





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 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @slow
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''      )
         __snake_case							       =   AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''      )
         model.to(_UpperCamelCase      )

         from datasets import load_dataset

         __snake_case							       =   load_dataset('''nielsr/rvlcdip-demo'''      )

         __snake_case							       =   dataset['''train'''][0]['''image'''].convert('''RGB'''      )

         __snake_case							       =   image_processor(_UpperCamelCase				,			return_tensors='''pt'''      ).to(_UpperCamelCase      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(**_UpperCamelCase      )
            __snake_case							       =   outputs.logits

         __snake_case							       =   torch.Size((1, 16)      )
         self.assertEqual(logits.shape				,			_UpperCamelCase      )

         __snake_case							       =   torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]				,			device=_UpperCamelCase				,			dtype=torch.float				,			)
         self.assertTrue(torch.allclose(logits[0, :3]				,			_UpperCamelCase				,			atol=1E-4      )      )




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

					lowerCAmelCase_       	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)