





'''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 __a (   _snake_case			):





   @slow
   @require_torch
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE     :   Dict      							=       EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""				,"""prajjwal1/bert-tiny"""    )
      __SCREAMING_SNAKE_CASE     :   Optional[Any]      							=       BertTokenizer.from_pretrained("""bert-base-uncased"""    )

      __SCREAMING_SNAKE_CASE     :   Optional[Any]      							=       bertabert.config.encoder.vocab_size
      __SCREAMING_SNAKE_CASE     :   List[str]      							=       tokenizer.sep_token_id
      __SCREAMING_SNAKE_CASE     :   Optional[Any]      							=       tokenizer.cls_token_id
      __SCREAMING_SNAKE_CASE     :   Any      							=       128

      __SCREAMING_SNAKE_CASE     :   Dict      							=       datasets.load_dataset("""cnn_dailymail"""				,"""3.0.0"""				,split="""train[:1%]"""    )
      __SCREAMING_SNAKE_CASE     :   List[str]      							=       datasets.load_dataset("""cnn_dailymail"""				,"""3.0.0"""				,split="""validation[:1%]"""    )

      __SCREAMING_SNAKE_CASE     :   Dict      							=       train_dataset.select(range(32    )    )
      __SCREAMING_SNAKE_CASE     :   Tuple      							=       val_dataset.select(range(16    )    )

      __SCREAMING_SNAKE_CASE     :   int      							=       4

      def _map_to_encoder_decoder_inputs(lowerCamelCase				:			Any    ):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         __SCREAMING_SNAKE_CASE     :   Union[str, Any]      							=       tokenizer(batch["""article"""]				,padding="""max_length"""				,truncation=lowerCamelCase				,max_length=512    )
         __SCREAMING_SNAKE_CASE     :   Tuple      							=       tokenizer(batch["""highlights"""]				,padding="""max_length"""				,truncation=lowerCamelCase				,max_length=128    )
         __SCREAMING_SNAKE_CASE     :   Dict      							=       inputs.input_ids
         __SCREAMING_SNAKE_CASE     :   Dict      							=       inputs.attention_mask

         __SCREAMING_SNAKE_CASE     :   List[Any]      							=       outputs.input_ids
         __SCREAMING_SNAKE_CASE     :   List[Any]      							=       outputs.input_ids.copy()
         __SCREAMING_SNAKE_CASE     :   Optional[int]      							=       [
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
         ]
         __SCREAMING_SNAKE_CASE     :   Tuple      							=       outputs.attention_mask

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

         return batch

      def _compute_metrics(lowerCamelCase				:			Tuple    ):
         __SCREAMING_SNAKE_CASE     :   Union[str, Any]      							=       pred.label_ids
         __SCREAMING_SNAKE_CASE     :   List[Any]      							=       pred.predictions

         # all unnecessary tokens are removed
         __SCREAMING_SNAKE_CASE     :   Tuple      							=       tokenizer.batch_decode(lowerCamelCase				,skip_special_tokens=lowerCamelCase    )
         __SCREAMING_SNAKE_CASE     :   Optional[Any]      							=       tokenizer.batch_decode(lowerCamelCase				,skip_special_tokens=lowerCamelCase    )

         __SCREAMING_SNAKE_CASE     :   List[str]      							=       sum([int(pred_str[i] == label_str[i]    ) for i in range(len(lowerCamelCase    )    )]    ) / len(lowerCamelCase    )

         return {"accuracy": accuracy}

      # map train dataset
      __SCREAMING_SNAKE_CASE     :   Any      							=       train_dataset.map(
          _map_to_encoder_decoder_inputs				,batched=lowerCamelCase				,batch_size=lowerCamelCase				,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
      __SCREAMING_SNAKE_CASE     :   int      							=       val_dataset.map(
          _map_to_encoder_decoder_inputs				,batched=lowerCamelCase				,batch_size=lowerCamelCase				,remove_columns=["""article""", """highlights"""]				,)
      val_dataset.set_format(
          type="""torch"""				,columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]				,)

      __SCREAMING_SNAKE_CASE     :   str      							=       self.get_auto_remove_tmp_dir()

      __SCREAMING_SNAKE_CASE     :   Union[str, Any]      							=       SeqaSeqTrainingArguments(
          output_dir=lowerCamelCase				,per_device_train_batch_size=lowerCamelCase				,per_device_eval_batch_size=lowerCamelCase				,predict_with_generate=lowerCamelCase				,evaluation_strategy="""steps"""				,do_train=lowerCamelCase				,do_eval=lowerCamelCase				,warmup_steps=0				,eval_steps=2				,logging_steps=2				,)

