




import unittest

import numpy as np

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

if is_vision_available():
 from PIL import Image

 from transformers import GLPNImageProcessor







class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def __init__(       self      :						Optional[int]		,							A_      :						str		,							A_      :						int=7		,							A_      :						Optional[Any]=3		,							A_      :						Tuple=18		,							A_      :						List[Any]=30		,							A_      :						Optional[int]=400		,							A_      :						List[str]=True		,							A_      :						str=32		,							A_      :						Union[str, Any]=True		,							)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					parent
       lowerCamelCase_	  =					batch_size
       lowerCamelCase_	  =					num_channels
       lowerCamelCase_	  =					image_size
       lowerCamelCase_	  =					min_resolution
       lowerCamelCase_	  =					max_resolution
       lowerCamelCase_	  =					do_resize
       lowerCamelCase_	  =					size_divisor
       lowerCamelCase_	  =					do_rescale





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

       """simple docstring"""






       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }







@require_torch
@require_vision
class 						A(		UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		GLPNImageProcessor if is_vision_available() else None

 def        a__				(       self      :						Optional[int]						)     ->			int:

       """simple docstring"""






       lowerCamelCase_	  =					GLPNImageProcessingTester(self						)

 @property
 def        a__				(       self      :						str						)     ->			str:

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

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

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(A_		,							'do_resize'						)						)
       self.assertTrue(hasattr(A_		,							'size_divisor'						)						)
       self.assertTrue(hasattr(A_		,							'resample'						)						)
       self.assertTrue(hasattr(A_		,							'do_rescale'						)						)

 def        a__				(       self      :						str						)     ->			Tuple:

       """simple docstring"""






       pass

 def        a__				(       self      :						List[str]						)     ->			Optional[int]:

       """simple docstring"""






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

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       lowerCamelCase_	  =					image_processing(image_inputs[0]		,							return_tensors='pt'						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

 def        a__				(       self      :						List[Any]						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowerCamelCase_	  =					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 (GLPNImageProcessor doesn't support batching)
       lowerCamelCase_	  =					image_processing(image_inputs[0]		,							return_tensors='pt'						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)





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

       """simple docstring"""






       lowerCamelCase_	  =					self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowerCamelCase_	  =					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 (GLPNImageProcessor doesn't support batching)
       lowerCamelCase_	  =					image_processing(image_inputs[0]		,							return_tensors='pt'						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCamelCase     )
class 						A(		UpperCamelCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		field(default='''language-modeling'''							,   metadata={'''include_in_asdict_even_if_is_default''': True}     )

 UpperCamelCase		     =		Features({'''text''': Value('''string'''     )}     )
 UpperCamelCase		     =		Features({}     )
 UpperCamelCase		     =		"text"

 @property
 def        a__				(       self      :						List[Any]						)     ->			Dict[str, str]:

       """simple docstring"""






       return {self.text_column: "text"}



