"""simple docstring"""




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCAmelCase       :    List[Any]									=			logging.get_logger(__name__)

@dataclass
class        __SCREAMING_SNAKE_CASE     :
				UpperCAmelCase	=      42
				UpperCAmelCase	=      42
				UpperCAmelCase	=      None
				UpperCAmelCase	=      None







				def   lowercase_		(    self    :int  )      ->					Optional[int]:




									"""simple docstring"""



									return json.dumps(dataclasses.asdict(self  )      ,indent=2  ) + "\n"





@dataclass(frozen=_lowerCAmelCase							)
class        __SCREAMING_SNAKE_CASE     :
				UpperCAmelCase	=      42
				UpperCAmelCase	=      None
				UpperCAmelCase	=      None
				UpperCAmelCase	=      None







				def   lowercase_		(    self    :List[Any]  )      ->					List[Any]:




									"""simple docstring"""



									return json.dumps(dataclasses.asdict(self  )  ) + "\n"





class        __SCREAMING_SNAKE_CASE     :







				def   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :List[str]  )      ->					str:




									"""simple docstring"""



									raise NotImplementedError()







				def   lowercase_		(    self    :Optional[int]      ,__UpperCAmelCase    :List[Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									raise NotImplementedError()







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]  )      ->					Optional[Any]:




									"""simple docstring"""



									raise NotImplementedError()







				def   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :Tuple  )      ->					List[Any]:




									"""simple docstring"""



									raise NotImplementedError()







				def   lowercase_		(    self    :str  )      ->					List[str]:




									"""simple docstring"""



									raise NotImplementedError()







				def   lowercase_		(    self    :Tuple      ,__UpperCAmelCase    :List[Any]  )      ->					int:




									"""simple docstring"""



									if len(self.get_labels()  ) > 1:
														lowerCamelCase__		:   str  				=					self.get_labels()[int(example.label  )]
									return example







				@classmethod
				def   lowercase_		(    cls    :Optional[Any]      ,__UpperCAmelCase    :int      ,__UpperCAmelCase    :Union[str, Any]=None  )      ->					int:




									"""simple docstring"""



									with open(__UpperCAmelCase      ,'''r'''      ,encoding='''utf-8-sig'''  ) as f:
														return list(csv.reader(__UpperCAmelCase      ,delimiter='''\t'''      ,quotechar=__UpperCAmelCase  )  )





class        __SCREAMING_SNAKE_CASE     (   _lowerCAmelCase							):







				def __init__(    self    :Any      ,__UpperCAmelCase    :Optional[int]=None      ,__UpperCAmelCase    :Any=None      ,__UpperCAmelCase    :int="classification"      ,__UpperCAmelCase    :int=False  )      ->					List[str]:




									"""simple docstring"""



									lowerCamelCase__		:   str  				=					[] if labels is None else labels
									lowerCamelCase__		:   Dict  				=					[] if examples is None else examples
									lowerCamelCase__		:   Any  				=					mode
									lowerCamelCase__		:   Optional[int]  				=					verbose







				def __len__(    self    :Union[str, Any]  )      ->					Optional[Any]:




									"""simple docstring"""



									return len(self.examples  )







				def __getitem__(    self    :Tuple      ,__UpperCAmelCase    :Optional[int]  )      ->					Any:




									"""simple docstring"""



									if isinstance(__UpperCAmelCase      ,__UpperCAmelCase  ):
														return SingleSentenceClassificationProcessor(labels=self.labels      ,examples=self.examples[idx]  )
									return self.examples[idx]







				@classmethod
				def   lowercase_		(    cls    :Optional[int]      ,__UpperCAmelCase    :Tuple      ,__UpperCAmelCase    :int=""      ,__UpperCAmelCase    :Any=0      ,__UpperCAmelCase    :Union[str, Any]=1      ,__UpperCAmelCase    :Tuple=None      ,__UpperCAmelCase    :Dict=False      ,**__UpperCAmelCase    :Tuple  )      ->					int:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					cls(**__UpperCAmelCase  )
									processor.add_examples_from_csv(
									    __UpperCAmelCase      ,split_name=__UpperCAmelCase      ,column_label=__UpperCAmelCase      ,column_text=__UpperCAmelCase      ,column_id=__UpperCAmelCase      ,skip_first_row=__UpperCAmelCase      ,overwrite_labels=__UpperCAmelCase      ,overwrite_examples=__UpperCAmelCase      ,)
									return processor







				@classmethod
				def   lowercase_		(    cls    :Dict      ,__UpperCAmelCase    :Union[str, Any]      ,__UpperCAmelCase    :Any=None      ,**__UpperCAmelCase    :int  )      ->					Tuple:




