

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 MobileNetVaImageProcessor




class      UpperCamelCase (     unittest.TestCase   ):

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Tuple=7							,    snake_case__ :      List[Any]=3							,    snake_case__ :      str=1_8							,    snake_case__ :      Dict=3_0							,    snake_case__ :      Tuple=4_0_0							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      List[str]=None							,    snake_case__ :      int=True							,    snake_case__ :      List[Any]=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			size if size is not None else {'shortest_edge': 2_0}
     SCREAMING_SNAKE_CASE								=			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			min_resolution
     SCREAMING_SNAKE_CASE								=			max_resolution
     SCREAMING_SNAKE_CASE								=			do_resize
     SCREAMING_SNAKE_CASE								=			size
     SCREAMING_SNAKE_CASE								=			do_center_crop
     SCREAMING_SNAKE_CASE								=			crop_size




    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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




@require_torch
@require_vision
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =MobileNetVaImageProcessor if is_vision_available() else None

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			MobileNetVaImageProcessingTester(self    )

    @property
    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processing_class(**self.image_processor_dict    )
     self.assertTrue(hasattr(snake_case__							,    'do_resize'    )    )
     self.assertTrue(hasattr(snake_case__							,    'size'    )    )
     self.assertTrue(hasattr(snake_case__							,    'do_center_crop'    )    )
     self.assertTrue(hasattr(snake_case__							,    'crop_size'    )    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.image_processing_class.from_dict(self.image_processor_dict    )
     self.assertEqual(image_processor.size							,    {'shortest_edge': 2_0}    )
     self.assertEqual(image_processor.crop_size							,    {'height': 1_8, 'width': 1_8}    )

     SCREAMING_SNAKE_CASE								=			self.image_processing_class.from_dict(self.image_processor_dict							,    size=4_2							,    crop_size=8_4    )
     self.assertEqual(image_processor.size							,    {'shortest_edge': 4_2}    )
     self.assertEqual(image_processor.crop_size							,    {'height': 8_4, 'width': 8_4}    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     pass

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			image_processing(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 UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			image_processing(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 UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






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

     # Test not batched input
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			image_processing(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 ....configuration_utils import PretrainedConfig
from ....utils import logging


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

