
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def __init__(    self	:	Dict						,__A	:	Union[str, Any]						,__A	:	Optional[Any]=13						,__A	:	List[str]=30						,__A	:	List[str]=2						,__A	:	Dict=3						,__A	:	Tuple=True						,__A	:	Optional[int]=True						,__A	:	Any=32						,__A	:	List[str]=5						,__A	:	Optional[int]=4						,__A	:	List[Any]=37						,__A	:	int="gelu"						,__A	:	int=0.1						,__A	:	Optional[int]=0.1						,__A	:	Union[str, Any]=10						,__A	:	Tuple=0.02						,)						->					Union[str, Any]:
         _lowercase							= parent
         _lowercase							= batch_size
         _lowercase							= image_size
         _lowercase							= patch_size
         _lowercase							= num_channels
         _lowercase							= is_training
         _lowercase							= use_labels
         _lowercase							= hidden_size
         _lowercase							= num_hidden_layers
         _lowercase							= num_attention_heads
         _lowercase							= intermediate_size
         _lowercase							= hidden_act
         _lowercase							= hidden_dropout_prob
         _lowercase							= attention_probs_dropout_prob
         _lowercase							= type_sequence_label_size
         _lowercase							= initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         _lowercase							= (image_size // patch_size) ** 2
         _lowercase							= num_patches + 1







       def    __UpperCAmelCase (    self	:	Tuple  )						->					Optional[int]:
         _lowercase							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         _lowercase							= ViTConfig(
             image_size=self.image_size						,patch_size=self.patch_size						,num_channels=self.num_channels						,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						,is_decoder=_a						,initializer_range=self.initializer_range						,)

         return config, pixel_values







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	Tuple						,__A	:	Tuple  )						->					Optional[int]:
         _lowercase							= FlaxViTModel(config=_a  )
         _lowercase							= model(_a  )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         _lowercase							= (self.image_size, self.image_size)
         _lowercase							= (self.patch_size, self.patch_size)
         _lowercase							= (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, num_patches + 1, self.hidden_size)  )







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Any						,__A	:	Union[str, Any]  )						->					int:
         _lowercase							= self.type_sequence_label_size
         _lowercase							= FlaxViTForImageClassification(config=_a  )
         _lowercase							= model(_a  )
         self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.type_sequence_label_size)  )

         # test greyscale images
         _lowercase							= 1
         _lowercase							= FlaxViTForImageClassification(_a  )

         _lowercase							= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         _lowercase							= model(_a  )







       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= self.prepare_config_and_inputs()
         (
             _lowercase					
         )							= config_and_inputs
         _lowercase							= {"""pixel_values""": pixel_values}
         return config, inputs_dict





@require_flax
class 				A_				(      __SCREAMING_SNAKE_CASE					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







       def    __UpperCAmelCase (    self	:	Dict  )						->					Optional[Any]:
         _lowercase							= FlaxViTModelTester(self  )
         _lowercase							= ConfigTester(self						,config_class=_a						,has_text_modality=_a						,hidden_size=37  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Dict:
         self.config_tester.run_common_tests()







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					int:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_a  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Tuple:
         _lowercase							= self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_a  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					int:
         _lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           _lowercase							= model_class(_a  )
           _lowercase							= inspect.signature(model.__call__  )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           _lowercase							= [*signature.parameters.keys()]

           _lowercase							= ["""pixel_values"""]
           self.assertListEqual(arg_names[:1]						,_a  )







       def    __UpperCAmelCase (    self	:	List[str]  )						->					Union[str, Any]:
         _lowercase							= self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
             _lowercase							= self._prepare_for_class(_a						,_a  )
             _lowercase							= model_class(_a  )

             @jax.jit
             def model_jitted(__A	:	Dict						,**__A	:	Dict  ):
               return model(pixel_values=_a						,**_a  )

             with self.subTest('JIT Enabled'  ):
               _lowercase							= model_jitted(**_a  ).to_tuple()

             with self.subTest('JIT Disabled'  ):
               with jax.disable_jit():
                 _lowercase							= model_jitted(**_a  ).to_tuple()

             self.assertEqual(len(_a  )						,len(_a  )  )
             for jitted_output, output in zip(_a						,_a  ):
               self.assertEqual(jitted_output.shape						,output.shape  )







       @slow
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Optional[int]:




         for model_class_name in self.all_model_classes:
           _lowercase							= model_class_name.from_pretrained('google/vit-base-patch16-224'  )
           _lowercase							= model(np.ones((1, 3, 224, 224)  )  )
           self.assertIsNotNone(_a  )






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 				A_				(      UpperCAmelCase	):







             """simple docstring"""





             def __init__(    self	:	str						,__A	:	int = 101  )						->					Optional[int]:
               _lowercase							= length







             def __len__(    self	:	List[Any]  )						->					Any:
               return self.length







             def __getitem__(    self	:	List[Any]						,__A	:	Optional[int]  )						->					int:
               return i





      class 				A_				:







             """simple docstring"""





             def __call__(    self	:	str						,__A	:	Union[str, Any]  )						->					Any:
               return {"input_ids": torch.tensor(__A  ), "labels": torch.tensor(__A  )}





      class 				A_				(      nn.Module	):







             """simple docstring"""





             def __init__(    self	:	Dict  )						->					Any:
               super().__init__()
               # Add some (unused) params otherwise DDP will complain.
               _lowercase							= nn.Linear(120						,80  )







             def    __UpperCAmelCase (    self	:	Dict						,__A	:	Dict						,__A	:	Any=None  )						->					Tuple:
               if labels is not None:
                 return torch.tensor(0.0						,device=input_ids.device  ), input_ids
               else:
                 return input_ids





class 				A_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_neuroncore
       def    __UpperCAmelCase (    self	:	int  )						->					Any:
         _lowercase							= F"""--nproc_per_node=2
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['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_				(      UpperCAmelCase	):







       """simple docstring"""





       @require_torch_multi_gpu
       def    __UpperCAmelCase (    self	:	Dict  )						->					List[Any]:
         _lowercase							= F"""--nproc_per_node={torch.cuda.device_count()}
            --master_port={get_torch_dist_unique_port()}
            {self.test_file_dir}/test_trainer_distributed.py
        """.split()
         _lowercase							= self.get_auto_remove_tmp_dir()
         _lowercase							= F"""--output_dir {output_dir}""".split()
         _lowercase							= ['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

      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 [1_0_1, 4_0, 7]:
            snake_case 				=       DummyDataset(dataset_length)

            def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :EvalPrediction     )      ->						Dict:
              _lowercase							= list(range(len(snake_case__     )     )     )
              _lowercase							= 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