


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				(  a__  ):





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






         """simple docstring"""







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

         UpperCAmelCase							=     bertabert.config.encoder.vocab_size
         UpperCAmelCase							=     tokenizer.sep_token_id
         UpperCAmelCase							=     tokenizer.cls_token_id
         UpperCAmelCase							=     1_2_8

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

         UpperCAmelCase							=     train_dataset.select(range(3_2       )       )
         UpperCAmelCase							=     val_dataset.select(range(1_6       )       )

         UpperCAmelCase							=     4

         def _map_to_encoder_decoder_inputs(__lowerCamelCase						:					Any       ):
            # Tokenizer will automatically set [BOS] <text> [EOS]
            UpperCAmelCase							=     tokenizer(batch["""article"""]					,       padding="""max_length"""					,       truncation=lowerCAmelCase__					,       max_length=5_1_2       )
            UpperCAmelCase							=     tokenizer(batch["""highlights"""]					,       padding="""max_length"""					,       truncation=lowerCAmelCase__					,       max_length=1_2_8       )
            UpperCAmelCase							=     inputs.input_ids
            UpperCAmelCase							=     inputs.attention_mask

            UpperCAmelCase							=     outputs.input_ids
            UpperCAmelCase							=     outputs.input_ids.copy()
            UpperCAmelCase							=     [
                [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
            ]
            UpperCAmelCase							=     outputs.attention_mask

            assert all(len(lowerCAmelCase__       ) == 5_1_2 for x in inputs.input_ids       )
            assert all(len(lowerCAmelCase__       ) == 1_2_8 for x in outputs.input_ids       )

            return batch

         def _compute_metrics(__lowerCamelCase						:					Optional[int]       ):
            UpperCAmelCase							=     pred.label_ids
            UpperCAmelCase							=     pred.predictions

            # all unnecessary tokens are removed
            UpperCAmelCase							=     tokenizer.batch_decode(lowerCAmelCase__					,       skip_special_tokens=lowerCAmelCase__       )
            UpperCAmelCase							=     tokenizer.batch_decode(lowerCAmelCase__					,       skip_special_tokens=lowerCAmelCase__       )

            UpperCAmelCase							=     sum([int(pred_str[i] == label_str[i]       ) for i in range(len(lowerCAmelCase__       )       )]       ) / len(lowerCAmelCase__       )

            return {"accuracy": accuracy}

         # map train dataset
         UpperCAmelCase							=     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
         UpperCAmelCase							=     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"""]					,       )

         UpperCAmelCase							=     self.get_auto_remove_tmp_dir()

         UpperCAmelCase							=     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
         UpperCAmelCase							=     SeqaSeqTrainer(
             model=lowerCAmelCase__					,       args=lowerCAmelCase__					,       compute_metrics=_compute_metrics					,       train_dataset=lowerCAmelCase__					,       eval_dataset=lowerCAmelCase__					,       tokenizer=lowerCAmelCase__					,       )

         # start training
         trainer.train()







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


__a     =			logging.get_logger(__name__)

__a     =			{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''ctrl'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }





      def __init__(		self						:					str					,       __lowerCamelCase						:					Optional[int]=2_4_6_5_3_4					,       __lowerCamelCase						:					Union[str, Any]=2_5_6					,       __lowerCamelCase						:					int=1_2_8_0					,       __lowerCamelCase						:					Optional[Any]=8_1_9_2					,       __lowerCamelCase						:					List[str]=4_8					,       __lowerCamelCase						:					Dict=1_6					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=1e-6					,       __lowerCamelCase						:					str=0.02					,       __lowerCamelCase						:					Union[str, Any]=True					,       **__lowerCamelCase						:					List[str]					,       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     n_positions
         UpperCAmelCase							=     n_embd
         UpperCAmelCase							=     n_layer
         UpperCAmelCase							=     n_head
         UpperCAmelCase							=     dff
         UpperCAmelCase							=     resid_pdrop
         UpperCAmelCase							=     embd_pdrop
         UpperCAmelCase							=     layer_norm_epsilon
         UpperCAmelCase							=     initializer_range

         UpperCAmelCase							=     use_cache

         super().__init__(**__lowerCamelCase       )



