
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case__  							=							logging.get_logger(__name__)

snake_case__  							=							{
    """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        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				str       ):
						for attribute in key.split('''.'''       ):
												lowercase       :    Any											=  getattr(lowercase__							,			lowercase__       )

						if weight_type is not None:
												lowercase       :    Optional[int]											=  getattr(lowercase__							,			lowercase__       ).shape
						else:
												lowercase       :    int											=  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":
												lowercase       :    Optional[int]											=  value
						elif weight_type == "weight_g":
												lowercase       :    str											=  value
						elif weight_type == "weight_v":
												lowercase       :    Union[str, Any]											=  value
						elif weight_type == "bias":
												lowercase       :    Any											=  value
						else:
												lowercase       :    Optional[Any]											=  value

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[int]							,			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    Dict											=  []
						lowercase       :    int											=  fairseq_model.state_dict()

						lowercase       :    Optional[Any]											=  hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase       :    int											=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowercase__							,			lowercase__							,			lowercase__							,			lowercase__							,			hf_model.config.feat_extract_norm == '''group'''							,			)
																		lowercase       :    Tuple											=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase       :    Any											=  mapped_key

																								if key in name:
																														lowercase       :    Tuple											=  True
																														if "*" in mapped_key:
																																				lowercase       :    List[str]											=  name.split(lowercase__       )[0].split('''.'''       )[-2]
																																				lowercase       :    Dict											=  mapped_key.replace('''*'''							,			lowercase__       )
																														if "weight_g" in name:
																																				lowercase       :    Tuple											=  '''weight_g'''
																														elif "weight_v" in name:
																																				lowercase       :    Tuple											=  '''weight_v'''
																														elif "weight" in name:
																																				lowercase       :    Optional[Any]											=  '''weight'''
																														elif "bias" in name:
																																				lowercase       :    List[str]											=  '''bias'''
																														else:
																																				lowercase       :    List[Any]											=  None
																														set_recursively(lowercase__							,			lowercase__							,			lowercase__							,			lowercase__							,			lowercase__       )
																								continue
												if not is_used:
																		unused_weights.append(lowercase__       )

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				str       ):
						lowercase       :    Optional[int]											=  full_name.split('''conv_layers.'''       )[-1]
						lowercase       :    Optional[Any]											=  name.split('''.'''       )
						lowercase       :    Optional[int]											=  int(items[0]       )
						lowercase       :    int											=  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.'''
																		)
																		lowercase       :    Union[str, Any]											=  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.'''
																		)
																		lowercase       :    Dict											=  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."
																		)
																		lowercase       :    Union[str, Any]											=  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.'''
																		)
																		lowercase       :    Any											=  value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
						else:
												unused_weights.append(lowercase__       )

def        lowerCamelCase_      (			UpperCAmelCase_							:				int       ):
						lowercase       :    Dict											=  HubertConfig()
						lowercase       :    List[str]											=  model.config

						lowercase       :    Any											=  fs_config.activation_dropout
						lowercase       :    Tuple											=  False
						lowercase       :    Optional[int]											=  fs_config.attention_dropout
						lowercase       :    Union[str, Any]											=  False
						lowercase       :    int											=  eval(fs_config.extractor_conv_feature_layers       )
						lowercase       :    str											=  [x[0] for x in conv_layers]
						lowercase       :    Any											=  [x[1] for x in conv_layers]
						lowercase       :    Union[str, Any]											=  [x[2] for x in conv_layers]
						lowercase       :    Optional[Any]											=  '''gelu'''
						lowercase       :    List[Any]											=  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						lowercase       :    List[str]											=  False
						lowercase       :    int											=  0.0
						lowercase       :    Optional[int]											=  0.0
						lowercase       :    List[str]											=  fs_config.activation_fn
						lowercase       :    Optional[Any]											=  fs_config.dropout
						lowercase       :    Dict											=  fs_config.encoder_embed_dim
						lowercase       :    Optional[Any]											=  0.02
						lowercase       :    List[str]											=  fs_config.encoder_ffn_embed_dim
						lowercase       :    Optional[int]											=  1E-5
						lowercase       :    Any											=  0.0
						lowercase       :    int											=  fs_config.encoder_attention_heads
						lowercase       :    Union[str, Any]											=  fs_config.conv_pos_groups
						lowercase       :    Dict											=  fs_config.conv_pos
						lowercase       :    Optional[int]											=  len(lowercase__       )
						lowercase       :    int											=  fs_config.encoder_layers

						return config

@torch.no_grad()
def        lowerCamelCase_      (			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				List[str]=None       ):
						lowercase       :    Dict											=  distilhubert().model.model

						if config_path is not None:
												lowercase       :    Any											=  HubertConfig.from_pretrained(lowercase__       )
						else:
												lowercase       :    Any											=  convert_config(lowercase__       )
						lowercase       :    str											=  model.eval()

						lowercase       :    Any											=  WavaVecaFeatureExtractor(
						    feature_size=1							,			sampling_rate=1_60_00							,			padding_value=0							,			do_normalize=lowercase__							,			return_attention_mask=lowercase__							,			)
						lowercase       :    str											=  HubertModel(lowercase__       )

						recursively_load_weights(lowercase__							,			lowercase__       )

						feature_extractor.save_pretrained(lowercase__       )
						hf_model.save_pretrained(lowercase__       )


if __name__ == "__main__":
				snake_case__  							=							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""")
				snake_case__  							=							parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)




from itertools import combinations_with_replacement

def        lowerCamelCase_      (			UpperCAmelCase_							:				int = 1_00       ):
						lowercase       :    list[int]											=  [*list(range(1							,			21       )       ), 25]
						lowercase       :    list[int]											=  [2 * x for x in range(1							,			21       )] + [50]
						lowercase       :    list[int]											=  [3 * x for x in range(1							,			21       )]
						lowercase       :    list[int]											=  singles + doubles + triples + [0]

						lowercase       :    int											=  0
						lowercase       :    int
						lowercase       :    int
						lowercase       :    int
						lowercase       :    int

						for double in doubles:
												for throwa, throwa in combinations_with_replacement(UpperCAmelCase_							,			2       ):
																		lowercase       :    Union[str, Any]											=  double + throwa + throwa
																		if checkout_total < limit:
																								num_checkouts += 1

						return num_checkouts


if __name__ == "__main__":
				print(F'{solution() = }')


