

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


a_    :						Optional[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      UpperCamelCase (     UpperCamelCase_   ):

        def __init__(			self :      int							,    snake_case__ :      Dict = 1_0_1    ):

         """simple docstring"""






         SCREAMING_SNAKE_CASE								=			length

        def __len__(			self :      List[Any]    ):

         """simple docstring"""






         return self.length




        def __getitem__(			self :      Union[str, Any]							,    snake_case__ :      Tuple    ):

         """simple docstring"""






         return i




    class      UpperCamelCase :

        def __call__(			self :      Dict							,    snake_case__ :      List[str]    ):

         """simple docstring"""






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




    class      UpperCamelCase (     nn.Module   ):

        def __init__(			self :      Any    ):

         """simple docstring"""






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




        def UpperCamelCase			(			self :      int							,    snake_case__ :      int							,    snake_case__ :      Union[str, 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      UpperCamelCase (     UpperCamelCase_   ):

    @require_torch_neuroncore
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""--output_dir {output_dir}""".split()
     SCREAMING_SNAKE_CASE								=			['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      UpperCamelCase (     UpperCamelCase_   ):

    @require_torch_multi_gpu
    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
     SCREAMING_SNAKE_CASE								=			self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE								=			F"""--output_dir {output_dir}""".split()
     SCREAMING_SNAKE_CASE								=			['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

    a_    :						Optional[int]    		=      HfArgumentParser((TrainingArguments,))
    a_    :						Union[str, 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]:
        a_    :						Dict    		=      DummyDataset(dataset_length)


        def        __lowerCAmelCase    (       _UpperCamelCase		:  EvalPrediction	)		->							Dict:
         '''simple docstring'''


         SCREAMING_SNAKE_CASE								=			list(range(len(_UpperCamelCase	)	)	)
         SCREAMING_SNAKE_CASE								=			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}

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

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

        a_    :						List[Any]    		=      2

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

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




        a_    :						Optional[Any]    		=      None




import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          NystromformerForMaskedLM,
          NystromformerForMultipleChoice,
          NystromformerForQuestionAnswering,
          NystromformerForSequenceClassification,
          NystromformerForTokenClassification,
          NystromformerModel,
      )
      from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCamelCase :

    def __init__(			self :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      Optional[Any]=1_3							,    snake_case__ :      Union[str, Any]=7							,    snake_case__ :      List[str]=True							,    snake_case__ :      Any=True							,    snake_case__ :      List[str]=True							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      List[str]=9_9							,    snake_case__ :      str=3_2							,    snake_case__ :      Dict=5							,    snake_case__ :      str=4							,    snake_case__ :      int=3_7							,    snake_case__ :      Union[str, Any]="gelu"							,    snake_case__ :      Any=0.1							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Optional[Any]=5_1_2							,    snake_case__ :      List[Any]=1_6							,    snake_case__ :      str=2							,    snake_case__ :      int=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      Dict=4							,    snake_case__ :      str=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			seq_length
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_input_mask
     SCREAMING_SNAKE_CASE								=			use_token_type_ids
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			vocab_size
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			max_position_embeddings
     SCREAMING_SNAKE_CASE								=			type_vocab_size
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			num_labels
     SCREAMING_SNAKE_CASE								=			num_choices
     SCREAMING_SNAKE_CASE								=			scope

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     if self.use_labels:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.num_choices    )

     SCREAMING_SNAKE_CASE								=			self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     return NystromformerConfig(
         vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=snake_case__							,    initializer_range=self.initializer_range							,    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[Any]							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForQuestionAnswering(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    start_positions=snake_case__							,    end_positions=snake_case__							,    )
     self.parent.assertEqual(result.start_logits.shape							,    (self.batch_size, self.seq_length)    )
     self.parent.assertEqual(result.end_logits.shape							,    (self.batch_size, self.seq_length)    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)    )

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForTokenClassification(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_choices
     SCREAMING_SNAKE_CASE								=			NystromformerForMultipleChoice(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			input_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			token_type_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			input_mask.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__							,    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)    )




    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (
            NystromformerModel,
            NystromformerForMaskedLM,
            NystromformerForMultipleChoice,
            NystromformerForQuestionAnswering,
            NystromformerForSequenceClassification,
            NystromformerForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    __UpperCamelCase         =(
        {
            "feature-extraction": NystromformerModel,
            "fill-mask": NystromformerForMaskedLM,
            "question-answering": NystromformerForQuestionAnswering,
            "text-classification": NystromformerForSequenceClassification,
            "token-classification": NystromformerForTokenClassification,
            "zero-shot": NystromformerForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE								=			type
      self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__    )




    @slow
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE								=			NystromformerModel.from_pretrained(snake_case__    )
      self.assertIsNotNone(snake_case__    )




@require_torch
class      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModel.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([[0, 1, 2, 3, 4, 5]]    )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(snake_case__    )[0]

     SCREAMING_SNAKE_CASE								=			torch.Size((1, 6, 7_6_8)    )
     self.assertEqual(output.shape							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			torch.tensor(
         [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]    )

     self.assertTrue(torch.allclose(output[:, :3, :3]							,    snake_case__							,    atol=1E-4    )    )




    @slow
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'the [MASK] of Belgium is Brussels'

     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'    )

     SCREAMING_SNAKE_CASE								=			tokenizer(snake_case__							,    return_tensors='pt'    )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(encoding.input_ids    ).logits

     SCREAMING_SNAKE_CASE								=			token_logits[:, 2, :].argmax(-1    )[0]

     self.assertEqual(tokenizer.decode(snake_case__    )							,    'capital'    )

