
"""simple docstring"""




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  snake_case     (							unittest.TestCase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=7       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=18       ,       SCREAMING_SNAKE_CASE_=30       ,       SCREAMING_SNAKE_CASE_=4_00       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,       SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = size if size is not None else {'''height''': 18, '''width''': 18}
									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
									SCREAMING_SNAKE_CASE_						 = do_normalize
									SCREAMING_SNAKE_CASE_						 = image_mean
									SCREAMING_SNAKE_CASE_						 = image_std







							def _lowercase					(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,
									}







@require_torch
@require_vision
class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      DPTImageProcessor if is_vision_available() else None

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = DPTImageProcessingTester(self    )

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()

							def _lowercase					(self    ):

									"""simple docstring"""







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

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size       ,       {'''height''': 18, '''width''': 18}    )

									SCREAMING_SNAKE_CASE_						 = self.image_processing_class.from_dict(self.image_processor_dict       ,       size=42    )
									self.assertEqual(image_processor.size       ,       {'''height''': 42, '''width''': 42}    )

							def _lowercase					(self    ):

									"""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=SCREAMING_SNAKE_CASE_    )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       Image.Image    )

									# Test not batched input
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = image_processing(SCREAMING_SNAKE_CASE_       ,       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					(self    ):

									"""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=SCREAMING_SNAKE_CASE_       ,       numpify=SCREAMING_SNAKE_CASE_    )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       np.ndarray    )

									# Test not batched input
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = image_processing(SCREAMING_SNAKE_CASE_       ,       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					(self    ):

									"""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=SCREAMING_SNAKE_CASE_       ,       torchify=SCREAMING_SNAKE_CASE_    )
									for image in image_inputs:
											self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,       torch.Tensor    )

									# Test not batched input
									SCREAMING_SNAKE_CASE_						 = 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
									SCREAMING_SNAKE_CASE_						 = image_processing(SCREAMING_SNAKE_CASE_       ,       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'''],
									    )       ,       )

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index