




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 ViltImageProcessor




class 			a__       (					unittest.TestCase      ):







     def __init__(	self  ,		A  ,		A=7  ,		A=3  ,		A=18  ,		A=30  ,		A=400  ,		A=True  ,		A=None  ,		A=2  ,		A=True  ,		A=[0.5, 0.5, 0.5]  ,		A=[0.5, 0.5, 0.5]  ,		)  ->    Tuple:



          '''simple docstring'''



          a      =      size if size is not None else {"shortest_edge": 30}
          a      =      parent
          a      =      batch_size
          a      =      num_channels
          a      =      image_size
          a      =      min_resolution
          a      =      max_resolution
          a      =      do_resize
          a      =      size
          a      =      size_divisor
          a      =      do_normalize
          a      =      image_mean
          a      =      image_std







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



          '''simple docstring'''



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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A=False )  ->    Optional[int]:



          '''simple docstring'''



          if not batched:
               a      =      self.size["shortest_edge"]
               a      =      image_inputs[0]
               if isinstance(A  ,		Image.Image ):
                    a  ,      a      =      image.size
               else:
                    a  ,      a      =      image.shape[1], image.shape[2]
               a      =      size / min(A  ,		A )
               if h < w:
                    a  ,      a      =      size, scale * w
               else:
                    a  ,      a      =      scale * h, size

               a      =      int((1333 / 800) * size )
               if max(A  ,		A ) > max_size:
                    a      =      max_size / max(A  ,		A )
                    a      =      newh * scale
                    a      =      neww * scale

               a  ,      a      =      int(newh + 0.5 ), int(neww + 0.5 )
               a  ,      a      =      (
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

          else:
               a      =      []
               for image in image_inputs:
                    a  ,      a      =      self.get_expected_values([image] )
                    expected_values.append((expected_height, expected_width) )
               a      =      max(A  ,		key=lambda A : item[0] )[0]
               a      =      max(A  ,		key=lambda A : item[1] )[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Tuple							=	ViltImageProcessor if is_vision_available() else None







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



          '''simple docstring'''



          a      =      ViltImageProcessingTester(self )







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



          '''simple docstring'''



          return self.image_processor_tester.prepare_image_processor_dict()







     def 			lowerCAmelCase_				(	self )  ->    Union[str, Any]:



          '''simple docstring'''



          a      =      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  ,		"size" ) )
          self.assertTrue(hasattr(A  ,		"size_divisor" ) )







     def 			lowerCAmelCase_				(	self )  ->    int:



          '''simple docstring'''



          a      =      self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size  ,		{"shortest_edge": 30} )

          a      =      self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42 )
          self.assertEqual(image_processor.size  ,		{"shortest_edge": 42} )







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          pass







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



          '''simple docstring'''



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

          # Test not batched input
          a      =      image_processing(image_inputs[0]  ,		return_tensors="pt" ).pixel_values

          a  ,      a      =      self.image_processor_tester.get_expected_values(A )
          self.assertEqual(
              encoded_images.shape  ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,		)

          # Test batched
          a      =      image_processing(A  ,		return_tensors="pt" ).pixel_values

          a  ,      a      =      self.image_processor_tester.get_expected_values(A  ,		batched=A )
          self.assertEqual(
              encoded_images.shape  ,		(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )  ,		)







     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          a      =      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
          a      =      image_processing(image_inputs[0]  ,		return_tensors="pt" ).pixel_values

          a  ,      a      =      self.image_processor_tester.get_expected_values(A )
          self.assertEqual(
              encoded_images.shape  ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,		)

          # Test batched
          a      =      image_processing(A  ,		return_tensors="pt" ).pixel_values

          a  ,      a      =      self.image_processor_tester.get_expected_values(A  ,		batched=A )
          self.assertEqual(
              encoded_images.shape  ,		(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )  ,		)







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



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          a      =      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
          a      =      image_processing(image_inputs[0]  ,		return_tensors="pt" ).pixel_values

          a  ,      a      =      self.image_processor_tester.get_expected_values(A )
          self.assertEqual(
              encoded_images.shape  ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,		)

          # Test batched
          a      =      image_processing(A  ,		return_tensors="pt" ).pixel_values

          a  ,      a      =      self.image_processor_tester.get_expected_values(A  ,		batched=A )
          self.assertEqual(
              encoded_images.shape  ,		(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )  ,		)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

