





"""simple docstring"""


# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


a__					:     Any							=				logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer



   class      lowercase_				(     __lowerCAmelCase							):

          def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 101     ):






           '''simple docstring'''

           snake_case__    							=				length

          def __len__(			self     ):






           '''simple docstring'''

           return self.length

          def __getitem__(			self   ,					__SCREAMING_SNAKE_CASE     ):






           '''simple docstring'''

           return i



   class      lowercase_				:

          def __call__(			self   ,					__SCREAMING_SNAKE_CASE     ):






           '''simple docstring'''

           return {"input_ids": torch.tensor(__SCREAMING_SNAKE_CASE     ), "labels": torch.tensor(__SCREAMING_SNAKE_CASE     )}



   class      lowercase_				(     nn.Module							):

          def __init__(			self     ):






           '''simple docstring'''

           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           snake_case__    							=				nn.Linear(120   ,					80     )

          def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=None     ):






           '''simple docstring'''

           if labels is not None:
            return torch.tensor(0.0   ,					device=input_ids.device     ), input_ids
           else:
            return input_ids





def 							a__			(  )    ->  List[Any]:

 '''simple docstring'''






 snake_case__    							=				HfArgumentParser((TrainingArguments,)	)
 sys.argv += ["--output_dir", "./examples"]
 snake_case__    							=				parser.parse_args_into_dataclasses()[0]

 logger.warning(
     f"Process rank: {training_args.local_rank}, device: {training_args.device}, "
     f"tpu_num_cores: {training_args.tpu_num_cores}"	,     )

 # Essentially, what we want to verify in the distributed case is
 # that we get all samples back, in the right order.
 # (this is crucial for prediction for instance)
 for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
  snake_case__    							=				DummyDataset(_UpperCAmelCase	)

  def compute_metrics(_UpperCAmelCase	) -> Dict:
   snake_case__    							=				list(range(len(_UpperCAmelCase	)	)	)
   snake_case__    							=				p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
   return {"success": success}

  snake_case__    							=				Trainer(
      model=DummyModel()	,     args=_UpperCAmelCase	,     data_collator=DummyDataCollator()	,     eval_dataset=_UpperCAmelCase	,     compute_metrics=_UpperCAmelCase	,     )
  snake_case__    							=				trainer.evaluate()
  logger.info(_UpperCAmelCase	)
  if metrics["eval_success"] is not True:
   logger.error(_UpperCAmelCase	)
   exit(1	)

  snake_case__    							=				trainer.predict(_UpperCAmelCase	)
  logger.info(p.metrics	)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics	)
   exit(1	)

  snake_case__    							=				2

  snake_case__    							=				trainer.evaluate()
  logger.info(_UpperCAmelCase	)
  if metrics["eval_success"] is not True:
   logger.error(_UpperCAmelCase	)
   exit(1	)

  snake_case__    							=				trainer.predict(_UpperCAmelCase	)
  logger.info(p.metrics	)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics	)
   exit(1	)

  snake_case__    							=				None

 logger.info("🔥 All distributed tests successful"	)





def 							a__			(  _UpperCAmelCase	)    ->  List[Any]:

 '''simple docstring'''






 main()


if __name__ == "__main__":
   main()








"""simple docstring"""


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


a__					:     Tuple							=				logging.get_logger(__name__)

a__					:     str							=				{
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}



class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							Tuple											=     """regnet"""
       snake_case__				:							List[Any]											=     ["""x""", """y"""]

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=[128, 192, 512, 1088]   ,					__SCREAMING_SNAKE_CASE=[2, 6, 12, 2]   ,					__SCREAMING_SNAKE_CASE=64   ,					__SCREAMING_SNAKE_CASE="y"   ,					__SCREAMING_SNAKE_CASE="relu"   ,					**__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE     )
        if layer_type not in self.layer_types:
         raise ValueError(F"layer_type={layer_type} is not one of {','.join(self.layer_types     )}"     )
        snake_case__    							=				num_channels
        snake_case__    							=				embedding_size
        snake_case__    							=				hidden_sizes
        snake_case__    							=				depths
        snake_case__    							=				groups_width
        snake_case__    							=				layer_type
        snake_case__    							=				hidden_act
        # always downsample in the first stage
        snake_case__    							=				True

