




'''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 : 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      A       (						_a       ):







											def __init__(    self			:    Any				,     lowerCAmelCase_			:    int = 1_01   )     ->				Dict:


																		"""simple docstring"""


																		_a      	=							length







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


																		"""simple docstring"""


																		return self.length







											def __getitem__(    self			:    Tuple				,     lowerCAmelCase_			:    List[Any]   )     ->				int:


																		"""simple docstring"""


																		return i



				class      A       :







											def __call__(    self			:    List[str]				,     lowerCAmelCase_			:    Any   )     ->				str:


																		"""simple docstring"""


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



				class      A       (						nn.Module       ):







											def __init__(    self			:    Any   )     ->				Tuple:


																		"""simple docstring"""


																		super().__init__()
																		# Add some (unused) params otherwise DDP will complain.
																		_a      	=							nn.Linear(1_20				,     80   )







											def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    List[str]				,     lowerCAmelCase_			:    List[str]=None   )     ->				int:


																		"""simple docstring"""


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

class      A       (						_a       ):







							@require_torch_neuroncore
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
														_a      	=							self.get_auto_remove_tmp_dir()
														_a      	=							F'--output_dir {output_dir}'.split()
														_a      	=							['''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      A       (						_a       ):







							@require_torch_multi_gpu
							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


														_a      	=							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()
														_a      	=							self.get_auto_remove_tmp_dir()
														_a      	=							F'--output_dir {output_dir}'.split()
														_a      	=							['''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

				_snake_case : List[str]  				=						HfArgumentParser((TrainingArguments,))
				_snake_case : Optional[Any]  				=						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 : Any  				=						DummyDataset(dataset_length)
								def 						snake_case_       (UpperCamelCase			:    EvalPrediction ):





															'''simple docstring'''


															_a      	=							list(range(len(UpperCamelCase ) ) )
															_a      	=							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 : Optional[Any]  				=						Trainer(
								    model=DummyModel(),
								    args=training_args,
								    data_collator=DummyDataCollator(),
								    eval_dataset=dataset,
								    compute_metrics=compute_metrics,
								)
								_snake_case : str  				=						trainer.evaluate()
								logger.info(metrics)
								if metrics["eval_success"] is not True:
												logger.error(metrics)
												exit(1)

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

								_snake_case : Any  				=						2

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

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

								_snake_case : Optional[Any]  				=						None




"""simple docstring"""

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


_UpperCamelCase		     =    logging.get_logger(__name__)

_UpperCamelCase		     =    {
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       __UpperCamelCase					:      str    =						'dpr'






       def __init__(   self			,					snake_case=30_522			,					snake_case=768			,					snake_case=12			,					snake_case=12			,					snake_case=3_072			,					snake_case="gelu"			,					snake_case=0.1			,					snake_case=0.1			,					snake_case=512			,					snake_case=2			,					snake_case=0.02			,					snake_case=1e-12			,					snake_case=0			,					snake_case="absolute"			,					snake_case = 0			,					**snake_case			,					):





        """simple docstring"""

        super().__init__(pad_token_id=snake_case			,					**snake_case   )

        lowerCAmelCase__   :				Dict 			=				vocab_size
        lowerCAmelCase__   :				int 			=				hidden_size
        lowerCAmelCase__   :				List[str] 			=				num_hidden_layers
        lowerCAmelCase__   :				List[Any] 			=				num_attention_heads
        lowerCAmelCase__   :				Any 			=				hidden_act
        lowerCAmelCase__   :				Any 			=				intermediate_size
        lowerCAmelCase__   :				str 			=				hidden_dropout_prob
        lowerCAmelCase__   :				List[str] 			=				attention_probs_dropout_prob
        lowerCAmelCase__   :				List[Any] 			=				max_position_embeddings
        lowerCAmelCase__   :				Optional[Any] 			=				type_vocab_size
        lowerCAmelCase__   :				Tuple 			=				initializer_range
        lowerCAmelCase__   :				Dict 			=				layer_norm_eps
        lowerCAmelCase__   :				Dict 			=				projection_dim
        lowerCAmelCase__   :				Any 			=				position_embedding_type






