






"""simple docstring"""






import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor





class    snake_case_							(     unittest.TestCase							):



	"""simple docstring"""

	def __init__(   self	,       __a	,       __a = True	,       __a = None	,       __a = 32	,       __a = True	,       __a = 1 / 255	,       __a = True	,       __a = True	,       __a = [0.4814_5466, 0.457_8275, 0.4082_1073]	,       __a = [0.2686_2954, 0.2613_0258, 0.2757_7711]	,       __a = True	,       __a=7	,       __a=30	,       __a=400	,       __a=3	,       ):


					"""simple docstring"""

					A__							=							parent
					A__							=							do_resize
					A__							=							size if size is not None else {'''shortest_edge''': 288}
					A__							=							size_divisor
					A__							=							do_rescale
					A__							=							rescale_factor
					A__							=							do_normalize
					A__							=							do_center_crop
					A__							=							image_mean
					A__							=							image_std
					A__							=							do_pad
					A__							=							batch_size
					A__							=							num_channels
					A__							=							min_resolution
					A__							=							max_resolution


	def    _UpperCAmelCase    (   self    ):


					"""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,
					    "size_divisor": self.size_divisor,
					}






	def    _UpperCAmelCase    (   self	,       __a	,       __a=False    ):


					"""simple docstring"""

					if not batched:
									A__							=							self.size['''shortest_edge''']
									A__							=							image_inputs[0]
									if isinstance(UpperCamelCase__	,       Image.Image    ):
													A__							=							image.size
									else:
													A__							=							image.shape[1], image.shape[2]
									A__							=							size / min(UpperCamelCase__	,       UpperCamelCase__    )
									if h < w:
													A__							=							size, scale * w
									else:
													A__							=							scale * h, size

									A__							=							int((1333 / 800) * size    )
									if max(UpperCamelCase__	,       UpperCamelCase__    ) > max_size:
													A__							=							max_size / max(UpperCamelCase__	,       UpperCamelCase__    )
													A__							=							newh * scale
													A__							=							neww * scale

									A__							=							int(newh + 0.5    ), int(neww + 0.5    )
									A__							=							(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

					else:
									A__							=							[]
									for image in image_inputs:
													A__							=							self.get_expected_values([image]    )
													expected_values.append((expected_height, expected_width)    )
									A__							=							max(UpperCamelCase__	,       key=lambda __a    : item[0]    )[0]
									A__							=							max(UpperCamelCase__	,       key=lambda __a    : item[1]    )[1]

					return expected_height, expected_width





@require_torch
@require_vision
class    snake_case_							(     __SCREAMING_SNAKE_CASE     ,				unittest.TestCase							):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:				Tuple       =       BridgeTowerImageProcessor if is_vision_available() else None


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							BridgeTowerImageProcessingTester(self    )


	@property
	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					A__							=							self.image_processing_class(**self.image_processor_dict    )
					self.assertTrue(hasattr(UpperCamelCase__	,       'image_mean'    )    )
					self.assertTrue(hasattr(UpperCamelCase__	,       'image_std'    )    )
					self.assertTrue(hasattr(UpperCamelCase__	,       'do_normalize'    )    )
					self.assertTrue(hasattr(UpperCamelCase__	,       'do_resize'    )    )
					self.assertTrue(hasattr(UpperCamelCase__	,       'size'    )    )
					self.assertTrue(hasattr(UpperCamelCase__	,       'size_divisor'    )    )


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					pass


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					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__							=							image_processing(image_inputs[0]	,       return_tensors='pt'    ).pixel_values

					A__							=							self.image_processor_tester.get_expected_values(UpperCamelCase__    )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					A__							=							image_processing(UpperCamelCase__	,       return_tensors='pt'    ).pixel_values

					A__							=							self.image_processor_tester.get_expected_values(UpperCamelCase__	,       batched=UpperCamelCase__    )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )


	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					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    )

					# Test not batched input
					A__							=							image_processing(image_inputs[0]	,       return_tensors='pt'    ).pixel_values

					A__							=							self.image_processor_tester.get_expected_values(UpperCamelCase__    )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					A__							=							image_processing(UpperCamelCase__	,       return_tensors='pt'    ).pixel_values

					A__							=							self.image_processor_tester.get_expected_values(UpperCamelCase__	,       batched=UpperCamelCase__    )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )






	def    _UpperCAmelCase    (   self    ):


					"""simple docstring"""

					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__							=							image_processing(image_inputs[0]	,       return_tensors='pt'    ).pixel_values

					A__							=							self.image_processor_tester.get_expected_values(UpperCamelCase__    )
					self.assertEqual(
					    encoded_images.shape	,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,       )

					# Test batched
					A__							=							image_processing(UpperCamelCase__	,       return_tensors='pt'    ).pixel_values

					A__							=							self.image_processor_tester.get_expected_values(UpperCamelCase__	,       batched=UpperCamelCase__    )
					self.assertEqual(
					    encoded_images.shape	,       (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )	,       )




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''  ,	UpperCamelCase__  ,	)
				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )


