
import argparse
import os
import platform

import numpy as np
import psutil
import torch

from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file

from ..utils import is_npu_available, is_xpu_available



def   _a  ( __UpperCamelCase							:	Optional[int]=None     ):
			if subparsers is not None:
						lowerCAmelCase__			:     str					   =      subparsers.add_parser('''env'''     )
			else:
						lowerCAmelCase__			:     List[str]					   =      argparse.ArgumentParser('''Accelerate env command'''     )

			parser.add_argument(
			    '''--config_file'''						,default=__UpperCamelCase						,help='''The config file to use for the default values in the launching script.'''     )

			if subparsers is not None:
						parser.set_defaults(func=__UpperCamelCase     )
			return parser



def   _a  ( __UpperCamelCase							:	str     ):
			lowerCAmelCase__			:     int					   =      torch.__version__
			lowerCAmelCase__			:     List[str]					   =      torch.cuda.is_available()
			lowerCAmelCase__			:     int					   =      is_xpu_available()
			lowerCAmelCase__			:     str					   =      is_npu_available()

			lowerCAmelCase__			:     Tuple					   =      '''Not found'''
			# Get the default from the config file.
			if args.config_file is not None or os.path.isfile(__UpperCamelCase     ):
						lowerCAmelCase__			:     str					   =      load_config_from_file(args.config_file     ).to_dict()

			lowerCAmelCase__			:     Union[str, Any]					   =      {
			    '''`Accelerate` version''': version,
			    '''Platform''': platform.platform(),
			    '''Python version''': platform.python_version(),
			    '''Numpy version''': np.__version__,
			    '''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
			    '''PyTorch XPU available''': str(__UpperCamelCase     ),
			    '''PyTorch NPU available''': str(__UpperCamelCase     ),
			    '''System RAM''': f'''{psutil.virtual_memory().total / 1024 ** 3:.2f} GB''',
			}
			if pt_cuda_available:
						lowerCAmelCase__			:     List[Any]					   =      torch.cuda.get_device_name()

			print('''\nCopy-and-paste the text below in your GitHub issue\n'''     )
			print('''\n'''.join([f'''- {prop}: {val}''' for prop, val in info.items()]     )     )

			print('''- `Accelerate` default config:''' if args.config_file is None else '''- `Accelerate` config passed:'''     )
			lowerCAmelCase__			:     List[str]					   =      (
			    '''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]     )
			    if isinstance(__UpperCamelCase						,__UpperCamelCase     )
			    else f'''\t{accelerate_config}'''
			)
			print(__UpperCamelCase     )

			lowerCAmelCase__			:     Optional[Any]					   =      accelerate_config

			return info



def   _a  ( ):
			lowerCAmelCase__			:     Union[str, Any]					   =      env_command_parser()
			lowerCAmelCase__			:     int					   =      parser.parse_args()
			env_command(__UpperCamelCase     )
			return 0


if __name__ == "__main__":
							raise SystemExit(main())




"""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					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _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(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					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(__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						:	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()
