




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:
     _UpperCAmelCase	:						List[Any]			=     False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor




class    __lowerCAmelCase       (							unittest.TestCase):






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					Any       ,					_lowerCAmelCase:					List[str]=7       ,					_lowerCAmelCase:					Dict=3       ,					_lowerCAmelCase:					Optional[int]=18       ,					_lowerCAmelCase:					Dict=30       ,					_lowerCAmelCase:					Dict=4_00       ,					_lowerCAmelCase:					Optional[Any]=None       ,					_lowerCAmelCase:					Tuple=True       ,					_lowerCAmelCase:					List[Any]=True       ,					_lowerCAmelCase:					Optional[Any]=None       ,					):
         lowercase    :List[str]      	=						size if size is not None else {"height": 20, "width": 20}
         lowercase    :Optional[Any]      	=						parent
         lowercase    :Any      	=						batch_size
         lowercase    :Optional[Any]      	=						num_channels
         lowercase    :Union[str, Any]      	=						image_size
         lowercase    :Any      	=						min_resolution
         lowercase    :Tuple      	=						max_resolution
         lowercase    :Union[str, Any]      	=						size
         lowercase    :Optional[int]      	=						do_normalize
         lowercase    :str      	=						do_convert_rgb
         lowercase    :Dict      	=						[5_12, 10_24, 20_48, 40_96]
         lowercase    :List[str]      	=						patch_size if patch_size is not None else {"height": 16, "width": 16}






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






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Union[str, Any]      	=						"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
         lowercase    :Tuple      	=						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       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						PixaStructImageProcessor if is_vision_available() else None






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Tuple      	=						PixaStructImageProcessingTester(self				)






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         return self.image_processor_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Tuple      	=						self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_normalize"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_convert_rgb"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Optional[Any]      	=						self.image_processor_tester.prepare_dummy_image()

         lowercase    :Optional[int]      	=						self.image_processing_class(**self.image_processor_dict				)
         lowercase    :str      	=						20_48

         lowercase    :Union[str, Any]      	=						image_processor(_lowerCAmelCase       ,					return_tensors="pt"       ,					max_patches=_lowerCAmelCase				)
         self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,					torch.tensor(0.06_06				)       ,					atol=1e-3       ,					rtol=1e-3				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         # Initialize image_processor
         lowercase    :Tuple      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Dict      	=						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    :List[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
           lowercase    :Optional[Any]      	=						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    :Any      	=						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 					SCREAMING_SNAKE_CASE    (   self:					str				):
         # Initialize image_processor
         lowercase    :List[str]      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Tuple      	=						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    :Optional[int]      	=						(
             (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
             * self.image_processor_tester.num_channels
         ) + 2

         lowercase    :int      	=						True

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

           lowercase    :List[Any]      	=						"Hello"

           lowercase    :Optional[Any]      	=						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    :Any      	=						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 					SCREAMING_SNAKE_CASE    (   self:					int				):
         # Initialize image_processor
         lowercase    :Any      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         lowercase    :List[str]      	=						prepare_image_inputs(self.image_processor_tester       ,					equal_resolution=_lowerCAmelCase       ,					numpify=_lowerCAmelCase				)
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase       ,					np.ndarray				)

         lowercase    :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
           lowercase    :List[Any]      	=						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    :List[Any]      	=						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 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         # Initialize image_processor
         lowercase    :Any      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         lowercase    :List[Any]      	=						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    :Dict      	=						(
             (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    :List[str]      	=						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    :Any      	=						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       (							lowerCAmelCase      , unittest.TestCase):
       _a 					=						PixaStructImageProcessor if is_vision_available() else None






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         lowercase    :List[str]      	=						PixaStructImageProcessingTester(self       ,					num_channels=4				)
         lowercase    :Any      	=						3






       @property
       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         return self.image_processor_tester.prepare_image_processor_dict()






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Dict      	=						self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_normalize"				)				)
         self.assertTrue(hasattr(_lowerCAmelCase       ,					"do_convert_rgb"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict				):
         # Initialize image_processor
         lowercase    :Tuple      	=						self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         lowercase    :Optional[int]      	=						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    :Optional[Any]      	=						(
             (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    :Tuple      	=						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    :Dict      	=						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       (							lowerCAmelCase):






       @staticmethod
       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   _lowerCAmelCase:					ArgumentParser				):
         raise NotImplementedError()






       @abstractmethod
       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         raise NotImplementedError()



