





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
	import datasets






class 							__lowercase       (      SCREAMING_SNAKE_CASE_						):







	'''simple docstring'''

	@slow
	@require_torch
	def 						_UpperCAmelCase     (self )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''     ,'''prajjwal1/bert-tiny''' )
				__lowercase										=							BertTokenizer.from_pretrained('''bert-base-uncased''' )

				__lowercase										=							bertabert.config.encoder.vocab_size
				__lowercase										=							tokenizer.sep_token_id
				__lowercase										=							tokenizer.cls_token_id
				__lowercase										=							128

				__lowercase										=							datasets.load_dataset('''cnn_dailymail'''     ,'''3.0.0'''     ,split='''train[:1%]''' )
				__lowercase										=							datasets.load_dataset('''cnn_dailymail'''     ,'''3.0.0'''     ,split='''validation[:1%]''' )

				__lowercase										=							train_dataset.select(range(32 ) )
				__lowercase										=							val_dataset.select(range(16 ) )

				__lowercase										=							4

				def _map_to_encoder_decoder_inputs(_lowerCamelCase ):
							# Tokenizer will automatically set [BOS] <text> [EOS]
							__lowercase										=							tokenizer(batch['''article''']     ,padding='''max_length'''     ,truncation=UpperCamelCase__     ,max_length=512 )
							__lowercase										=							tokenizer(batch['''highlights''']     ,padding='''max_length'''     ,truncation=UpperCamelCase__     ,max_length=128 )
							__lowercase										=							inputs.input_ids
							__lowercase										=							inputs.attention_mask

							__lowercase										=							outputs.input_ids
							__lowercase										=							outputs.input_ids.copy()
							__lowercase										=							[
							    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
							]
							__lowercase										=							outputs.attention_mask

							assert all(len(UpperCamelCase__ ) == 512 for x in inputs.input_ids )
							assert all(len(UpperCamelCase__ ) == 128 for x in outputs.input_ids )

							return batch

				def _compute_metrics(_lowerCamelCase ):
							__lowercase										=							pred.label_ids
							__lowercase										=							pred.predictions

							# all unnecessary tokens are removed
							__lowercase										=							tokenizer.batch_decode(UpperCamelCase__     ,skip_special_tokens=UpperCamelCase__ )
							__lowercase										=							tokenizer.batch_decode(UpperCamelCase__     ,skip_special_tokens=UpperCamelCase__ )

							__lowercase										=							sum([int(pred_str[i] == label_str[i] ) for i in range(len(UpperCamelCase__ ) )] ) / len(UpperCamelCase__ )

							return {"accuracy": accuracy}

				# map train dataset
				__lowercase										=							train_dataset.map(
				    _map_to_encoder_decoder_inputs     ,batched=UpperCamelCase__     ,batch_size=UpperCamelCase__     ,remove_columns=['''article''', '''highlights''']     ,)
				train_dataset.set_format(
				    type='''torch'''     ,columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']     ,)

				# same for validation dataset
				__lowercase										=							val_dataset.map(
				    _map_to_encoder_decoder_inputs     ,batched=UpperCamelCase__     ,batch_size=UpperCamelCase__     ,remove_columns=['''article''', '''highlights''']     ,)
				val_dataset.set_format(
				    type='''torch'''     ,columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']     ,)

				__lowercase										=							self.get_auto_remove_tmp_dir()

				__lowercase										=							SeqaSeqTrainingArguments(
				    output_dir=UpperCamelCase__     ,per_device_train_batch_size=UpperCamelCase__     ,per_device_eval_batch_size=UpperCamelCase__     ,predict_with_generate=UpperCamelCase__     ,evaluation_strategy='''steps'''     ,do_train=UpperCamelCase__     ,do_eval=UpperCamelCase__     ,warmup_steps=0     ,eval_steps=2     ,logging_steps=2     ,)

				# instantiate trainer
				__lowercase										=							SeqaSeqTrainer(
				    model=UpperCamelCase__     ,args=UpperCamelCase__     ,compute_metrics=_compute_metrics     ,train_dataset=UpperCamelCase__     ,eval_dataset=UpperCamelCase__     ,tokenizer=UpperCamelCase__     ,)

				# start training
				trainer.train()












'''simple docstring'''



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]				):
			__lowercase										=							UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''projector.weight''']
			__lowercase										=							downstream_dict['''projector.bias''']
			__lowercase										=							downstream_dict['''model.post_net.linear.weight''']
			__lowercase										=							downstream_dict['''model.post_net.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''model.linear.weight''']
			__lowercase										=							downstream_dict['''model.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							UniSpeechSatForXVector.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''connector.weight''']
			__lowercase										=							downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						__lowercase										=							downstream_dict[
						    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						__lowercase										=							downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
			__lowercase										=							downstream_dict['''objective.W''']
			return model




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)

			__lowercase										=							checkpoint['''Downstream''']

			__lowercase										=							UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
			__lowercase										=							WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_		,       return_attention_mask=lowerCamelCase_		,       do_normalize=lowerCamelCase_				)

			__lowercase										=							hf_config.architectures[0]
			if arch.endswith('''ForSequenceClassification'''				):
						__lowercase										=							convert_classification(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForAudioFrameClassification'''				):
						__lowercase										=							convert_diarization(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForXVector'''				):
						__lowercase										=							convert_xvector(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			else:
						raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"				)

			if hf_config.use_weighted_layer_sum:
						__lowercase										=							checkpoint['''Featurizer''']['''weights''']

			hf_feature_extractor.save_pretrained(lowerCamelCase_				)
			hf_model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    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.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





