






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				):
	@slow
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Any       						= AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""			)
							__magic_name__      :       str       						= AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""			)
							model.to(lowerCAmelCase__			)

							from datasets import load_dataset

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

							__magic_name__      :       int       						= dataset["""train"""][0]["""image"""].convert("""RGB"""			)

							__magic_name__      :       Optional[int]       						= image_processor(lowerCAmelCase__      ,       return_tensors="""pt"""			).to(lowerCAmelCase__			)

							# forward pass
							with torch.no_grad():
													__magic_name__      :       str       						= model(**lowerCAmelCase__			)
													__magic_name__      :       Optional[Any]       						= outputs.logits

							__magic_name__      :       List[str]       						= torch.Size((1, 16)			)
							self.assertEqual(logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       str       						= torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]      ,       device=lowerCAmelCase__      ,       dtype=torch.float      ,       )
							self.assertTrue(torch.allclose(logits[0, :3]      ,       lowerCAmelCase__      ,       atol=1e-4			)			)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__:					Tuple							=     {
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					Dict							=     [
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__:					int							=     [
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		__magic_name__:					Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
