

"""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									=						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        _UpperCamelCase	(						lowerCamelCase__	):


   """simple docstring"""





   def __init__( self					: Dict       ,       snake_case					: int = 101      )	->			Any:


          '''simple docstring'''





          __magic_name__			:		Dict        =     length



   def __len__( self					: int      )	->			Optional[int]:


          '''simple docstring'''





          return self.length




   def __getitem__( self					: str       ,       snake_case					: Any      )	->			int:


          '''simple docstring'''





          return i

 class        _UpperCamelCase	:


   """simple docstring"""





   def __call__( self					: str       ,       snake_case					: str      )	->			Dict:


          '''simple docstring'''





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

 class        _UpperCamelCase	(						nn.Module	):


   """simple docstring"""





   def __init__( self					: List[str]      )	->			str:


          '''simple docstring'''





          super().__init__()
          # Add some (unused) params otherwise DDP will complain.
          __magic_name__			:		Union[str, Any]        =     nn.Linear(120       ,       80      )




   def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: str       ,       snake_case					: Optional[int]=None      )	->			Dict:


          '''simple docstring'''





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







def       UpperCamelCase_					(     )						->     str:



       """simple docstring"""







       __magic_name__			:		List[str]        =     HfArgumentParser((TrainingArguments,)   )
       sys.argv += ["--output_dir", "./examples"]
       __magic_name__			:		int        =     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 [1001, 256, 15]:
              __magic_name__			:		Optional[int]        =     DummyDataset(lowerCamelCase   )

              def compute_metrics(lowerCamelCase							:					EvalPrediction   ) -> Dict:
                     __magic_name__			:		List[Any]        =     list(range(len(lowerCamelCase   )   )   )
                     __magic_name__			:		Optional[int]        =     p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                     return {"success": success}

              __magic_name__			:		Union[str, Any]        =     Trainer(
                  model=DummyModel()     , args=lowerCamelCase     , data_collator=DummyDataCollator()     , eval_dataset=lowerCamelCase     , compute_metrics=lowerCamelCase     , )
              __magic_name__			:		Optional[int]        =     trainer.evaluate()
              logger.info(lowerCamelCase   )
              if metrics["eval_success"] is not True:
                     logger.error(lowerCamelCase   )
                     exit(1   )

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

              __magic_name__			:		List[str]        =     2

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

              __magic_name__			:		Union[str, Any]        =     trainer.predict(lowerCamelCase   )
              logger.info(p.metrics   )
              if p.metrics["test_success"] is not True:
                     logger.error(p.metrics   )
                     exit(1   )

              __magic_name__			:		Dict        =     None

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







def       UpperCamelCase_					(     lowerCamelCase							:					Tuple   )						->     Any:



       """simple docstring"""







       main()


if __name__ == "__main__":
 main()



"""simple docstring"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
