


"""simple docstring"""





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


__UpperCAmelCase     					=   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_	(			__A    ):







         def __init__(   self			:				str		,	_lowercase			:				Dict = 1_0_1     ):
           lowerCAmelCase__     :     Any										=       length







         def __len__(   self			:				Optional[Any]     ):
           return self.length







         def __getitem__(   self			:				Union[str, Any]		,	_lowercase			:				int     ):
           return i

   class 			lowercase_	:







         def __call__(   self			:				Any		,	_lowercase			:				Optional[Any]     ):
           return {"input_ids": torch.tensor(snake_case_     ), "labels": torch.tensor(snake_case_     )}

   class 			lowercase_	(			nn.Module    ):







         def __init__(   self			:				Dict     ):
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           lowerCAmelCase__     :     str										=       nn.Linear(1_2_0		,	8_0     )







         def 					_lowerCAmelCase							(   self			:				List[Any]		,	_lowercase			:				str		,	_lowercase			:				int=None     ):


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

class 			lowercase_	(			__A    ):







      @require_torch_neuroncore
      def 					_lowerCAmelCase							(   self			:				Optional[int]     ):
        lowerCAmelCase__     :     str										=       f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowerCAmelCase__     :     int										=       self.get_auto_remove_tmp_dir()
        lowerCAmelCase__     :     int										=       f"--output_dir {output_dir}".split()
        lowerCAmelCase__     :     Optional[int]										=       ["""torchrun"""] + distributed_args + args
        execute_subprocess_async(snake_case_		,	env=self.get_env()     )
        # successful return here == success - any errors would have caused an error in the sub-call

class 			lowercase_	(			__A    ):







      @require_torch_multi_gpu
      def 					_lowerCAmelCase							(   self			:				Union[str, Any]     ):
        lowerCAmelCase__     :     str										=       f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowerCAmelCase__     :     Tuple										=       self.get_auto_remove_tmp_dir()
        lowerCAmelCase__     :     List[str]										=       f"--output_dir {output_dir}".split()
        lowerCAmelCase__     :     int										=       ["""torchrun"""] + distributed_args + args
        execute_subprocess_async(snake_case_		,	env=self.get_env()     )
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
   # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
   #
   # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

   __UpperCAmelCase     					=   HfArgumentParser((TrainingArguments,))
   __UpperCAmelCase     					=   parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
       f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
   )

   # 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 [101, 40, 7]:
      __UpperCAmelCase     					=   DummyDataset(dataset_length)





      def      lowercase__						(	lowerCamelCase  :	int					)       ->  str:
        lowerCAmelCase__     :     Union[str, Any]										=       list(range(len(lowerCamelCase					)					)					)
        lowerCAmelCase__     :     Tuple										=       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
        if not success and training_args.local_rank == 0:
          logger.warning(
              "Predictions and/or labels do not match expected results:\n  - predictions: "
              F"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"					)
        return {"success": success}

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

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

      __UpperCAmelCase     					=   2

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

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







      __UpperCAmelCase     					=   None

"""simple docstring"""



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin
class 						__UpperCAmelCase    (unittest.TestCase							,   __A     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[Any]								=			load_tool("""text-classification"""							)
										self.tool.setup()
										A__       :     Any								=			load_tool("""text-classification"""   ,      remote=snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			self.tool("""That's quite cool"""   ,      ["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.remote_tool("""That's quite cool"""   ,      ["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     str								=			self.tool(text="""That's quite cool"""   ,      labels=["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     int								=			self.remote_tool(text="""That's quite cool"""   ,      labels=["""positive""", """negative"""]							)
										self.assertEqual(snake_case_   ,      """positive"""							)





