'''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        a__ (  a_					):






       def __init__(					self						:						List[Any]    ,							a						:						Dict = 1_01       ):





           """simple docstring"""



           __lowerCamelCase            =    length






       def __len__(					self						:						Optional[Any]       ):





           """simple docstring"""



           return self.length






       def __getitem__(					self						:						List[str]    ,							a						:						Union[str, Any]       ):





           """simple docstring"""



           return i




  class        a__ :






       def __call__(					self						:						List[str]    ,							a						:						Any       ):





           """simple docstring"""



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




  class        a__ (  nn.Module					):






       def __init__(					self						:						List[str]       ):





           """simple docstring"""



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






       def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Any    ,							a						:						Any=None       ):





           """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   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    f"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         __lowerCamelCase            =    self.get_auto_remove_tmp_dir()
         __lowerCamelCase            =    f"""--output_dir {output_dir}""".split()
         __lowerCamelCase            =    ["torchrun"] + distributed_args + args
         execute_subprocess_async(_a    ,							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   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    f"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         __lowerCamelCase            =    self.get_auto_remove_tmp_dir()
         __lowerCamelCase            =    f"""--output_dir {output_dir}""".split()
         __lowerCamelCase            =    ["torchrun"] + distributed_args + args
         execute_subprocess_async(_a    ,							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 [1_0_1, 4_0, 7]:
    __UpperCAmelCase					=DummyDataset(dataset_length)






    def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Dict:
        __lowerCamelCase            =    list(range(len(__snake_case							)							)							)
        __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}

    __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"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

          lowercase       :							List[Any]									=				vocab_size
          lowercase       :							Optional[Any]									=				max_position_embeddings
          lowercase       :							str									=				hidden_size
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Optional[Any]									=				num_attention_heads
          lowercase       :							List[Any]									=				intermediate_size
          lowercase       :							int									=				hidden_act
          lowercase       :							str									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							Tuple									=				initializer_range
          lowercase       :							Optional[int]									=				type_vocab_size
          lowercase       :							str									=				layer_norm_eps
          lowercase       :							Tuple									=				use_cache

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


