

'''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):






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"     )
				UpperCAmelCase_     :      Tuple						=  AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip"     )
				model.to(_snake_case     )

				from datasets import load_dataset

				UpperCAmelCase_     :      Tuple						=  load_dataset("nielsr/rvlcdip-demo"     )

				UpperCAmelCase_     :      Dict						=  dataset["train"][0]["image"].convert("RGB"     )

				UpperCAmelCase_     :      Optional[Any]						=  image_processor(_snake_case			,return_tensors="pt"     ).to(_snake_case     )

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

				UpperCAmelCase_     :      List[Any]						=  torch.Size((1, 16)     )
				self.assertEqual(logits.shape			,_snake_case     )

				UpperCAmelCase_     :      Dict						=  torch.tensor(
				    [-0.4158, -0.4092, -0.4347]			,device=_snake_case			,dtype=torch.float			,)
				self.assertTrue(torch.allclose(logits[0, :3]			,_snake_case			,atol=1E-4     )     )










from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase__					:  List[Any]      							=				TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase__					:  Union[str, Any]      							=				TaTokenizerFast

lowerCamelCase__					:  Dict      							=				{'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				[
       'MT5EncoderModel',
       'MT5ForConditionalGeneration',
       'MT5ForQuestionAnswering',
       'MT5Model',
       'MT5PreTrainedModel',
       'MT5Stack',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__					:  Tuple      							=				['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   lowerCamelCase__					:  int      							=				_LazyModule(
       __name__,
       globals()['__file__'],
       _import_structure,
       extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
       module_spec=__spec__,
   )