





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       :     int   =     False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor



class 					A__   (       unittest.TestCase			):





      def __init__( self       ,							A_       ,							A_=7       ,							A_=3       ,							A_=18       ,							A_=30       ,							A_=400       ,							A_=None       ,							A_=True       ,							A_=True       ,							A_=None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   size if size is not None else {"height": 20, "width": 20}
       UpperCamelCase	:					List[str]       	=   parent
       UpperCamelCase	:					Dict       	=   batch_size
       UpperCamelCase	:					Any       	=   num_channels
       UpperCamelCase	:					str       	=   image_size
       UpperCamelCase	:					Dict       	=   min_resolution
       UpperCamelCase	:					int       	=   max_resolution
       UpperCamelCase	:					int       	=   size
       UpperCamelCase	:					List[Any]       	=   do_normalize
       UpperCamelCase	:					Any       	=   do_convert_rgb
       UpperCamelCase	:					Optional[int]       	=   [512, 1024, 2048, 4096]
       UpperCamelCase	:					int       	=   patch_size if patch_size is not None else {"height": 16, "width": 16}





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
       UpperCamelCase	:					List[Any]       	=   Image.open(requests.get(A_       ,							stream=A_   ).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__   (       __a       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   PixaStructImageProcessor if is_vision_available() else None





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   PixaStructImageProcessingTester(self   )





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(A_       ,							"do_normalize"   )   )
       self.assertTrue(hasattr(A_       ,							"do_convert_rgb"   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase	:					Tuple       	=   self.image_processing_class(**self.image_processor_dict   )
       UpperCamelCase	:					Optional[int]       	=   2048

       UpperCamelCase	:					Tuple       	=   image_processor(A_       ,							return_tensors="pt"       ,							max_patches=A_   )
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()       ,							torch.tensor(0.06_06   )       ,							atol=1e-3       ,							rtol=1e-3   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       UpperCamelCase	:					List[str]       	=   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=A_   )
       for image in image_inputs:
        self.assertIsInstance(A_       ,							Image.Image   )

       # Test not batched input
       UpperCamelCase	:					Union[str, 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
        UpperCamelCase	:					Optional[Any]       	=   image_processor(
            image_inputs[0]       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(1, max_patch, expected_hidden_dim)       ,							)

        # Test batched
        UpperCamelCase	:					Any       	=   image_processor(
            A_       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PIL images
       UpperCamelCase	:					List[str]       	=   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=A_   )
       for image in image_inputs:
        self.assertIsInstance(A_       ,							Image.Image   )

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

       UpperCamelCase	:					str       	=   True

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

        UpperCamelCase	:					Union[str, Any]       	=   "Hello"

        UpperCamelCase	:					Optional[Any]       	=   image_processor(
            image_inputs[0]       ,							return_tensors="pt"       ,							max_patches=A_       ,							header_text=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(1, max_patch, expected_hidden_dim)       ,							)

        # Test batched
        UpperCamelCase	:					Dict       	=   image_processor(
            A_       ,							return_tensors="pt"       ,							max_patches=A_       ,							header_text=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       UpperCamelCase	:					str       	=   (
           (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	:					List[str]       	=   image_processor(
            image_inputs[0]       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(1, max_patch, expected_hidden_dim)       ,							)

        # Test batched
        UpperCamelCase	:					str       	=   image_processor(
            A_       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.image_processing_class(**self.image_processor_dict   )
       # create random PyTorch tensors
       UpperCamelCase	:					Union[str, Any]       	=   prepare_image_inputs(self.image_processor_tester       ,							equal_resolution=A_       ,							torchify=A_   )
       for image in image_inputs:
        self.assertIsInstance(A_       ,							torch.Tensor   )

       # Test not batched input
       UpperCamelCase	:					Union[str, 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
        UpperCamelCase	:					str       	=   image_processor(
            image_inputs[0]       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(1, max_patch, expected_hidden_dim)       ,							)

        # Test batched
        UpperCamelCase	:					Dict       	=   image_processor(
            A_       ,							return_tensors="pt"       ,							max_patches=A_   ).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__   (       __a       ,   unittest.TestCase			):
      _UpperCAmelCase	:Tuple						    =   PixaStructImageProcessor if is_vision_available() else None





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.image_processing_class(**self.image_processor_dict   )
       self.assertTrue(hasattr(A_       ,							"do_normalize"   )   )
       self.assertTrue(hasattr(A_       ,							"do_convert_rgb"   )   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




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

       # Test not batched input
       UpperCamelCase	:					Optional[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
        UpperCamelCase	:					Dict       	=   image_processor(
            image_inputs[0]       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(1, max_patch, expected_hidden_dim)       ,							)

        # Test batched
        UpperCamelCase	:					Optional[Any]       	=   image_processor(
            A_       ,							return_tensors="pt"       ,							max_patches=A_   ).flattened_patches
        self.assertEqual(
            encoded_images.shape       ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)       ,							)






'''simple docstring'''







import os





def 						snake_case_							():
    UpperCAmelCase			       =		os.path.join(os.path.dirname(_a     )    ,   '''num.txt'''     )
    with open(_a     ) as file_hand:
        return str(sum(int(_a     ) for line in file_hand     )     )[:1_0]


if __name__ == "__main__":
   print(solution())




