




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def __init__(				self					:  Any						,							A_					:  List[str]						,							A_					:  Dict=7						,							A_					:  str=3						,							A_					:  Optional[Any]=18						,							A_					:  Tuple=30						,							A_					:  Optional[int]=400						,							A_					:  Tuple=True						,							A_					:  Optional[int]=None						,							A_					:  Dict=True						,							A_					:  List[Any]=None						,							A_					:  List[Any]=True						,							A_					:  Optional[int]=[0.5, 0.5, 0.5]						,							A_					:  Any=[0.5, 0.5, 0.5]						,							)   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    size if size is not None else {'shortest_edge': 18}
       lowerCamelCase_		     =    crop_size if crop_size is not None else {'height': 18, 'width': 18}
       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    num_channels
       lowerCamelCase_		     =    image_size
       lowerCamelCase_		     =    min_resolution
       lowerCamelCase_		     =    max_resolution
       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    do_center_crop
       lowerCamelCase_		     =    crop_size
       lowerCamelCase_		     =    do_normalize
       lowerCamelCase_		     =    image_mean
       lowerCamelCase_		     =    image_std





 def 				a__ (				self					:  Tuple  )   ->	Tuple:

       """simple docstring"""






       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "do_center_crop": self.do_center_crop,
           "size": self.size,
           "crop_size": self.crop_size,
       }







@require_torch
@require_vision
class   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						LevitImageProcessor if is_vision_available() else None

 def 				a__ (				self					:  List[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    LevitImageProcessingTester(self  )

 @property
 def 				a__ (				self					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def 				a__ (				self					:  Any  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'image_mean'  )  )
       self.assertTrue(hasattr(A_						,							'image_std'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'do_center_crop'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )

 def 				a__ (				self					:  List[str]  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 18}  )
       self.assertEqual(image_processor.crop_size						,							{'height': 18, 'width': 18}  )

       lowerCamelCase_		     =    self.image_processing_class.from_dict(self.image_processor_dict						,							size=42						,							crop_size=84  )
       self.assertEqual(image_processor.size						,							{'shortest_edge': 42}  )
       self.assertEqual(image_processor.crop_size						,							{'height': 84, 'width': 84}  )

 def 				a__ (				self					:  str  )   ->	Tuple:

       """simple docstring"""






       pass

 def 				a__ (				self					:  List[str]  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       lowerCamelCase_		     =    prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							Image.Image  )

       # Test not batched input
       lowerCamelCase_		     =    image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

 def 				a__ (				self					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       lowerCamelCase_		     =    prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_						,							numpify=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							np.ndarray  )

       # Test not batched input
       lowerCamelCase_		     =    image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)





 def 				a__ (				self					:  Tuple  )   ->	Tuple:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       lowerCamelCase_		     =    prepare_image_inputs(self.image_processor_tester						,							equal_resolution=A_						,							torchify=A_  )
       for image in image_inputs:
             self.assertIsInstance(A_						,							torch.Tensor  )

       # Test not batched input
       lowerCamelCase_		     =    image_processing(image_inputs[0]						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)

       # Test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  ).pixel_values
       self.assertEqual(
           encoded_images.shape						,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['height'],
               self.image_processor_tester.crop_size['width'],
           )						,							)









from typing import TYPE_CHECKING

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


lowerCamelCase				:		str     =   {
    "configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST",
     "NezhaForNextSentencePrediction",
     "NezhaForMaskedLM",
     "NezhaForPreTraining",
     "NezhaForMultipleChoice",
     "NezhaForQuestionAnswering",
     "NezhaForSequenceClassification",
     "NezhaForTokenClassification",
     "NezhaModel",
     "NezhaPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nezha import (
      NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
      NezhaForMaskedLM,
      NezhaForMultipleChoice,
      NezhaForNextSentencePrediction,
      NezhaForPreTraining,
      NezhaForQuestionAnswering,
      NezhaForSequenceClassification,
      NezhaForTokenClassification,
      NezhaModel,
      NezhaPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase				:		str     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



