






'''simple docstring'''


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 				lowerCAmelCase       (				unittest.TestCase ):







   """simple docstring"""







   def  __magic_name__     (			self      )     -> Union[str, Any]:
    __a     :						Optional[Any]          =    inspect.getfile(accelerate.test_utils      )
    __a     :						List[Any]          =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_script.py']      )
    __a     :						int          =    os.path.sep.join(
        mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_distributed_data_loop.py']      )
    __a     :						List[Any]          =    os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ['scripts', 'test_ops.py']      )



   @require_multi_gpu
   def  __magic_name__     (			self      )     -> Dict:
    print(f'''Found {torch.cuda.device_count()} devices.'''      )
    __a     :						Union[str, Any]          =    ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
    with patch_environment(omp_num_threads=1      ):
     execute_subprocess_async(_UpperCAmelCase , env=os.environ.copy()      )



   @require_multi_gpu
   def  __magic_name__     (			self      )     -> Optional[Any]:
    print(f'''Found {torch.cuda.device_count()} devices.'''      )
    __a     :						Union[str, Any]          =    ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
    print(f'''Command: {cmd}'''      )
    with patch_environment(omp_num_threads=1      ):
     execute_subprocess_async(_UpperCAmelCase , env=os.environ.copy()      )



   @require_multi_gpu
   def  __magic_name__     (			self      )     -> Any:
    __a     :						List[str]          =    ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
    with patch_environment(omp_num_threads=1      ):
     execute_subprocess_async(_UpperCAmelCase , env=os.environ.copy()      )



   @require_multi_gpu
   def  __magic_name__     (			self      )     -> List[str]:
    print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''      )
    __a     :						Any          =    ['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
    with patch_environment(omp_num_threads=1 , cuda_visible_devices='0,1'      ):
     execute_subprocess_async(_UpperCAmelCase , env=os.environ.copy()      )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_					   =				Accelerator()
  SCREAMING_SNAKE_CASE_					   =				(accelerator.state.process_index + 2, 1_0)
  SCREAMING_SNAKE_CASE_					   =				torch.randint(0, 1_0, shape).to(accelerator.device)

  SCREAMING_SNAKE_CASE_					   =				""""""

  SCREAMING_SNAKE_CASE_					   =				accelerator.pad_across_processes(tensor)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
    error_msg += "Padding was not done with the right value (0)."

  SCREAMING_SNAKE_CASE_					   =				accelerator.pad_across_processes(tensor, pad_first=True)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  SCREAMING_SNAKE_CASE_					   =				accelerator.state.num_processes - accelerator.state.process_index - 1
  if not torch.equal(tensora[index:], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[:index] == 0):
    error_msg += "Padding was not done with the right value (0)."

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)












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							(	UpperCAmelCase__     ):






  """simple docstring"""





  @slow
  @require_torch
  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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_						=  128

     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(32			)			)
     UpperCamelCase_						=  val_dataset.select(range(16			)			)

     UpperCamelCase_						=  4

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

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

        # all unnecessary tokens are removed
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)
        UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)

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

        return {"accuracy": accuracy}

     # map train dataset
     UpperCamelCase_						=  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
     UpperCamelCase_						=  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']		,		)

     UpperCamelCase_						=  self.get_auto_remove_tmp_dir()

     UpperCamelCase_						=  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
     UpperCamelCase_						=  SeqaSeqTrainer(
         model=_UpperCAmelCase		,		args=_UpperCAmelCase		,		compute_metrics=_compute_metrics		,		train_dataset=_UpperCAmelCase		,		eval_dataset=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		)

     # start training
     trainer.train()





