"""simple docstring"""





import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def      lowerCamelCase_(*__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=2  )->    Dict:
							from .. import __version__

							_SCREAMING_SNAKE_CASE		:						int			  =    take_from
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ()
							if not isinstance(args[0]     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						int			  =    (args,)

							for attribute, version_name, message in args:
														if version.parse(version.parse(__SCREAMING_SNAKE_CASE  ).base_version  ) >= version.parse(__SCREAMING_SNAKE_CASE  ):
																					raise ValueError(
																					    F"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
																					    F""" version {__version__} is >= {version_name}"""  )

														_SCREAMING_SNAKE_CASE		:						str			  =    None
														if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) and attribute in deprecated_kwargs:
																					values += (deprecated_kwargs.pop(__SCREAMING_SNAKE_CASE  ),)
																					_SCREAMING_SNAKE_CASE		:						str			  =    F"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
														elif hasattr(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
																					values += (getattr(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ),)
																					_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    F"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
														elif deprecated_kwargs is None:
																					_SCREAMING_SNAKE_CASE		:						Tuple			  =    F"""`{attribute}` is deprecated and will be removed in version {version_name}."""

														if warning is not None:
																					_SCREAMING_SNAKE_CASE		:						int			  =    warning + """ """ if standard_warn else """"""
																					warnings.warn(warning + message     ,	__SCREAMING_SNAKE_CASE     ,	stacklevel=__SCREAMING_SNAKE_CASE  )

							if isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) and len(__SCREAMING_SNAKE_CASE  ) > 0:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    inspect.getouterframes(inspect.currentframe()  )[1]
														_SCREAMING_SNAKE_CASE		:						Dict			  =    call_frame.filename
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    call_frame.lineno
														_SCREAMING_SNAKE_CASE		:						str			  =    call_frame.function
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[Any]			  =    next(iter(deprecated_kwargs.items()  )  )
														raise TypeError(F"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""  )

							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														return
							elif len(__SCREAMING_SNAKE_CASE  ) == 1:
														return values[0]
							return values



"""simple docstring"""





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  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											_SCREAMING_SNAKE_CASE		:						str			  =    AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											model.to(_A)

											from datasets import load_dataset

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""nielsr/rvlcdip-demo""")

											_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""][0]["""image"""].convert("""RGB""")

											_SCREAMING_SNAKE_CASE		:						str			  =    image_processor(_A      ,    return_tensors="""pt""").to(_A)

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

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_6))
											self.assertEqual(logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
											    [-0.4_158, -0.4_092, -0.4_347]      ,    device=_A      ,    dtype=torch.float      ,    )
											self.assertTrue(torch.allclose(logits[0, :3]      ,    _A      ,    atol=1e-4))


