



"""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


__SCREAMING_SNAKE_CASE	:    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 				lowerCamelCase_( _SCREAMING_SNAKE_CASE     ):





								'''simple docstring'''



								def __init__(     self					,							lowerCamelCase__ = 1_0_1 ):
												_lowerCamelCase   	=					length

								def __len__(     self ):
												return self.length





								def __getitem__(     self					,							lowerCamelCase__ ):
												return i


			class 				lowerCamelCase_:





								'''simple docstring'''



								def __call__(     self					,							lowerCamelCase__ ):
												return {"input_ids": torch.tensor(_lowerCAmelCase ), "labels": torch.tensor(_lowerCAmelCase )}


			class 				lowerCamelCase_( nn.Module     ):





								'''simple docstring'''



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





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


class 				lowerCamelCase_( _SCREAMING_SNAKE_CASE     ):





					'''simple docstring'''



					@require_torch_neuroncore
					def       snake_case__   (     self ):
									_lowerCamelCase   	=					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   	=					self.get_auto_remove_tmp_dir()
									_lowerCamelCase   	=					F"""--output_dir {output_dir}""".split()
									_lowerCamelCase   	=					['''torchrun'''] + distributed_args + args
									execute_subprocess_async(_lowerCAmelCase					,							env=self.get_env() )
									# successful return here == success - any errors would have caused an error in the sub-call


class 				lowerCamelCase_( _SCREAMING_SNAKE_CASE     ):





					'''simple docstring'''



					@require_torch_multi_gpu
					def       snake_case__   (     self ):
									_lowerCamelCase   	=					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   	=					self.get_auto_remove_tmp_dir()
									_lowerCamelCase   	=					F"""--output_dir {output_dir}""".split()
									_lowerCamelCase   	=					['''torchrun'''] + distributed_args + args
									execute_subprocess_async(_lowerCAmelCase					,							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

			__SCREAMING_SNAKE_CASE	:    int     				=   HfArgumentParser((TrainingArguments,))
			__SCREAMING_SNAKE_CASE	:    Optional[int]     				=   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 [1_0_1, 4_0, 7]:
						__SCREAMING_SNAKE_CASE	:    str     				=   DummyDataset(dataset_length)



						def 							lowerCAmelCase_( lowercase_  :				EvalPrediction				)						->							Dict:
										_lowerCamelCase   	=					list(range(len(__UpperCAmelCase				)				)				)
										_lowerCamelCase   	=					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}

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

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

						__SCREAMING_SNAKE_CASE	:    List[str]     				=   2

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

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




						__SCREAMING_SNAKE_CASE	:    Union[str, Any]     				=   None






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase__					:  List[Any]      							=				TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				[
       'MT5EncoderModel',
       'MT5ForConditionalGeneration',
       'MT5ForQuestionAnswering',
       'MT5Model',
       'MT5PreTrainedModel',
       'MT5Stack',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   lowerCamelCase__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )