




"""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:
       a					=			False

if is_vision_available():
       from PIL import Image

       from transformers import PixaStructImageProcessor





class 				lowercase_       (     unittest.TestCase							):



 '''simple docstring'''

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

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



 def 			lowerCAmelCase_							( self       :       Any		):
       _A					   =   'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
       _A					   =   Image.open(requests.get(UpperCAmelCase__    ,							stream=UpperCAmelCase__		).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_       (     __lowerCAmelCase			,	unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     Union[str, Any]        =	PixaStructImageProcessor if is_vision_available() else None

 def 			lowerCAmelCase_							( self       :       Union[str, Any]		):
       _A					   =   PixaStructImageProcessingTester(self		)

 @property
 def 			lowerCAmelCase_							( self       :       Dict		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def 			lowerCAmelCase_							( self       :       int		):
       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCAmelCase__    ,							'do_normalize'		)		)
       self.assertTrue(hasattr(UpperCAmelCase__    ,							'do_convert_rgb'		)		)

 def 			lowerCAmelCase_							( self       :       int		):
       _A					   =   self.image_processor_tester.prepare_dummy_image()

       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       _A					   =   2_048

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

 def 			lowerCAmelCase_							( self       :       Dict		):
       # Initialize image_processor
       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _A					   =   prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=UpperCAmelCase__		)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase__    ,							Image.Image		)

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

             # Test batched
             _A					   =   image_processor(
                 UpperCAmelCase__    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__		).flattened_patches
             self.assertEqual(
                 encoded_images.shape    ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,							)

 def 			lowerCAmelCase_							( self       :       Tuple		):
       # Initialize image_processor
       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _A					   =   prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=UpperCAmelCase__		)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase__    ,							Image.Image		)

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

       _A					   =   True

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

             _A					   =   'Hello'

             _A					   =   image_processor(
                 image_inputs[0]    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__    ,							header_text=UpperCAmelCase__		).flattened_patches
             self.assertEqual(
                 encoded_images.shape    ,							(1, max_patch, expected_hidden_dim)    ,							)

             # Test batched
             _A					   =   image_processor(
                 UpperCAmelCase__    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__    ,							header_text=UpperCAmelCase__		).flattened_patches
             self.assertEqual(
                 encoded_images.shape    ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,							)

 def 			lowerCAmelCase_							( self       :       Any		):
       # Initialize image_processor
       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       _A					   =   prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=UpperCAmelCase__    ,							numpify=UpperCAmelCase__		)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase__    ,							np.ndarray		)

       _A					   =   (
           (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					   =   image_processor(
                 image_inputs[0]    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__		).flattened_patches
             self.assertEqual(
                 encoded_images.shape    ,							(1, max_patch, expected_hidden_dim)    ,							)

             # Test batched
             _A					   =   image_processor(
                 UpperCAmelCase__    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__		).flattened_patches
             self.assertEqual(
                 encoded_images.shape    ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,							)



 def 			lowerCAmelCase_							( self       :       Dict		):
       # Initialize image_processor
       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       _A					   =   prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=UpperCAmelCase__    ,							torchify=UpperCAmelCase__		)
       for image in image_inputs:
             self.assertIsInstance(UpperCAmelCase__    ,							torch.Tensor		)

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

             # Test batched
             _A					   =   image_processor(
                 UpperCAmelCase__    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__		).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_       (     __lowerCAmelCase			,	unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase    :     List[Any]        =	PixaStructImageProcessor if is_vision_available() else None

 def 			lowerCAmelCase_							( self       :       str		):
       _A					   =   PixaStructImageProcessingTester(self    ,							num_channels=4		)
       _A					   =   3

 @property
 def 			lowerCAmelCase_							( self       :       Optional[int]		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def 			lowerCAmelCase_							( self       :       str		):
       _A					   =   self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(UpperCAmelCase__    ,							'do_normalize'		)		)
       self.assertTrue(hasattr(UpperCAmelCase__    ,							'do_convert_rgb'		)		)



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

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

             # Test batched
             _A					   =   image_processor(
                 UpperCAmelCase__    ,							return_tensors='pt'    ,							max_patches=UpperCAmelCase__		).flattened_patches
             self.assertEqual(
                 encoded_images.shape    ,							(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)    ,							)




"""simple docstring"""





from collections.abc import Sequence




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   return sum(c * (x**i) for i, c in enumerate(UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   __lowercase								=						0.0
   for coeff in reversed(UpperCamelCase_):
      __lowercase								=						result * x + coeff
   return result


if __name__ == "__main__":
   _a       =							(0.0, 0.0, 5.0, 9.3, 7.0)
   _a       =							10.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))




