





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   UpperCamelCase_       (     unittest.TestCase    ):


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



									"""simple docstring"""



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


							def      _snake_case  (							self    :Optional[int]		)   ->   List[Any]:



									"""simple docstring"""



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




@require_torch
@require_vision
class   UpperCamelCase_       (     __a				,  unittest.TestCase    ):
							lowerCamelCase_           =					GLPNImageProcessor if is_vision_available() else None


							def      _snake_case  (							self    :Any		)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      GLPNImageProcessingTester(self		)


							@property
							def      _snake_case  (							self    :Tuple		)   ->   Dict:



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def      _snake_case  (							self    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(lowerCAmelCase_							,				"""do_resize"""		)		)
									self.assertTrue(hasattr(lowerCAmelCase_							,				"""size_divisor"""		)		)
									self.assertTrue(hasattr(lowerCAmelCase_							,				"""resample"""		)		)
									self.assertTrue(hasattr(lowerCAmelCase_							,				"""do_rescale"""		)		)


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



									"""simple docstring"""



									pass


							def      _snake_case  (							self    :Tuple		)   ->   Any:



									"""simple docstring"""



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

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Optional[Any]		)   ->   List[Any]:



									"""simple docstring"""



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

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Any		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE__			=      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 (GLPNImageProcessor doesn't support batching)
									SCREAMING_SNAKE_CASE__			=      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		)






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

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate




@dataclass(frozen=UpperCamelCase__    )
class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					field(default="image-classification"				,  metadata={"include_in_asdict_even_if_is_default": True}    )
							lowerCamelCase_           =					Features({"image": Image()}    )
							lowerCamelCase_           =					Features({"labels": ClassLabel}    )
							lowerCamelCase_           =					"image"
							lowerCamelCase_           =					"labels"


							def      _snake_case  (							self    :List[str]							,				__A    :Tuple		)   ->   Tuple:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]							,				__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__			=      self.label_schema.copy()
									SCREAMING_SNAKE_CASE__			=      features[self.label_column]
									SCREAMING_SNAKE_CASE__			=      label_schema
									return task_template


							@property
							def      _snake_case  (							self    :Dict		)   ->   Dict[str, str]:



									"""simple docstring"""



									return {
									    self.image_column: "image",
									    self.label_column: "labels",
									}