






"""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()
SCREAMING_SNAKE_CASE						:						str					   =       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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				for attribute in key.split('.'				):
								A__							 =       getattr(__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE				)

				if weight_type is not None:
								A__							 =       getattr(__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE				).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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				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(
												    __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,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(__SCREAMING_SNAKE_CASE				)[0].split('.'				)[-2]
																								A__							 =       mapped_key.replace('*'       ,__SCREAMING_SNAKE_CASE				)
																				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(__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE				)
																continue
								if not is_used:
												unused_weights.append(__SCREAMING_SNAKE_CASE				)

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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				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(__SCREAMING_SNAKE_CASE				)



@torch.no_grad()
def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=True				):
				if config_path is not None:
								A__							 =       HubertConfig.from_pretrained(__SCREAMING_SNAKE_CASE				)
				else:
								A__							 =       HubertConfig()

				if is_finetuned:
								if dict_path:
												A__							 =       Dictionary.load(__SCREAMING_SNAKE_CASE				)

												# 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(__SCREAMING_SNAKE_CASE       ,'vocab.json'				)
												if not os.path.isdir(__SCREAMING_SNAKE_CASE				):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__SCREAMING_SNAKE_CASE				)				)
																return
												os.makedirs(__SCREAMING_SNAKE_CASE       ,exist_ok=__SCREAMING_SNAKE_CASE				)
												with open(__SCREAMING_SNAKE_CASE       ,'w'       ,encoding='utf-8'				) as vocab_handle:
																json.dump(target_dict.indices       ,__SCREAMING_SNAKE_CASE				)
												A__							 =       WavaVecaCTCTokenizer(
												    __SCREAMING_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=__SCREAMING_SNAKE_CASE       ,)
												A__							 =       True if config.feat_extract_norm == 'layer' else False
												A__							 =       WavaVecaFeatureExtractor(
												    feature_size=1       ,sampling_rate=1_6000       ,padding_value=0       ,do_normalize=__SCREAMING_SNAKE_CASE       ,return_attention_mask=__SCREAMING_SNAKE_CASE       ,)
												A__							 =       WavaVecaProcessor(feature_extractor=__SCREAMING_SNAKE_CASE       ,tokenizer=__SCREAMING_SNAKE_CASE				)
												processor.save_pretrained(__SCREAMING_SNAKE_CASE				)

								A__							 =       HubertForCTC(__SCREAMING_SNAKE_CASE				)
				else:
								A__							 =       HubertModel(__SCREAMING_SNAKE_CASE				)

				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(__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE				)

				hf_wavavec.save_pretrained(__SCREAMING_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						:						Optional[int]					   =       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"""






from __future__ import annotations



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = None				):
				A__							 =       word_bank or []
				# create a table
				A__							 =       len(lowerCAmelCase__				) + 1

				A__							 =       []
				for _ in range(lowerCAmelCase__				):
								table.append([]				)
				# seed value
				A__							 =       [[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(lowerCAmelCase__				):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(lowerCAmelCase__				)] == word:
																				A__							 =       [
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(lowerCAmelCase__				)] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(lowerCAmelCase__				)]:
								combination.reverse()

				return table[len(lowerCAmelCase__				)]


if __name__ == "__main__":
					print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
					print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
					print(
					    all_construct(
					        '''hexagonosaurus''',
					        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
					    )
					)


