





"""simple docstring"""






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      (      _UpperCamelCase				):







					'''simple docstring'''




					@slow
					@require_torch
					def 			_lowerCamelCase		(    self  ):
									__a			:    List[str]      							=	EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''						,      '''prajjwal1/bert-tiny'''  )
									__a			:    Optional[Any]      							=	BertTokenizer.from_pretrained('''bert-base-uncased'''  )

									__a			:    Optional[int]      							=	bertabert.config.encoder.vocab_size
									__a			:    str      							=	tokenizer.sep_token_id
									__a			:    Union[str, Any]      							=	tokenizer.cls_token_id
									__a			:    Tuple      							=	128

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

									__a			:    Optional[int]      							=	train_dataset.select(range(32  )  )
									__a			:    Optional[Any]      							=	val_dataset.select(range(16  )  )

									__a			:    List[Any]      							=	4

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

													__a			:    Tuple      							=	outputs.input_ids
													__a			:    str      							=	outputs.input_ids.copy()
													__a			:    Tuple      							=	[
													    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
													]
													__a			:    Tuple      							=	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(_UpperCAmelCase  ):
													__a			:    int      							=	pred.label_ids
													__a			:    int      							=	pred.predictions

													# all unnecessary tokens are removed
													__a			:    Optional[int]      							=	tokenizer.batch_decode(_UpperCAmelCase						,      skip_special_tokens=_UpperCAmelCase  )
													__a			:    Union[str, Any]      							=	tokenizer.batch_decode(_UpperCAmelCase						,      skip_special_tokens=_UpperCAmelCase  )

													__a			:    Any      							=	sum([int(pred_str[i] == label_str[i]  ) for i in range(len(_UpperCAmelCase  )  )]  ) / len(_UpperCAmelCase  )

													return {"accuracy": accuracy}

									# map train dataset
									__a			:    str      							=	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
									__a			:    Optional[int]      							=	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''']						,      )

									__a			:    Optional[Any]      							=	self.get_auto_remove_tmp_dir()

									__a			:    int      							=	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
									__a			:    Dict      							=	SeqaSeqTrainer(
									    model=_UpperCAmelCase						,      args=_UpperCAmelCase						,      compute_metrics=_compute_metrics						,      train_dataset=_UpperCAmelCase						,      eval_dataset=_UpperCAmelCase						,      tokenizer=_UpperCAmelCase						,      )

									# start training
									trainer.train()





"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

       super().__init__(
           pad_token_id=_UpperCAmelCase       ,				bos_token_id=_UpperCAmelCase       ,				eos_token_id=_UpperCAmelCase       ,				is_encoder_decoder=_UpperCAmelCase       ,				decoder_start_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase       ,				)

