


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 PoolFormerImageProcessor






class    UpperCAmelCase__	( unittest.TestCase		):






    def __init__(      self ,       A_ ,       A_=7 ,       A_=3 ,       A_=30 ,       A_=400 ,       A_=True ,       A_=None ,       A_=0.9 ,       A_=None ,       A_=True ,       A_=[0.5, 0.5, 0.5] ,       A_=[0.5, 0.5, 0.5] ,       )  ->	Optional[Any]:
         __UpperCamelCase   							=size if size is not None else {'''shortest_edge''': 30}
         __UpperCamelCase   							=crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         __UpperCamelCase   							=parent
         __UpperCamelCase   							=batch_size
         __UpperCamelCase   							=num_channels
         __UpperCamelCase   							=min_resolution
         __UpperCamelCase   							=max_resolution
         __UpperCamelCase   							=do_resize_and_center_crop
         __UpperCamelCase   							=size
         __UpperCamelCase   							=crop_pct
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean
         __UpperCamelCase   							=image_std






    def _a (      self   )  ->	Tuple:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }






@require_torch
@require_vision
class    UpperCAmelCase__	( __lowerCamelCase       ,  unittest.TestCase		):
    UpperCAmelCase__						:		Any              =  PoolFormerImageProcessor if is_vision_available() else None






    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=PoolFormerImageProcessingTester(self   )






    @property
    def _a (      self   )  ->	str:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.image_processing_class(**self.image_processor_dict   )
         self.assertTrue(hasattr(__lowercase ,       'do_resize_and_center_crop'   )   )
         self.assertTrue(hasattr(__lowercase ,       'size'   )   )
         self.assertTrue(hasattr(__lowercase ,       'crop_pct'   )   )
         self.assertTrue(hasattr(__lowercase ,       'do_normalize'   )   )
         self.assertTrue(hasattr(__lowercase ,       'image_mean'   )   )
         self.assertTrue(hasattr(__lowercase ,       'image_std'   )   )






    def _a (      self   )  ->	List[Any]:
         __UpperCamelCase   							=self.image_processing_class.from_dict(self.image_processor_dict   )
         self.assertEqual(image_processor.size ,       {'shortest_edge': 30}   )
         self.assertEqual(image_processor.crop_size ,       {'height': 30, 'width': 30}   )

         __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 _a (      self   )  ->	Optional[Any]:
         pass






    def _a (      self   )  ->	Any:
         # Initialize image_processing
         __UpperCamelCase   							=self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         __UpperCamelCase   							=prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__lowercase   )
         for image in image_inputs:
              self.assertIsInstance(__lowercase ,       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(__lowercase ,       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   )  ->	Any:
         # Initialize image_processing
         __UpperCamelCase   							=self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         __UpperCamelCase   							=prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__lowercase ,       numpify=__lowercase   )
         for image in image_inputs:
              self.assertIsInstance(__lowercase ,       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(__lowercase ,       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   )  ->	Union[str, Any]:
         # Initialize image_processing
         __UpperCamelCase   							=self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         __UpperCamelCase   							=prepare_image_inputs(self.image_processor_tester ,       equal_resolution=__lowercase ,       torchify=__lowercase   )
         for image in image_inputs:
              self.assertIsInstance(__lowercase ,       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(__lowercase ,       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_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_A			=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						['ViTFeatureExtractor']
   _A			=						['ViTImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTForImageClassification',
       'ViTForMaskedImageModeling',
       'ViTModel',
       'ViTPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'TFViTForImageClassification',
       'TFViTModel',
       'TFViTPreTrainedModel',
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A			=						[
       'FlaxViTForImageClassification',
       'FlaxViTModel',
       'FlaxViTPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _A			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





