






def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):

	if not isinstance(snake_case_  ,						snake_case_     ):
		raise ValueError("multiplicative_persistence() only accepts integral values"     )
	if num < 0:
		raise ValueError("multiplicative_persistence() does not accept negative values"     )

	snake_case__				:   Optional[int]     =						0
	snake_case__				:   List[str]     =						str(snake_case_     )

	while len(snake_case_     ) != 1:
		snake_case__				:   Optional[Any]     =						[int(snake_case_     ) for i in num_string]

		snake_case__				:   int     =						1
		for i in range(0  ,						len(snake_case_     )     ):
			total *= numbers[i]

		snake_case__				:   Tuple     =						str(snake_case_     )

		steps += 1
	return steps







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):

	if not isinstance(snake_case_  ,						snake_case_     ):
		raise ValueError("additive_persistence() only accepts integral values"     )
	if num < 0:
		raise ValueError("additive_persistence() does not accept negative values"     )

	snake_case__				:   Any     =						0
	snake_case__				:   List[str]     =						str(snake_case_     )

	while len(snake_case_     ) != 1:
		snake_case__				:   int     =						[int(snake_case_     ) for i in num_string]

		snake_case__				:   Any     =						0
		for i in range(0  ,						len(snake_case_     )     ):
			total += numbers[i]

		snake_case__				:   Tuple     =						str(snake_case_     )

		steps += 1
	return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()











import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__lowerCamelCase      :    int   		=   logging.get_logger(__name__)

__lowerCamelCase      :    int   		=   {
    """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""",
}
__lowerCamelCase      :    Tuple   		=   [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	for attribute in key.split("."     ):
		snake_case__				:   int     =						getattr(snake_case_  ,						snake_case_     )

	if weight_type is not None:
		snake_case__				:   Optional[Any]     =						getattr(snake_case_  ,						snake_case_     ).shape
	else:
		snake_case__				:   List[str]     =						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__				:   str     =						value
	elif weight_type == "weight_g":
		snake_case__				:   Union[str, Any]     =						value
	elif weight_type == "weight_v":
		snake_case__				:   Optional[Any]     =						value
	elif weight_type == "bias":
		snake_case__				:   str     =						value
	else:
		snake_case__				:   Union[str, Any]     =						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_      :							Any  ,						snake_case_      :							Union[str, Any]     ):
	snake_case__				:   str     =						[]
	snake_case__				:   Optional[int]     =						fairseq_model.state_dict()

	snake_case__				:   int     =						hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case__				:   Dict     =						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						hf_model.config.feat_extract_norm == "group"  ,						)
			snake_case__				:   str     =						True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case__				:   Optional[int]     =						"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__				:   int     =						True
					if "*" in mapped_key:
						snake_case__				:   Any     =						name.split(snake_case_     )[0].split("."     )[-2]
						snake_case__				:   Any     =						mapped_key.replace("*"  ,						snake_case_     )
					if "weight_g" in name:
						snake_case__				:   List[Any]     =						"weight_g"
					elif "weight_v" in name:
						snake_case__				:   Optional[Any]     =						"weight_v"
					elif "bias" in name:
						snake_case__				:   Optional[Any]     =						"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						snake_case__				:   Optional[Any]     =						"weight"
					else:
						snake_case__				:   Optional[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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Any  ,						snake_case_      :							List[str]  ,						snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   Tuple     =						full_name.split("conv_layers."     )[-1]
	snake_case__				:   Union[str, Any]     =						name.split("."     )
	snake_case__				:   str     =						int(items[0]     )
	snake_case__				:   str     =						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__				:   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.'''     )
			snake_case__				:   Any     =						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__				:   Optional[Any]     =						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__				:   int     =						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_      :							int  ,						snake_case_      :							Any  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Optional[int]=None  ,						snake_case_      :							Any=True     ):
	if config_path is not None:
		snake_case__				:   Tuple     =						UniSpeechSatConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   Tuple     =						UniSpeechSatConfig()

	snake_case__				:   str     =						""

	if is_finetuned:
		snake_case__				:   Tuple     =						UniSpeechSatForCTC(snake_case_     )
	else:
		snake_case__				:   Any     =						UniSpeechSatForPreTraining(snake_case_     )

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

	recursively_load_weights(snake_case_  ,						snake_case_     )

	hf_wavavec.save_pretrained(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    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"""
	)
	__lowerCamelCase      :    List[Any]   		=   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
	)



