





'''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:
     snake_case_     =				False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor


class   _lowercase						(							unittest.TestCase				):


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=7    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=18    ,   _UpperCAmelCase=30    ,   _UpperCAmelCase=400    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   ):
       A       :       str							=						size if size is not None else {'''height''': 20, '''width''': 20}
       A       :       List[str]							=						parent
       A       :       str							=						batch_size
       A       :       int							=						num_channels
       A       :       Union[str, Any]							=						image_size
       A       :       int							=						min_resolution
       A       :       Union[str, Any]							=						max_resolution
       A       :       int							=						size
       A       :       Dict							=						do_normalize
       A       :       Optional[Any]							=						do_convert_rgb
       A       :       Optional[int]							=						[512, 1_024, 2_048, 4_096]
       A       :       List[Any]							=						patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}


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


   def 					snake_case				( self					):
       A       :       Tuple							=						'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
       A       :       Any							=						Image.open(requests.get(snake_case_    ,   stream=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   _lowercase						(							_a		,					unittest.TestCase				):
   _UpperCamelCase       		=   PixaStructImageProcessor if is_vision_available() else None


   def 					snake_case				( self					):
       A       :       Optional[Any]							=						PixaStructImageProcessingTester(self					)


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


   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(snake_case_    ,   '''do_normalize'''					)					)
       self.assertTrue(hasattr(snake_case_    ,   '''do_convert_rgb'''					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						self.image_processor_tester.prepare_dummy_image()

       A       :       Tuple							=						self.image_processing_class(**self.image_processor_dict					)
       A       :       str							=						2_048

       A       :       Dict							=						image_processor(snake_case_    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()    ,   torch.tensor(0.0606					)    ,   atol=1E-3    ,   rtol=1E-3					)					)


   def 					snake_case				( self					):
       # Initialize image_processor
       A       :       Tuple							=						self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       A       :       Optional[Any]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=snake_case_					)
       for image in image_inputs:
           self.assertIsInstance(snake_case_    ,   Image.Image					)

       # Test not batched input
       A       :       Optional[int]							=						(
           (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
           A       :       str							=						image_processor(
               image_inputs[0]    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (1, max_patch, expected_hidden_dim)    ,   )

           # Test batched
           A       :       Union[str, Any]							=						image_processor(
               snake_case_    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,   )


   def 					snake_case				( self					):
       # Initialize image_processor
       A       :       List[Any]							=						self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       A       :       List[str]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=snake_case_					)
       for image in image_inputs:
           self.assertIsInstance(snake_case_    ,   Image.Image					)

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

       A       :       Dict							=						True

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

           A       :       List[str]							=						'''Hello'''

           A       :       int							=						image_processor(
               image_inputs[0]    ,   return_tensors='''pt'''    ,   max_patches=snake_case_    ,   header_text=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (1, max_patch, expected_hidden_dim)    ,   )

           # Test batched
           A       :       Optional[int]							=						image_processor(
               snake_case_    ,   return_tensors='''pt'''    ,   max_patches=snake_case_    ,   header_text=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,   )


   def 					snake_case				( self					):
       # Initialize image_processor
       A       :       Optional[Any]							=						self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       A       :       Union[str, Any]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=snake_case_    ,   numpify=snake_case_					)
       for image in image_inputs:
           self.assertIsInstance(snake_case_    ,   np.ndarray					)

       A       :       Optional[Any]							=						(
           (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
           A       :       Tuple							=						image_processor(
               image_inputs[0]    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (1, max_patch, expected_hidden_dim)    ,   )

           # Test batched
           A       :       List[Any]							=						image_processor(
               snake_case_    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,   )


   def 					snake_case				( self					):
       # Initialize image_processor
       A       :       List[str]							=						self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       A       :       Optional[Any]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=snake_case_    ,   torchify=snake_case_					)
       for image in image_inputs:
           self.assertIsInstance(snake_case_    ,   torch.Tensor					)

       # Test not batched input
       A       :       Optional[int]							=						(
           (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
           A       :       Any							=						image_processor(
               image_inputs[0]    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (1, max_patch, expected_hidden_dim)    ,   )

           # Test batched
           A       :       Any							=						image_processor(
               snake_case_    ,   return_tensors='''pt'''    ,   max_patches=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   _lowercase						(							_a		,					unittest.TestCase				):
   _UpperCamelCase       		=   PixaStructImageProcessor if is_vision_available() else None


   def 					snake_case				( self					):
       A       :       Optional[Any]							=						PixaStructImageProcessingTester(self    ,   num_channels=4					)
       A       :       List[str]							=						3


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


   def 					snake_case				( self					):
       A       :       str							=						self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(snake_case_    ,   '''do_normalize'''					)					)
       self.assertTrue(hasattr(snake_case_    ,   '''do_convert_rgb'''					)					)


   def 					snake_case				( self					):
       # Initialize image_processor
       A       :       Any							=						self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       A       :       Optional[int]							=						prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=snake_case_					)
       for image in image_inputs:
           self.assertIsInstance(snake_case_    ,   Image.Image					)

       # Test not batched input
       A       :       int							=						(
           (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
           A       :       List[Any]							=						image_processor(
               image_inputs[0]    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (1, max_patch, expected_hidden_dim)    ,   )

           # Test batched
           A       :       Optional[int]							=						image_processor(
               snake_case_    ,   return_tensors='''pt'''    ,   max_patches=snake_case_					).flattened_patches
           self.assertEqual(
               encoded_images.shape    ,   (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,   )







'''simple docstring'''






from ..utils import DummyObject, requires_backends


class   _lowercase						(							metaclass=a				):
   _UpperCamelCase       		=   ["""onnx"""]


   def __init__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(self    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)


   @classmethod
   def 					snake_case				( cls    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       requires_backends(cls    ,   ['''onnx''']					)