									"""simple docstring"""



									lowerCamelCase__		:   List[Any]  				=					cls(**__UpperCAmelCase  )
									processor.add_examples(__UpperCAmelCase      ,labels=__UpperCAmelCase  )
									return processor







				def   lowercase_		(    self    :str      ,__UpperCAmelCase    :Optional[int]      ,__UpperCAmelCase    :Union[str, Any]=""      ,__UpperCAmelCase    :Optional[int]=0      ,__UpperCAmelCase    :Tuple=1      ,__UpperCAmelCase    :Any=None      ,__UpperCAmelCase    :Tuple=False      ,__UpperCAmelCase    :List[str]=False      ,__UpperCAmelCase    :Optional[int]=False      ,)      ->					Any:




									"""simple docstring"""



									lowerCamelCase__		:   Dict  				=					self._read_tsv(__UpperCAmelCase  )
									if skip_first_row:
														lowerCamelCase__		:   List[Any]  				=					lines[1:]
									lowerCamelCase__		:   Union[str, Any]  				=					[]
									lowerCamelCase__		:   Union[str, Any]  				=					[]
									lowerCamelCase__		:   str  				=					[]
									for i, line in enumerate(__UpperCAmelCase  ):
														texts.append(line[column_text]  )
														labels.append(line[column_label]  )
														if column_id is not None:
																			ids.append(line[column_id]  )
														else:
																			lowerCamelCase__		:   List[Any]  				=					F"""{split_name}-{i}""" if split_name else str(__UpperCAmelCase  )
																			ids.append(__UpperCAmelCase  )

									return self.add_examples(
									    __UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase      ,overwrite_labels=__UpperCAmelCase      ,overwrite_examples=__UpperCAmelCase  )







				def   lowercase_		(    self    :List[Any]      ,__UpperCAmelCase    :List[str]      ,__UpperCAmelCase    :List[str]=None      ,__UpperCAmelCase    :Optional[Any]=None      ,__UpperCAmelCase    :List[str]=False      ,__UpperCAmelCase    :Optional[int]=False  )      ->					Dict:




									"""simple docstring"""



									if labels is not None and len(__UpperCAmelCase  ) != len(__UpperCAmelCase  ):
														raise ValueError(
														    F"""Text and labels have mismatched lengths {len(__UpperCAmelCase  )} and {len(__UpperCAmelCase  )}"""  )
									if ids is not None and len(__UpperCAmelCase  ) != len(__UpperCAmelCase  ):
														raise ValueError(F"""Text and ids have mismatched lengths {len(__UpperCAmelCase  )} and {len(__UpperCAmelCase  )}"""  )
									if ids is None:
														lowerCamelCase__		:   Union[str, Any]  				=					[None] * len(__UpperCAmelCase  )
									if labels is None:
														lowerCamelCase__		:   Union[str, Any]  				=					[None] * len(__UpperCAmelCase  )
									lowerCamelCase__		:   Optional[Any]  				=					[]
									lowerCamelCase__		:   Union[str, Any]  				=					set()
									for text_or_text_and_label, label, guid in zip(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  ):
														if isinstance(__UpperCAmelCase      ,(tuple, list)  ) and label is None:
																			lowerCamelCase__				,						lowerCamelCase__		:   Any  				=					text_or_text_and_label
														else:
																			lowerCamelCase__		:   Optional[Any]  				=					text_or_text_and_label
														added_labels.add(__UpperCAmelCase  )
														examples.append(InputExample(guid=__UpperCAmelCase      ,text_a=__UpperCAmelCase      ,text_b=__UpperCAmelCase      ,label=__UpperCAmelCase  )  )

									# Update examples
									if overwrite_examples:
														lowerCamelCase__		:   int  				=					examples
									else:
														self.examples.extend(__UpperCAmelCase  )

									# Update labels
									if overwrite_labels:
														lowerCamelCase__		:   Dict  				=					list(__UpperCAmelCase  )
									else:
														lowerCamelCase__		:   int  				=					list(set(self.labels  ).union(__UpperCAmelCase  )  )

									return self.examples







				def   lowercase_		(    self    :Union[str, Any]      ,__UpperCAmelCase    :Dict      ,__UpperCAmelCase    :int=None      ,__UpperCAmelCase    :str=False      ,__UpperCAmelCase    :str=0      ,__UpperCAmelCase    :int=True      ,__UpperCAmelCase    :int=None      ,)      ->					Union[str, Any]:




