




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  _lowerCAmelCase     (  unittest.TestCase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=3      ,				__UpperCAmelCase=18      ,				__UpperCAmelCase=30      ,				__UpperCAmelCase=400      ,				__UpperCAmelCase=None      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=None      ,				):
        lowerCAmelCase__		       =     size if size is not None else {'''height''': 20, '''width''': 20}
        lowerCAmelCase__		       =     parent
        lowerCAmelCase__		       =     batch_size
        lowerCAmelCase__		       =     num_channels
        lowerCAmelCase__		       =     image_size
        lowerCAmelCase__		       =     min_resolution
        lowerCAmelCase__		       =     max_resolution
        lowerCAmelCase__		       =     size
        lowerCAmelCase__		       =     do_normalize
        lowerCAmelCase__		       =     do_convert_rgb
        lowerCAmelCase__		       =     [512, 1024, 2048, 4096]
        lowerCAmelCase__		       =     patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

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







      def 	__magic_name__(   self		):
        lowerCAmelCase__		       =     '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
        lowerCAmelCase__		       =     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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		PixaStructImageProcessor if is_vision_available() else None

      def 	__magic_name__(   self		):
        lowerCAmelCase__		       =     PixaStructImageProcessingTester(self		)

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__		       =     self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(__lowerCAmelCase      ,				'''do_normalize'''		)		)
        self.assertTrue(hasattr(__lowerCAmelCase      ,				'''do_convert_rgb'''		)		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__		       =     self.image_processor_tester.prepare_dummy_image()

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

        lowerCAmelCase__		       =     image_processor(__lowerCAmelCase      ,				return_tensors='''pt'''      ,				max_patches=__lowerCAmelCase		)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()      ,				torch.tensor(0.0606		)      ,				atol=1e-3      ,				rtol=1e-3		)		)

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

        # Test not batched input
        lowerCAmelCase__		       =     (
            (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
          lowerCAmelCase__		       =     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
          lowerCAmelCase__		       =     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 	__magic_name__(   self		):
        lowerCAmelCase__		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowerCAmelCase__		       =     prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=__lowerCAmelCase		)
        for image in image_inputs:
          self.assertIsInstance(__lowerCAmelCase      ,				Image.Image		)

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

        lowerCAmelCase__		       =     True

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

          lowerCAmelCase__		       =     '''Hello'''

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

        lowerCAmelCase__		       =     (
            (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
          lowerCAmelCase__		       =     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
          lowerCAmelCase__		       =     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 	__magic_name__(   self		):
        lowerCAmelCase__		       =     self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        lowerCAmelCase__		       =     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
        lowerCAmelCase__		       =     (
            (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
          lowerCAmelCase__		       =     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
          lowerCAmelCase__		       =     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  _lowerCAmelCase     (  _lowercase    ,			unittest.TestCase	):
      A__		     =		PixaStructImageProcessor if is_vision_available() else None

      def 	__magic_name__(   self		):
        lowerCAmelCase__		       =     PixaStructImageProcessingTester(self      ,				num_channels=4		)
        lowerCAmelCase__		       =     3

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

      def 	__magic_name__(   self		):
        lowerCAmelCase__		       =     self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(__lowerCAmelCase      ,				'''do_normalize'''		)		)
        self.assertTrue(hasattr(__lowerCAmelCase      ,				'''do_convert_rgb'''		)		)







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

        # Test not batched input
        lowerCAmelCase__		       =     (
            (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
          lowerCAmelCase__		       =     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
          lowerCAmelCase__		       =     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)      ,				)







from abc import ABC, abstractmethod
from argparse import ArgumentParser






class  _lowerCAmelCase     (  _lowercase	):

      @staticmethod
      @abstractmethod
      def 	__magic_name__(   __UpperCAmelCase		):
        raise NotImplementedError()







      @abstractmethod
      def 	__magic_name__(   self		):
        raise NotImplementedError()

