




import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


__a							     =					'scheduler_config.json'

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = 1
    lowerCAmelCase         = 2
    lowerCAmelCase         = 3
    lowerCAmelCase         = 4
    lowerCAmelCase         = 5
    lowerCAmelCase         = 6
    lowerCAmelCase         = 7
    lowerCAmelCase         = 8
    lowerCAmelCase         = 9
    lowerCAmelCase         = 10
    lowerCAmelCase         = 11
    lowerCAmelCase         = 12
    lowerCAmelCase         = 13
    lowerCAmelCase         = 14


@dataclass
class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = 42


class     __a:






    """simple docstring"""





    lowerCAmelCase         = SCHEDULER_CONFIG_NAME
    lowerCAmelCase         = []
    lowerCAmelCase         = True





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE=False  ,**_SCREAMING_SNAKE_CASE  ,)					->						List[str]:
         UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				str  =     cls.load_config(
             pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE  ,subfolder=_SCREAMING_SNAKE_CASE  ,return_unused_kwargs=_SCREAMING_SNAKE_CASE  ,return_commit_hash=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)
         return cls.from_config(_SCREAMING_SNAKE_CASE  ,return_unused_kwargs=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = False  ,**_SCREAMING_SNAKE_CASE    )					->						Dict:
         self.save_config(save_directory=_SCREAMING_SNAKE_CASE  ,push_to_hub=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )





    @property
    def 				a__ ( self    )					->						Tuple:
         return self._get_compatibles()





    @classmethod
    def 				a__ ( cls    )					->						Optional[Any]:
         UpperCAmelCase_     :				int  =     list(set([cls.__name__] + cls._compatibles    )    )
         UpperCAmelCase_     :				int  =     importlib.import_module(__name__.split('''.'''    )[0]    )
         UpperCAmelCase_     :				Any  =     [
             getattr(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    ) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         ]
         return compatible_classes





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
     import torch

     from transformers import AutoModelForImageClassification

if is_vision_available():
     from transformers import AutoImageProcessor

@require_torch
@require_vision
class     __a(	unittest.TestCase  ):






    """simple docstring"""





    @slow
    def 				a__ ( self    )					->						List[str]:
         UpperCAmelCase_     :				Tuple  =     AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         UpperCAmelCase_     :				Union[str, Any]  =     AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''    )
         model.to(_SCREAMING_SNAKE_CASE    )

         from datasets import load_dataset

         UpperCAmelCase_     :				Optional[int]  =     load_dataset('''nielsr/rvlcdip-demo'''    )

         UpperCAmelCase_     :				Optional[Any]  =     dataset['''train'''][0]['''image'''].convert('''RGB'''    )

         UpperCAmelCase_     :				str  =     image_processor(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).to(_SCREAMING_SNAKE_CASE    )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_     :				Optional[int]  =     model(**_SCREAMING_SNAKE_CASE    )
              UpperCAmelCase_     :				List[Any]  =     outputs.logits

         UpperCAmelCase_     :				Tuple  =     torch.Size((1, 16)    )
         self.assertEqual(logits.shape  ,_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				int  =     torch.tensor(
             [-0.41_58, -0.40_92, -0.43_47]  ,device=_SCREAMING_SNAKE_CASE  ,dtype=torch.float  ,)
         self.assertTrue(torch.allclose(logits[0, :3]  ,_SCREAMING_SNAKE_CASE  ,atol=1e-4    )    )