									"""simple docstring"""



									if max_length is None:
														lowerCamelCase__		:   Dict  				=					tokenizer.max_len

									lowerCamelCase__		:   Optional[int]  				=					{label: i for i, label in enumerate(self.labels  )}

									lowerCamelCase__		:   int  				=					[]
									for ex_index, example in enumerate(self.examples  ):
														if ex_index % 1_00_00 == 0:
																			logger.info(F"""Tokenizing example {ex_index}"""  )

														lowerCamelCase__		:   List[str]  				=					tokenizer.encode(
														    example.text_a      ,add_special_tokens=__UpperCAmelCase      ,max_length=min(__UpperCAmelCase      ,tokenizer.max_len  )      ,)
														all_input_ids.append(__UpperCAmelCase  )

									lowerCamelCase__		:   str  				=					max(len(__UpperCAmelCase  ) for input_ids in all_input_ids  )

									lowerCamelCase__		:   Dict  				=					[]
									for ex_index, (input_ids, example) in enumerate(zip(__UpperCAmelCase      ,self.examples  )  ):
														if ex_index % 1_00_00 == 0:
																			logger.info(F"""Writing example {ex_index}/{len(self.examples  )}"""  )
														# The mask has 1 for real tokens and 0 for padding tokens. Only real
														# tokens are attended to.
														lowerCamelCase__		:   Dict  				=					[1 if mask_padding_with_zero else 0] * len(__UpperCAmelCase  )

														# Zero-pad up to the sequence length.
														lowerCamelCase__		:   Tuple  				=					batch_length - len(__UpperCAmelCase  )
														if pad_on_left:
																			lowerCamelCase__		:   Optional[int]  				=					([pad_token] * padding_length) + input_ids
																			lowerCamelCase__		:   Tuple  				=					([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																			lowerCamelCase__		:   Tuple  				=					input_ids + ([pad_token] * padding_length)
																			lowerCamelCase__		:   Optional[int]  				=					attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

														if len(__UpperCAmelCase  ) != batch_length:
																			raise ValueError(F"""Error with input length {len(__UpperCAmelCase  )} vs {batch_length}"""  )
														if len(__UpperCAmelCase  ) != batch_length:
																			raise ValueError(F"""Error with input length {len(__UpperCAmelCase  )} vs {batch_length}"""  )

														if self.mode == "classification":
																			lowerCamelCase__		:   List[str]  				=					label_map[example.label]
														elif self.mode == "regression":
																			lowerCamelCase__		:   Optional[int]  				=					float(example.label  )
														else:
																			raise ValueError(self.mode  )

														if ex_index < 5 and self.verbose:
																			logger.info('''*** Example ***'''  )
																			logger.info(F"""guid: {example.guid}"""  )
																			logger.info(F"""input_ids: {' '.join([str(__UpperCAmelCase  ) for x in input_ids]  )}"""  )
																			logger.info(F"""attention_mask: {' '.join([str(__UpperCAmelCase  ) for x in attention_mask]  )}"""  )
																			logger.info(F"""label: {example.label} (id = {label})"""  )

														features.append(InputFeatures(input_ids=__UpperCAmelCase      ,attention_mask=__UpperCAmelCase      ,label=__UpperCAmelCase  )  )

									if return_tensors is None:
														return features
									elif return_tensors == "tf":
														if not is_tf_available():
																			raise RuntimeError('''return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'''  )
														import tensorflow as tf

														def gen():
																			for ex in features:
																								yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

														lowerCamelCase__		:   Tuple  				=					tf.data.Dataset.from_generator(
														    __UpperCAmelCase      ,({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)      ,({'''input_ids''': tf.TensorShape([None]  ), '''attention_mask''': tf.TensorShape([None]  )}, tf.TensorShape([]  ))      ,)
														return dataset
									elif return_tensors == "pt":
														if not is_torch_available():
																			raise RuntimeError('''return_tensors set to \'pt\' but PyTorch can\'t be imported'''  )
														import torch
														from torch.utils.data import TensorDataset

														lowerCamelCase__		:   Union[str, Any]  				=					torch.tensor([f.input_ids for f in features]      ,dtype=torch.long  )
														lowerCamelCase__		:   Union[str, Any]  				=					torch.tensor([f.attention_mask for f in features]      ,dtype=torch.long  )
														if self.mode == "classification":
																			lowerCamelCase__		:   Tuple  				=					torch.tensor([f.label for f in features]      ,dtype=torch.long  )
														elif self.mode == "regression":
																			lowerCamelCase__		:   str  				=					torch.tensor([f.label for f in features]      ,dtype=torch.float  )

														lowerCamelCase__		:   Optional[Any]  				=					TensorDataset(__UpperCAmelCase      ,__UpperCAmelCase      ,__UpperCAmelCase  )
														return dataset
									else:
														raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''  )







"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, map_location='''cpu'''						)
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			argparse.ArgumentParser()
					parser.add_argument(
					    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
					)
					parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
					parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






