-8'							) as vocab_handle:
								json.dump(target_dict.indices						,   snake_case							)
						__lowercase											=     WavaVecaCTCTokenizer(
						    snake_case						,   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=snake_case						,   )
						__lowercase											=     True if config.feat_extract_norm == 'layer' else False
						__lowercase											=     WavaVecaFeatureExtractor(
						    feature_size=1						,   sampling_rate=16_000						,   padding_value=0						,   do_normalize=snake_case						,   return_attention_mask=snake_case						,   )
						__lowercase											=     WavaVecaProcessor(feature_extractor=snake_case						,   tokenizer=snake_case							)
						processor.save_pretrained(snake_case							)

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

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

		__lowercase											=     model[0].eval()

		recursively_load_weights(snake_case						,   snake_case						,   snake_case							)

		hf_wavavec.save_pretrained(snake_case							)


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_   :      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'''
		)
		SCREAMING_SNAKE_CASE_   :      List[str]			=   parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)











SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




