
'''simple docstring'''







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase    :							List[str]      =					logging.get_logger(__name__)

lowercase    :							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",
}
lowercase    :							Optional[int]      =					[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	str:







	for attribute in key.split('.'	):
		_snake_case   						=		getattr(__UpperCamelCase			,				__UpperCamelCase	)

	if weight_type is not None:
		_snake_case   						=		getattr(__UpperCamelCase			,				__UpperCamelCase	).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 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[str]:
	_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(
			    __UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase			,				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(__UpperCamelCase	)[0].split('.'	)[-2]
						_snake_case   						=		mapped_key.replace('*'			,				__UpperCamelCase	)
					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(__UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase			,				__UpperCamelCase	)
				continue
		if not is_used:
			unused_weights.append(__UpperCamelCase	)

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







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	List[str]:
	_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(__UpperCamelCase	)







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=None			,				__A=None			,				__A=True	)       ->	str:







	if config_path is not None:
		_snake_case   						=		UniSpeechSatConfig.from_pretrained(__UpperCamelCase	)
	else:
		_snake_case   						=		UniSpeechSatConfig()

	_snake_case   						=		''

	if is_finetuned:
		_snake_case   						=		UniSpeechSatForCTC(__UpperCamelCase	)
	else:
		_snake_case   						=		UniSpeechSatForPreTraining(__UpperCamelCase	)

	_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(__UpperCamelCase			,				__UpperCamelCase	)

	hf_wavavec.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
					lowercase    :							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"
					)
					lowercase    :							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
					)









"""simple docstring"""


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     list[int]  ):

     '''simple docstring'''
     __A									= len(_lowerCamelCase  )
     __A									= [0] * len_array

     if len_array > 0:
       __A									= array[0]

     for i in range(1,       _lowerCamelCase  ):
       __A									= self.prefix_sum[i - 1] + array[i]


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     if start == 0:
       return self.prefix_sum[end]

     return self.prefix_sum[end] - self.prefix_sum[start - 1]






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int  ):

     '''simple docstring'''
     __A									= {0}
     for sum_item in self.prefix_sum:
       if sum_item - target_sum in sums:
         return True

       sums.add(_lowerCamelCase  )

     return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()




