




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 OwlViTImageProcessor




class 			a__       (					unittest.TestCase      ):







     def __init__(	self  ,		A  ,		A=7  ,		A=3  ,		A=18  ,		A=30  ,		A=400  ,		A=True  ,		A=None  ,		A=True  ,		A=None  ,		A=True  ,		A=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]  ,		A=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]  ,		A=True  ,		)  ->    Any:



          '''simple docstring'''



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







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



          '''simple docstring'''



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







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







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      OwlViTImageProcessingTester(self )







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



          '''simple docstring'''



          return self.image_processor_tester.prepare_image_processor_dict()







     def 			lowerCAmelCase_				(	self )  ->    str:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(A  ,		"do_resize" ) )
          self.assertTrue(hasattr(A  ,		"size" ) )
          self.assertTrue(hasattr(A  ,		"do_center_crop" ) )
          self.assertTrue(hasattr(A  ,		"center_crop" ) )
          self.assertTrue(hasattr(A  ,		"do_normalize" ) )
          self.assertTrue(hasattr(A  ,		"image_mean" ) )
          self.assertTrue(hasattr(A  ,		"image_std" ) )
          self.assertTrue(hasattr(A  ,		"do_convert_rgb" ) )







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



          '''simple docstring'''



          a      =      self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size  ,		{"height": 18, "width": 18} )
          self.assertEqual(image_processor.crop_size  ,		{"height": 18, "width": 18} )

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







     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''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
          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
          a      =      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 			lowerCAmelCase_				(	self )  ->    Any:



          '''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
          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
          a      =      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 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''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
          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
          a      =      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 ...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

