
import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


lowerCamelCase_						     =					logging.get_logger(__name__)




class      __A(    A__						):

  """simple docstring"""







  def __init__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       warnings.warn(
           """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
           """ use OwlViTImageProcessor instead."""	,      __A	,      )
       super().__init__(*__A	,      **__A						)







import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCamelCase_						     =					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    size if size is not None else {"""height""": 20, """width""": 20}
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    size
       UpperCamelCase__				=    do_normalize
       UpperCamelCase__				=    do_convert_rgb
       UpperCamelCase__				=    [5_12, 10_24, 20_48, 40_96]
       UpperCamelCase__				=    patch_size if patch_size is not None else {"""height""": 16, """width""": 16}







  def 	UpperCAmelCase_ (self						):
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
       UpperCamelCase__				=    Image.open(requests.get(SCREAMING_SNAKE_CASE_	,      stream=SCREAMING_SNAKE_CASE_						).raw						).convert("""RGB"""						)
       return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,				reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,				)
@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       UpperCamelCase__				=    20_48

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,      torch.tensor(0.0606						)	,      atol=1E-3	,      rtol=1E-3						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       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__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       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__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       UpperCamelCase__				=    True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=    image_processor(
                     image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches

            UpperCamelCase__				=    """Hello"""

            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       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						)

       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       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__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,				reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,				)
@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self	,      num_channels=4						)
       UpperCamelCase__				=    3







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       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__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )





