


'''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


__snake_case							      =     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__					):

				"""simple docstring"""

				def __init__(		self    ,   UpperCamelCase_ = 101							):

									'''simple docstring'''


									UpperCamelCase__   :int												= length




				def __len__(		self							):

									'''simple docstring'''


									return self.length




				def __getitem__(		self    ,   UpperCamelCase_							):

									'''simple docstring'''


									return i



		class    lowercase	:

				"""simple docstring"""

				def __call__(		self    ,   UpperCamelCase_							):

									'''simple docstring'''


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



		class    lowercase	(							nn.Module					):

				"""simple docstring"""

				def __init__(		self							):

									'''simple docstring'''


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




				def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_=None							):

									'''simple docstring'''


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

		"""simple docstring"""

		@require_torch_neuroncore
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= F'''--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							UpperCamelCase__   :Optional[Any]												= self.get_auto_remove_tmp_dir()
							UpperCamelCase__   :Tuple												= F'''--output_dir {output_dir}'''.split()
							UpperCamelCase__   :Tuple												= ['''torchrun'''] + distributed_args + args
							execute_subprocess_async(UpperCamelCase_    ,   env=self.get_env()							)
							# successful return here == success - any errors would have caused an error in the sub-call



class    lowercase	(							A__					):

		"""simple docstring"""

		@require_torch_multi_gpu
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= F'''--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        '''.split()
							UpperCamelCase__   :int												= self.get_auto_remove_tmp_dir()
							UpperCamelCase__   :List[Any]												= F'''--output_dir {output_dir}'''.split()
							UpperCamelCase__   :str												= ['''torchrun'''] + distributed_args + args
							execute_subprocess_async(UpperCamelCase_    ,   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

		__snake_case							      =     HfArgumentParser((TrainingArguments,))
		__snake_case							      =     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]:
				__snake_case							      =     DummyDataset(dataset_length)


				def 			a      (			__a   )  ->	Dict:



									'''simple docstring'''






									UpperCamelCase__   :List[Any]												= list(range(len(__a   )   )   )
									UpperCamelCase__   :Optional[int]												= 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}

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

				__snake_case							      =     trainer.predict(dataset)
				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(metrics)
				if metrics["eval_success"] is not True:
						logger.error(metrics)
						exit(1)

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







				__snake_case							      =     None


# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


snake_case_			  = logging.get_logger(__name__)

snake_case_			  = {}
snake_case_			  = {}
snake_case_			  = {}

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    type	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None	,     ):



     '''simple docstring'''





     lowercase__    :   Union[str, Any]      =       aliases if aliases is not None else []
     if format_type in _FORMAT_TYPES:
          logger.warning(
              f"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""			)
     lowercase__    :   Any      =       formatter_cls
     for alias in set(aliases + [format_type]			):
          if alias in _FORMAT_TYPES_ALIASES:
               logger.warning(
                   f"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""			)
          lowercase__    :   int      =       format_type

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Exception	,     SCREAMING_SNAKE_CASE_		:    Optional[str]	,     SCREAMING_SNAKE_CASE_		:    Optional[List[str]] = None			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       aliases if aliases is not None else []
     for alias in set(aliases + [format_type]			):
          lowercase__    :   Any      =       unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
       from .torch_formatter import TorchFormatter

       _register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
       snake_case_			  = ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
       _register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
       from .tf_formatter import TFFormatter

       _register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
       snake_case_			  = ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
       _register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
       from .jax_formatter import JaxFormatter

       _register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
       snake_case_			  = ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
       _register_unavailable_formatter(_jax_error, '''jax''', aliases=[])

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]			):



     '''simple docstring'''





     if format_type in _FORMAT_TYPES_ALIASES:
          return _FORMAT_TYPES_ALIASES[format_type]
     else:
          return format_type

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    Optional[str]	,     **SCREAMING_SNAKE_CASE_		:    Any			):



     '''simple docstring'''





     lowercase__    :   Tuple      =       get_format_type_from_alias(SCREAMING_SNAKE_CASE_			)
     if format_type in _FORMAT_TYPES:
          return _FORMAT_TYPES[format_type](**SCREAMING_SNAKE_CASE_			)
     if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
          raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
     else:
          raise ValueError(
              f"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got '{format_type}'"""			)

