


'''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__      (						__lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							Any	,   __lowerCamelCase	:							Optional[Any]							):







			'''simple docstring'''




			for attribute in key.split('.'							):
						_UpperCAmelCase       :       Optional[int]							   =getattr(_snake_case	,   _snake_case							)

			if weight_type is not None:
						_UpperCAmelCase       :       List[str]							   =getattr(_snake_case	,   _snake_case							).shape
			else:
						_UpperCAmelCase       :       Dict							   =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":
						_UpperCAmelCase       :       Optional[int]							   =value
			elif weight_type == "weight_g":
						_UpperCAmelCase       :       Union[str, Any]							   =value
			elif weight_type == "weight_v":
						_UpperCAmelCase       :       Tuple							   =value
			elif weight_type == "bias":
						_UpperCAmelCase       :       Optional[Any]							   =value
			else:
						_UpperCAmelCase       :       Dict							   =value

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





def lowerCamelCase__      (						__lowerCamelCase	:							Any	,   __lowerCamelCase	:							Optional[Any]							):







			'''simple docstring'''




			_UpperCAmelCase       :       str							   =[]
			_UpperCAmelCase       :       int							   =fairseq_model.state_dict()

			_UpperCAmelCase       :       str							   =hf_model.unispeech_sat.feature_extractor

			for name, value in fairseq_dict.items():
						_UpperCAmelCase       :       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'	,   )
									_UpperCAmelCase       :       Union[str, Any]							   =True
						else:
									for key, mapped_key in MAPPING.items():
												_UpperCAmelCase       :       List[Any]							   ='''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
															_UpperCAmelCase       :       List[str]							   =True
															if "*" in mapped_key:
																		_UpperCAmelCase       :       Optional[Any]							   =name.split(_snake_case							)[0].split('.'							)[-2]
																		_UpperCAmelCase       :       Dict							   =mapped_key.replace('*'	,   _snake_case							)
															if "weight_g" in name:
																		_UpperCAmelCase       :       List[Any]							   ='''weight_g'''
															elif "weight_v" in name:
																		_UpperCAmelCase       :       Optional[Any]							   ='''weight_v'''
															elif "bias" in name:
																		_UpperCAmelCase       :       Union[str, Any]							   ='''bias'''
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		_UpperCAmelCase       :       List[str]							   ='''weight'''
															else:
																		_UpperCAmelCase       :       Dict							   =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 lowerCamelCase__      (						__lowerCamelCase	:							List[Any]	,   __lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							Tuple	,   __lowerCamelCase	:							List[str]	,   __lowerCamelCase	:							str							):







			'''simple docstring'''




			_UpperCAmelCase       :       Optional[Any]							   =full_name.split('conv_layers.'							)[-1]
			_UpperCAmelCase       :       Any							   =name.split('.'							)
			_UpperCAmelCase       :       Tuple							   =int(items[0]							)
			_UpperCAmelCase       :       Union[str, Any]							   =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."							)
									_UpperCAmelCase       :       List[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."							)
									_UpperCAmelCase       :       Optional[int]							   =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."							)
									_UpperCAmelCase       :       str							   =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."							)
									_UpperCAmelCase       :       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 lowerCamelCase__      (						__lowerCamelCase	:							Optional[Any]	,   __lowerCamelCase	:							Optional[int]	,   __lowerCamelCase	:							List[Any]=None	,   __lowerCamelCase	:							int=None	,   __lowerCamelCase	:							Union[str, Any]=True							):







			'''simple docstring'''




			if config_path is not None:
						_UpperCAmelCase       :       int							   =UniSpeechSatConfig.from_pretrained(_snake_case							)
			else:
						_UpperCAmelCase       :       str							   =UniSpeechSatConfig()

			_UpperCAmelCase       :       Optional[Any]							   =''''''

			if is_finetuned:
						_UpperCAmelCase       :       Optional[Any]							   =UniSpeechSatForCTC(_snake_case							)
			else:
						_UpperCAmelCase       :       Tuple							   =UniSpeechSatForPreTraining(_snake_case							)

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

			recursively_load_weights(_snake_case	,   _snake_case							)

			hf_wavavec.save_pretrained(_snake_case							)


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'''






from __future__ import annotations

from random import choice





def lowerCamelCase__      (						__lowerCamelCase	:							Optional[int]							):







   '''simple docstring'''




   return choice(__lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       int							   =random_pivot(__lowerCamelCase							)

   # partition based on pivot
   # linear time
   _UpperCAmelCase       :       str							   =[e for e in lst if e < pivot]
   _UpperCAmelCase       :       Dict							   =[e for e in lst if e > pivot]

   # if we get lucky, pivot might be the element we want.
   # we can easily see this:
   # small (elements smaller than k)
   # + pivot (kth element)
   # + big (elements larger than k)
   if len(__lowerCamelCase							) == k - 1:
      return pivot
   # pivot is in elements bigger than k
   elif len(__lowerCamelCase							) < k - 1:
      return kth_number(__lowerCamelCase	,   k - len(__lowerCamelCase							) - 1							)
   # pivot is in elements smaller than k
   else:
      return kth_number(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

