






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      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    List[str]   ,						_SCREAMING_SNAKE_CASE      :    int=7   ,						_SCREAMING_SNAKE_CASE      :    int=3   ,						_SCREAMING_SNAKE_CASE      :    str=18   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=30   ,						_SCREAMING_SNAKE_CASE      :    List[Any]=400   ,						_SCREAMING_SNAKE_CASE      :    Tuple=True   ,						_SCREAMING_SNAKE_CASE      :    Any=None   ,						_SCREAMING_SNAKE_CASE      :    int=True   ,						_SCREAMING_SNAKE_CASE      :    Dict=None   ,						_SCREAMING_SNAKE_CASE      :    List[str]=True   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=[0.5, 0.5, 0.5]   ,						_SCREAMING_SNAKE_CASE      :    Union[str, Any]=[0.5, 0.5, 0.5]   ,						):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """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__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					LevitImageProcessor if is_vision_available() else None







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  LevitImageProcessingTester(self )







     @property
     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           return self.image_processor_tester.prepare_image_processor_dict()







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  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} )

           UpperCamelCase					       =  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   _SCREAMING_SNAKE_CASE     ( self      :    List[str] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




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

           # Test not batched input
           UpperCamelCase					       =  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
           UpperCamelCase					       =  image_processing(_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




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

           # Test not batched input
           UpperCamelCase					       =  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
           UpperCamelCase					       =  image_processing(_SCREAMING_SNAKE_CASE   ,						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   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




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

           # Test not batched input
           UpperCamelCase					       =  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
           UpperCamelCase					       =  image_processing(_SCREAMING_SNAKE_CASE   ,						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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__   :  Tuple        =							{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Optional[Any]        =							['''PLBartTokenizer''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __magic_name__   :  Tuple        =							[
           '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PLBartForCausalLM''',
           '''PLBartForConditionalGeneration''',
           '''PLBartForSequenceClassification''',
           '''PLBartModel''',
           '''PLBartPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_plbart import PLBartTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_plbart import (
                  PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PLBartForCausalLM,
                  PLBartForConditionalGeneration,
                  PLBartForSequenceClassification,
                  PLBartModel,
                  PLBartPreTrainedModel,
              )


else:
       import sys

       __magic_name__   :  Optional[int]        =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)