      # instantiate trainer
      __SCREAMING_SNAKE_CASE     :   Optional[int]      							=       SeqaSeqTrainer(
          model=lowerCamelCase				,args=lowerCamelCase				,compute_metrics=_compute_metrics				,train_dataset=lowerCamelCase				,eval_dataset=lowerCamelCase				,tokenizer=lowerCamelCase				,)

      # start training
      trainer.train()












'''simple docstring'''

import functools
import operator

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


a        =						logging.get_logger(__name__)

a        =						{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'sew'





   def __init__(					self				:			str				,lowerCamelCase				:			Any=32				,lowerCamelCase				:			str=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			Union[str, Any]=12				,lowerCamelCase				:			Union[str, Any]=3072				,lowerCamelCase				:			int=2				,lowerCamelCase				:			Union[str, Any]="gelu"				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Optional[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Optional[Any]=0.02				,lowerCamelCase				:			List[str]=1E-5				,lowerCamelCase				:			Tuple="group"				,lowerCamelCase				:			Optional[Any]="gelu"				,lowerCamelCase				:			List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,lowerCamelCase				:			Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Optional[int]=False				,lowerCamelCase				:			Dict=128				,lowerCamelCase				:			Union[str, Any]=16				,lowerCamelCase				:			List[Any]=True				,lowerCamelCase				:			List[Any]=0.05				,lowerCamelCase				:			Optional[int]=10				,lowerCamelCase				:			Any=2				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Tuple=10				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple="mean"				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Dict=False				,lowerCamelCase				:			Optional[int]=256				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			Tuple=2				,**lowerCamelCase				:			Union[str, Any]				,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase				,pad_token_id=lowerCamelCase				,bos_token_id=lowerCamelCase				,eos_token_id=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       feat_extract_norm
      __SCREAMING_SNAKE_CASE      							=       feat_extract_activation
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       conv_bias
      __SCREAMING_SNAKE_CASE      							=       num_conv_pos_embeddings
      __SCREAMING_SNAKE_CASE      							=       num_conv_pos_embedding_groups
      __SCREAMING_SNAKE_CASE      							=       len(self.conv_dim    )
      __SCREAMING_SNAKE_CASE      							=       num_hidden_layers
      __SCREAMING_SNAKE_CASE      							=       intermediate_size
      __SCREAMING_SNAKE_CASE      							=       squeeze_factor
      __SCREAMING_SNAKE_CASE      							=       hidden_act
      __SCREAMING_SNAKE_CASE      							=       num_attention_heads
      __SCREAMING_SNAKE_CASE      							=       hidden_dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       feat_proj_dropout
      __SCREAMING_SNAKE_CASE      							=       final_dropout
      __SCREAMING_SNAKE_CASE      							=       layerdrop
      __SCREAMING_SNAKE_CASE      							=       layer_norm_eps
      __SCREAMING_SNAKE_CASE      							=       initializer_range
      __SCREAMING_SNAKE_CASE      							=       vocab_size

      if (
          (len(self.conv_stride    ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
          or (len(self.conv_dim    ) != self.num_feat_extract_layers)
      ):
         raise ValueError(
             """Configuration for convolutional layers is incorrect."""
             """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
             f"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
             f"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __SCREAMING_SNAKE_CASE      							=       apply_spec_augment
      __SCREAMING_SNAKE_CASE      							=       mask_time_prob
      __SCREAMING_SNAKE_CASE      							=       mask_time_length
      __SCREAMING_SNAKE_CASE      							=       mask_time_min_masks
      __SCREAMING_SNAKE_CASE      							=       mask_feature_prob
      __SCREAMING_SNAKE_CASE      							=       mask_feature_length
      __SCREAMING_SNAKE_CASE      							=       mask_feature_min_masks

      # ctc loss
      __SCREAMING_SNAKE_CASE      							=       ctc_loss_reduction
      __SCREAMING_SNAKE_CASE      							=       ctc_zero_infinity

      # sequence classification
      __SCREAMING_SNAKE_CASE      							=       use_weighted_layer_sum
      __SCREAMING_SNAKE_CASE      							=       classifier_proj_size





   @property
   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      return functools.reduce(operator.mul				,self.conv_stride				,1    )





