

import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor







class   __lowercase      (unittest.TestCase ):
 def __init__(							self      ,							A_      ,							A_=7      ,							A_=3      ,							A_=18      ,							A_=30      ,							A_=400      ,							A_=True      ,							A_=None      ,							A_=True      ,							A_=[0.5, 0.5, 0.5]      ,							A_=[0.5, 0.5, 0.5]      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					size if size is not None else {'''height''': 18, '''width''': 18}
       __lowerCAmelCase					:						Dict					=					parent
       __lowerCAmelCase					:						int					=					batch_size
       __lowerCAmelCase					:						Union[str, Any]					=					num_channels
       __lowerCAmelCase					:						Tuple					=					image_size
       __lowerCAmelCase					:						List[str]					=					min_resolution
       __lowerCAmelCase					:						Dict					=					max_resolution
       __lowerCAmelCase					:						Any					=					do_resize
       __lowerCAmelCase					:						Optional[Any]					=					size
       __lowerCAmelCase					:						str					=					do_normalize
       __lowerCAmelCase					:						Optional[int]					=					image_mean
       __lowerCAmelCase					:						Optional[int]					=					image_std





 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }







@require_torch
@require_vision
class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  DPTImageProcessor if is_vision_available() else None
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					DPTImageProcessingTester(self					)
 @property
 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(A_      ,							'''image_mean'''					)					)
       self.assertTrue(hasattr(A_      ,							'''image_std'''					)					)
       self.assertTrue(hasattr(A_      ,							'''do_normalize'''					)					)
       self.assertTrue(hasattr(A_      ,							'''do_resize'''					)					)
       self.assertTrue(hasattr(A_      ,							'''size'''					)					)
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size      ,							{'''height''': 18, '''width''': 18}					)

       __lowerCAmelCase					:						Union[str, Any]					=					self.image_processing_class.from_dict(self.image_processor_dict      ,							size=42					)
       self.assertEqual(image_processor.size      ,							{'''height''': 42, '''width''': 42}					)
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





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

       # Test not batched input
       __lowerCAmelCase					:						List[str]					=					image_processing(image_inputs[0]      ,							return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape      ,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,							)

       # Test batched
       __lowerCAmelCase					:						Any					=					image_processing(A_      ,							return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape      ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,							)
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





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

       # Test not batched input
       __lowerCAmelCase					:						Any					=					image_processing(image_inputs[0]      ,							return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape      ,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,							)

       # Test batched
       __lowerCAmelCase					:						Optional[Any]					=					image_processing(A_      ,							return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape      ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,							)





 def 	UpperCamelCase__  (							self					)					->Union[str, Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					self.image_processing_class(**self.image_processor_dict					)
       # create random PyTorch tensors
       __lowerCAmelCase					:						Optional[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
       __lowerCAmelCase					:						Any					=					image_processing(image_inputs[0]      ,							return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape      ,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,							)

       # Test batched
       __lowerCAmelCase					:						Optional[Any]					=					image_processing(A_      ,							return_tensors='''pt'''					).pixel_values
       self.assertEqual(
           encoded_images.shape      ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size['''height'''],
               self.image_processor_tester.size['''width'''],
           )      ,							)





def 		_lowercase (     lowercase__						):
      if not all(x.isalpha() for x in string						):
            raise ValueError('''String must only contain alphabetic characters.'''						)

      __lowerCAmelCase					:						int					=					sorted(string.lower()						)
      return len(lowercase__						) == len(set(lowercase__						)						)


if __name__ == "__main__":
 _UpperCamelCase 			=		input("Enter a string ").strip()

 _UpperCamelCase 			=		is_isogram(input_str)
 print(F"{input_str} is {'an' if isogram else 'not an'} isogram.")


