






"""simple docstring"""



import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__A		:       Tuple  				=		logging.get_logger(__name__)

__A		:       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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
__A		:       Tuple  				=		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]



def 						lowercase    (				__snake_case				: Optional[Any]		,					__snake_case				: Dict		,					__snake_case				: Dict		,					__snake_case				: Optional[Any]		,					__snake_case				: List[Any]	):
		for attribute in key.split('''.'''	):
				lowercase_ : Union[str, Any]							       =   getattr(__snake_case		,					__snake_case	)

		if weight_type is not None:
				lowercase_ : List[str]							       =   getattr(__snake_case		,					__snake_case	).shape
		else:
				lowercase_ : Optional[Any]							       =   hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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_ : Union[str, Any]							       =   value
		elif weight_type == "weight_g":
				lowercase_ : Union[str, Any]							       =   value
		elif weight_type == "weight_v":
				lowercase_ : Optional[Any]							       =   value
		elif weight_type == "bias":
				lowercase_ : Tuple							       =   value
		else:
				lowercase_ : List[Any]							       =   value

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



def 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: Dict	):
		lowercase_ : str							       =   []
		lowercase_ : int							       =   fairseq_model.state_dict()

		lowercase_ : List[str]							       =   hf_model.unispeech_sat.feature_extractor

		for name, value in fairseq_dict.items():
				lowercase_ : List[Any]							       =   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_ : Union[str, Any]							       =   True
				else:
						for key, mapped_key in MAPPING.items():
								lowercase_ : Dict							       =   '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
										if "layer_norm_for_extract" in name and (".".join(name.split('''.'''	)[:-1]	) != key):
												# special case since naming is very similar
												continue
										lowercase_ : Optional[int]							       =   True
										if "*" in mapped_key:
												lowercase_ : Optional[Any]							       =   name.split(__snake_case	)[0].split('''.'''	)[-2]
												lowercase_ : Optional[Any]							       =   mapped_key.replace('''*'''		,					__snake_case	)
										if "weight_g" in name:
												lowercase_ : List[str]							       =   '''weight_g'''
										elif "weight_v" in name:
												lowercase_ : str							       =   '''weight_v'''
										elif "bias" in name:
												lowercase_ : Optional[Any]							       =   '''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												lowercase_ : str							       =   '''weight'''
										else:
												lowercase_ : Union[str, Any]							       =   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 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: Any		,					__snake_case				: Dict		,					__snake_case				: Dict		,					__snake_case				: Optional[Any]	):
		lowercase_ : Tuple							       =   full_name.split('''conv_layers.'''	)[-1]
		lowercase_ : Union[str, Any]							       =   name.split('''.'''	)
		lowercase_ : str							       =   int(items[0]	)
		lowercase_ : Dict							       =   int(items[1]	)

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''	)
						lowercase_ : Optional[Any]							       =   value
						logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''	)
						lowercase_ : Dict							       =   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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'''	)
						lowercase_ : Optional[int]							       =   value
						logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F'''{full_name} has size {value.shape}, but'''
								    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''	)
						lowercase_ : Optional[Any]							       =   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 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[Any]		,					__snake_case				: List[Any]=None		,					__snake_case				: Optional[Any]=None		,					__snake_case				: List[Any]=True	):
		if config_path is not None:
				lowercase_ : List[Any]							       =   UniSpeechSatConfig.from_pretrained(__snake_case	)
		else:
				lowercase_ : List[str]							       =   UniSpeechSatConfig()

		lowercase_ : Union[str, Any]							       =   ''''''

		if is_finetuned:
				lowercase_ : List[Any]							       =   UniSpeechSatForCTC(__snake_case	)
		else:
				lowercase_ : Dict							       =   UniSpeechSatForPreTraining(__snake_case	)

		lowercase_       ,					lowercase_       ,					lowercase_ : str							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]		,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
		lowercase_ : Any							       =   model[0].eval()

		recursively_load_weights(__snake_case		,					__snake_case	)

		hf_wavavec.save_pretrained(__snake_case	)


if __name__ == "__main__":
						__A		:       Tuple  				=		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'''
						)
						__A		:       Optional[int]  				=		parser.parse_args()
						convert_unispeech_sat_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)











"""simple docstring"""



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



