






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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     @slow
     @require_torch
     def 					_A							(					self							:						Dict   ):
       SCREAMING_SNAKE_CASE							:   str						    =     EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"       ,			"prajjwal1/bert-tiny"   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     BertTokenizer.from_pretrained("bert-base-uncased"   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     bertabert.config.encoder.vocab_size
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tokenizer.sep_token_id
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     tokenizer.cls_token_id
       SCREAMING_SNAKE_CASE							:   List[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							:   List[str]						    =     train_dataset.select(range(32   )   )
       SCREAMING_SNAKE_CASE							:   int						    =     val_dataset.select(range(16   )   )

       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     4

       def _map_to_encoder_decoder_inputs(UpperCAmelCase_							:						Optional[Any]   ):
         # Tokenizer will automatically set [BOS] <text> [EOS]
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tokenizer(batch["article"]       ,			padding="max_length"       ,			truncation=UpperCAmelCase_       ,			max_length=512   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     tokenizer(batch["highlights"]       ,			padding="max_length"       ,			truncation=UpperCAmelCase_       ,			max_length=128   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     inputs.input_ids
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     inputs.attention_mask

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     outputs.input_ids
         SCREAMING_SNAKE_CASE							:   List[str]						    =     outputs.input_ids.copy()
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [
             [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
         ]
         SCREAMING_SNAKE_CASE							:   str						    =     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_							:						Optional[Any]   ):
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     pred.label_ids
         SCREAMING_SNAKE_CASE							:   Any						    =     pred.predictions

         # all unnecessary tokens are removed
         SCREAMING_SNAKE_CASE							:   str						    =     tokenizer.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tokenizer.batch_decode(UpperCAmelCase_       ,			skip_special_tokens=UpperCAmelCase_   )

         SCREAMING_SNAKE_CASE							:   str						    =     sum([int(pred_str[i] == label_str[i]   ) for i in range(len(UpperCAmelCase_   )   )]   ) / len(UpperCAmelCase_   )

         return {"accuracy": accuracy}

       # map train dataset
       SCREAMING_SNAKE_CASE							:   Dict						    =     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
       SCREAMING_SNAKE_CASE							:   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"]       ,			)

       SCREAMING_SNAKE_CASE							:   Any						    =     self.get_auto_remove_tmp_dir()

       SCREAMING_SNAKE_CASE							:   str						    =     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
       SCREAMING_SNAKE_CASE							:   List[str]						    =     SeqaSeqTrainer(
           model=UpperCAmelCase_       ,			args=UpperCAmelCase_       ,			compute_metrics=_compute_metrics       ,			train_dataset=UpperCAmelCase_       ,			eval_dataset=UpperCAmelCase_       ,			tokenizer=UpperCAmelCase_       ,			)

       # start training
       trainer.train()










import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )




@require_torch
class A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	int					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =3
             _lowerCamelCase			:						Dict            =250

             _lowerCamelCase			:						Tuple            =ids_tensor((batch_size, length)     ,  lowercase_					)
             _lowerCamelCase			:						str            =torch.ones((batch_size, length)     ,  device=lowercase_     ,  dtype=torch.float					) / length
             return input_ids, scores






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Dict            =StoppingCriteriaList(
                 [
                     MaxLengthCriteria(max_length=10					),
                     MaxTimeCriteria(max_time=0.1					),
                 ]					)

             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Any            =MaxLengthCriteria(max_length=10					)

             _lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[int]            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =MaxNewTokensCriteria(start_length=5     ,  max_new_tokens=5					)

             _lowerCamelCase      ,	_lowerCamelCase			:						str            =self._get_tensors(5					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self._get_tensors(9					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =self._get_tensors(10					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						Optional[Any]            =StoppingCriteriaList([criteria]					)
             self.assertEqual(criteria_list.max_length     ,  10					)






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase			:						Optional[Any]            =self._get_tensors(5					)

             _lowerCamelCase			:						Tuple            =MaxTimeCriteria(max_time=0.1					)
             self.assertFalse(criteria(lowercase_     ,  lowercase_					)					)

             _lowerCamelCase			:						int            =MaxTimeCriteria(max_time=0.1     ,  initial_timestamp=time.time() - 0.2					)
             self.assertTrue(criteria(lowercase_     ,  lowercase_					)					)






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[int]:
             """simple docstring"""




             validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  10					)

             with self.assertWarns(lowercase_					):
                    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10					)]					)     ,  11					)

             _lowerCamelCase			:						Optional[Any]            =validate_stopping_criteria(StoppingCriteriaList()     ,  11					)

             self.assertEqual(len(lowercase_					)     ,  1					)


