





"""simple docstring"""




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

UpperCamelCase_	 ={
    """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 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						for attribute in key.split('''.'''  ):
												_UpperCamelCase     :   Tuple      =       getattr(_lowercase			,		_lowercase  )

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

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





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Union[str, Any]      =       []
						_UpperCamelCase     :   Any      =       fairseq_model.state_dict()

						_UpperCamelCase     :   Any      =       hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCamelCase     :   Any      =       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		hf_model.config.feat_extract_norm == '''group'''			,		)
																		_UpperCamelCase     :   Optional[int]      =       True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCamelCase     :   List[Any]      =       mapped_key

																								if key in name:
																														_UpperCamelCase     :   Union[str, Any]      =       True
																														if "*" in mapped_key:
																																				_UpperCamelCase     :   Tuple      =       name.split(_lowercase  )[0].split('''.'''  )[-2]
																																				_UpperCamelCase     :   Optional[Any]      =       mapped_key.replace('''*'''			,		_lowercase  )
																														if "weight_g" in name:
																																				_UpperCamelCase     :   List[Any]      =       '''weight_g'''
																														elif "weight_v" in name:
																																				_UpperCamelCase     :   Dict      =       '''weight_v'''
																														elif "weight" in name:
																																				_UpperCamelCase     :   List[Any]      =       '''weight'''
																														elif "bias" in name:
																																				_UpperCamelCase     :   List[str]      =       '''bias'''
																														else:
																																				_UpperCamelCase     :   Dict      =       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 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Any      =       full_name.split('''conv_layers.'''  )[-1]
						_UpperCamelCase     :   Union[str, Any]      =       name.split('''.'''  )
						_UpperCamelCase     :   int      =       int(items[0]  )
						_UpperCamelCase     :   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."""
																		)
																		_UpperCamelCase     :   Optional[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."""
																		)
																		_UpperCamelCase     :   Tuple      =       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."
																		)
																		_UpperCamelCase     :   Optional[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."""
																		)
																		_UpperCamelCase     :   str      =       value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
						else:
												unused_weights.append(_lowercase  )





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Tuple      =       HubertConfig()
						_UpperCamelCase     :   Optional[int]      =       model.config

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

						return config





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase=None  ):
						_UpperCamelCase     :   Optional[Any]      =       distilhubert().model.model

						if config_path is not None:
												_UpperCamelCase     :   Any      =       HubertConfig.from_pretrained(_lowercase  )
						else:
												_UpperCamelCase     :   List[str]      =       convert_config(_lowercase  )
						_UpperCamelCase     :   List[Any]      =       model.eval()

						_UpperCamelCase     :   int      =       WavaVecaFeatureExtractor(
						    feature_size=1			,		sampling_rate=1_6000			,		padding_value=0			,		do_normalize=_lowercase			,		return_attention_mask=_lowercase			,		)
						_UpperCamelCase     :   Optional[Any]      =       HubertModel(_lowercase  )

						recursively_load_weights(_lowercase			,		_lowercase  )

						feature_extractor.save_pretrained(_lowercase  )
						hf_model.save_pretrained(_lowercase  )


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







import torch

from transformers import AutoTokenizer







class      _lowercase					(				lowercase__):


       """simple docstring"""




       def __init__(  self				:     Optional[Any]   , __lowerCamelCase				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    AutoTokenizer.from_pretrained(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Union[str, Any]   , __lowerCamelCase				:     str			):


             '''simple docstring'''



             if isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							List[Any]       =    None
                   for l in x:
                         lowerCamelCase__						:							str       =    self.tokenizer(
                             __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )
                         lowerCamelCase__						:							Dict       =    torch.tensor([len(__lowerCamelCase			)]			)
                         if d is not None:
                               for k in d.keys():
                                     lowerCamelCase__						:							Tuple       =    torch.cat((d[k], t[k])   , 0			)
                         else:
                               lowerCamelCase__						:							Union[str, Any]       =    t

                   lowerCamelCase__						:							List[Any]       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"			)			)
                   lowerCamelCase__						:							Dict       =    torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"			)			)

             elif isinstance(__lowerCamelCase   , __lowerCamelCase			) and all(isinstance(_x   , __lowerCamelCase			) for _x in x			):
                   lowerCamelCase__						:							Tuple       =    self.tokenizer(
                       __lowerCamelCase   , padding="max_length"   , max_length=384   , truncation=__lowerCamelCase   , return_tensors="pt"   , )

             else:
                   raise Exception(
                       "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
                       " strings` for supports are supported."			)

             return d





       def 		lowerCAmelCase	(  self				:     List[Any]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Dict   , __lowerCamelCase				:     Any   , __lowerCamelCase				:     Optional[Any]=0.7_0			):


             '''simple docstring'''



             lowerCamelCase__						:							Union[str, Any]       =    []
             for idx in range(len(W_query["input_ids"]			)			):
                   lowerCamelCase__						:							str       =    range(p_start.shape[1]			)

                   lowerCamelCase__						:							int       =    []
                   for start_id in start_indexes:
                         for end_id in end_indexes:
                               if start_id < end_id:
                                     output.append(
                                         (
                                             start_id,
                                             end_id,
                                             p_start[idx][start_id].item(),
                                             p_end[idx][end_id].item(),
                                         )			)

                   output.sort(key=lambda __lowerCamelCase			: (tup[2] * tup[3])   , reverse=__lowerCamelCase			)
                   lowerCamelCase__						:							str       =    []
                   for k in range(len(__lowerCamelCase			)			):
                         if output[k][2] * output[k][3] >= thresh:
                               lowerCamelCase__       ,  lowerCamelCase__						:							Optional[int]       =    output[k][0], output[k][1]
                               lowerCamelCase__						:							Tuple       =    self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]			)
                               temp.append((decoded, output[k][2] * output[k][3])			)

                   final_outputs.append(__lowerCamelCase			)

             return final_outputs


