"""simple docstring"""






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    :       Any					     =False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor





class  _A						(       unittest.TestCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase=7		,       __lowerCAmelCase=3		,       __lowerCAmelCase=18		,       __lowerCAmelCase=30		,       __lowerCAmelCase=400		,       __lowerCAmelCase=None		,       __lowerCAmelCase=True		,       __lowerCAmelCase=True		,       __lowerCAmelCase=None		,       ):







            """simple docstring"""



            lowercase														=	size if size is not None else {"""height""": 20, """width""": 20}
            lowercase														=	parent
            lowercase														=	batch_size
            lowercase														=	num_channels
            lowercase														=	image_size
            lowercase														=	min_resolution
            lowercase														=	max_resolution
            lowercase														=	size
            lowercase														=	do_normalize
            lowercase														=	do_convert_rgb
            lowercase														=	[512, 1024, 2048, 4096]
            lowercase														=	patch_size if patch_size is not None else {"""height""": 16, """width""": 16}

      def 				A__       (						self						):







            """simple docstring"""



            return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
            lowercase														=	Image.open(requests.get(__lowerCAmelCase		,       stream=__lowerCAmelCase						).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	):
      snake_case__			:   Any							  =	PixaStructImageProcessor if is_vision_available() else None

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	PixaStructImageProcessingTester(self						)

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self.image_processor_tester.prepare_image_processor_dict()

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_normalize"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_convert_rgb"""						)						)

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.image_processor_tester.prepare_dummy_image()

            lowercase														=	self.image_processing_class(**self.image_processor_dict						)
            lowercase														=	2048

            lowercase														=	image_processor(__lowerCAmelCase		,       return_tensors="""pt"""		,       max_patches=__lowerCAmelCase						)
            self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,       torch.tensor(0.0_6_0_6						)		,       atol=1E-3		,       rtol=1E-3						)						)

      def 				A__       (						self						):







            """simple docstring"""



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

            # Test not batched input
            lowercase														=	(
                (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
                  lowercase														=	image_processor(
                      image_inputs[0]		,       return_tensors="""pt"""		,       max_patches=__lowerCAmelCase						).flattened_patches
                  self.assertEqual(
                      encoded_images.shape		,       (1, max_patch, expected_hidden_dim)		,       )

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

      def 				A__       (						self						):







            """simple docstring"""



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

            # Test not batched input
            lowercase														=	(
                (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
                * self.image_processor_tester.num_channels
            ) + 2

            lowercase														=	True

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

                  lowercase														=	"""Hello"""

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

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

      def 				A__       (						self						):







            """simple docstring"""



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

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

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







      def 				A__       (						self						):







            """simple docstring"""



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

            # Test not batched input
            lowercase														=	(
                (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
                  lowercase														=	image_processor(
                      image_inputs[0]		,       return_tensors="""pt"""		,       max_patches=__lowerCAmelCase						).flattened_patches
                  self.assertEqual(
                      encoded_images.shape		,       (1, max_patch, expected_hidden_dim)		,       )

                  # Test batched
                  lowercase														=	image_processor(
                      __lowerCAmelCase		,       return_tensors="""pt"""		,       max_patches=__lowerCAmelCase						).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	):
      snake_case__			:   Tuple							  =	PixaStructImageProcessor if is_vision_available() else None

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	PixaStructImageProcessingTester(self		,       num_channels=4						)
            lowercase														=	3

      @property
      def 				A__       (						self						):







            """simple docstring"""



            return self.image_processor_tester.prepare_image_processor_dict()

      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.image_processing_class(**self.image_processor_dict						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_normalize"""						)						)
            self.assertTrue(hasattr(__lowerCAmelCase		,       """do_convert_rgb"""						)						)







      def 				A__       (						self						):







            """simple docstring"""



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

            # Test not batched input
            lowercase														=	(
                (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
                  lowercase														=	image_processor(
                      image_inputs[0]		,       return_tensors="""pt"""		,       max_patches=__lowerCAmelCase						).flattened_patches
                  self.assertEqual(
                      encoded_images.shape		,       (1, max_patch, expected_hidden_dim)		,       )

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




"""simple docstring"""






from __future__ import annotations

import numpy as np


def 			UpperCAmelCase__       (				lowerCAmelCase__     :list[float]		) ->    Optional[Any]:
      '''simple docstring'''


      return np.maximum(0					,		lowerCAmelCase__		)


if __name__ == "__main__":
       print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



