


'''simple docstring'''
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__magic_name__   :   Union[str, Any]  							=logging.get_logger(__name__)

__magic_name__   :   Any  							={
    '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',
    'mask_emb': 'masked_spec_embed',
}




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				List[Any]	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




				for attribute in key.split("."						):
								__magic_name__	  = getattr(lowerCamelCase_	,  lowerCamelCase_						)

				if weight_type is not None:
								__magic_name__	  = getattr(lowerCamelCase_	,  lowerCamelCase_						).shape
				else:
								__magic_name__	  = hf_pointer.shape

				assert hf_shape == value.shape, (
				    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":
								__magic_name__	  = value
				elif weight_type == "weight_g":
								__magic_name__	  = value
				elif weight_type == "weight_v":
								__magic_name__	  = value
				elif weight_type == "bias":
								__magic_name__	  = value
				else:
								__magic_name__	  = value

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




def      __snake_case       (       lowerCamelCase_ :				Any	,  lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




				__magic_name__	  = []
				__magic_name__	  = fairseq_model.state_dict()

				__magic_name__	  = hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__magic_name__	  = False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  hf_model.config.feat_extract_norm == "group"	,  )
												__magic_name__	  = True
								else:
												for key, mapped_key in MAPPING.items():
																__magic_name__	  = mapped_key

																if key in name:
																				__magic_name__	  = True
																				if "*" in mapped_key:
																								__magic_name__	  = name.split(lowerCamelCase_						)[0].split("."						)[-2]
																								__magic_name__	  = mapped_key.replace("*"	,  lowerCamelCase_						)
																				if "weight_g" in name:
																								__magic_name__	  = "weight_g"
																				elif "weight_v" in name:
																								__magic_name__	  = "weight_v"
																				elif "weight" in name:
																								__magic_name__	  = "weight"
																				elif "bias" in name:
																								__magic_name__	  = "bias"
																				else:
																								__magic_name__	  = None
																				set_recursively(lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_	,  lowerCamelCase_						)
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase_						)

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




def      __snake_case       (       lowerCamelCase_ :				Dict	,  lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				int	,  lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




				__magic_name__	  = full_name.split("conv_layers."						)[-1]
				__magic_name__	  = name.split("."						)
				__magic_name__	  = int(items[0]						)
				__magic_name__	  = int(items[1]						)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
												)
												__magic_name__	  = value
												logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'						)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
												)
												__magic_name__	  = 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:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
												    " found."
												)
												__magic_name__	  = value
												logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    F'{full_name} has size {value.shape}, but'
												    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
												)
												__magic_name__	  = value
												logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
				else:
								unused_weights.append(lowerCamelCase_						)




def      __snake_case       (       lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




				__magic_name__	  = HubertConfig()
				__magic_name__	  = model.config

				__magic_name__	  = fs_config.activation_dropout
				__magic_name__	  = False
				__magic_name__	  = fs_config.attention_dropout
				__magic_name__	  = False
				__magic_name__	  = eval(fs_config.extractor_conv_feature_layers						)
				__magic_name__	  = [x[0] for x in conv_layers]
				__magic_name__	  = [x[1] for x in conv_layers]
				__magic_name__	  = [x[2] for x in conv_layers]
				__magic_name__	  = "gelu"
				__magic_name__	  = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
				__magic_name__	  = False
				__magic_name__	  = 0.0
				__magic_name__	  = 0.0
				__magic_name__	  = fs_config.activation_fn
				__magic_name__	  = fs_config.dropout
				__magic_name__	  = fs_config.encoder_embed_dim
				__magic_name__	  = 0.02
				__magic_name__	  = fs_config.encoder_ffn_embed_dim
				__magic_name__	  = 1e-5
				__magic_name__	  = 0.0
				__magic_name__	  = fs_config.encoder_attention_heads
				__magic_name__	  = fs_config.conv_pos_groups
				__magic_name__	  = fs_config.conv_pos
				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = fs_config.encoder_layers

				return config




@torch.no_grad()
def      __snake_case       (       lowerCamelCase_ :				int	,  lowerCamelCase_ :				int=None						):
				'''simple docstring'''




				__magic_name__	  = distilhubert().model.model

				if config_path is not None:
								__magic_name__	  = HubertConfig.from_pretrained(lowerCamelCase_						)
				else:
								__magic_name__	  = convert_config(lowerCamelCase_						)
				__magic_name__	  = model.eval()

				__magic_name__	  = WavaVecaFeatureExtractor(
				    feature_size=1	,  sampling_rate=1_6000	,  padding_value=0	,  do_normalize=lowerCamelCase_	,  return_attention_mask=lowerCamelCase_	,  )
				__magic_name__	  = HubertModel(lowerCamelCase_						)

				recursively_load_weights(lowerCamelCase_	,  lowerCamelCase_						)

				feature_extractor.save_pretrained(lowerCamelCase_						)
				hf_model.save_pretrained(lowerCamelCase_						)


if __name__ == "__main__":
						__magic_name__   :   Dict  							=argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						__magic_name__   :   Tuple  							=parser.parse_args()
						convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				barcode //= 10  # exclude the last digit
				__magic_name__	  = False
				__magic_name__	  = 0

				# extract and check each digit
				while barcode != 0:
								__magic_name__	  = 1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__magic_name__	  = not checker

				return (10 - (s % 10)) % 10




def      __snake_case       (       lowerCamelCase_ :				int						):
				'''simple docstring'''




				return len(str(lowerCamelCase_						)						) == 13 and get_check_digit(lowerCamelCase_						) == barcode % 10




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				if str(lowerCamelCase_						).isalpha():
								__magic_name__	  = F'Barcode \'{barcode}\' has alphabetic characters.'
								raise ValueError(lowerCamelCase_						)
				elif int(lowerCamelCase_						) < 0:
								raise ValueError("The entered barcode has a negative value. Try again."						)
				else:
								return int(lowerCamelCase_						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__magic_name__   :   Optional[int]  							=get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(F'''\'{barcode}\' is a valid barcode.''')
						else:
												print(F'''\'{barcode}\' is NOT a valid barcode.''')



