




'''simple docstring'''


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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""",
    """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""",
}
_lowercase  =			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a							):
						for attribute in key.split('.'							):
												__snake_case						 =   getattr(a    ,	a							)

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

						logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'							)
def 						lowerCamelCase__   (				a    ,	a							):
						__snake_case						 =   []
						__snake_case						 =   fairseq_model.state_dict()

						__snake_case						 =   hf_model.unispeech_sat.feature_extractor

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

						logger.warning(f'Unused weights: {unused_weights}'							)
def 						lowerCamelCase__   (				a    ,	a    ,	a    ,	a    ,	a							):
						__snake_case						 =   full_name.split('conv_layers.'							)[-1]
						__snake_case						 =   name.split('.'							)
						__snake_case						 =   int(items[0]							)
						__snake_case						 =   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.'							)
																		__snake_case						 =   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.'							)
																		__snake_case						 =   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.'							)
																		__snake_case						 =   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.'							)
																		__snake_case						 =   value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'							)
						else:
												unused_weights.append(a							)
@torch.no_grad()
def 						lowerCamelCase__   (				a    ,	a    ,	a=None    ,	a=None    ,	a=True							):
						if config_path is not None:
												__snake_case						 =   UniSpeechSatConfig.from_pretrained(a							)
						else:
												__snake_case						 =   UniSpeechSatConfig()

						__snake_case						 =   ''

						if is_finetuned:
												__snake_case						 =   UniSpeechSatForCTC(a							)
						else:
												__snake_case						 =   UniSpeechSatForPreTraining(a							)

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

						recursively_load_weights(a    ,	a							)

						hf_wavavec.save_pretrained(a							)


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_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)







'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						assert (
						    isinstance(a    ,	a							) and number_of_steps > 0
						), f'number_of_steps needs to be positive integer, your input {number_of_steps}'
						if number_of_steps == 1:
												return 1
						__snake_case ,       __snake_case						 =   1, 1
						for _ in range(number_of_steps - 1							):
												__snake_case ,       __snake_case						 =   current + previous, current
						return current


if __name__ == "__main__":
	import doctest

	doctest.testmod()

