
"""simple docstring"""





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
				from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import DPTImageProcessor






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=2							,						__a=32							,						__a=16							,						__a=3							,						__a=True							,						__a=True							,						__a=32							,						__a=4							,						__a=[0, 1, 2, 3]							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=0.02							,						__a=3							,						__a=[1, 3_84, 24, 24]							,						__a=True							,						__a=None							,						)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    image_size
							_UpperCamelCase									=    patch_size
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    use_labels
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    backbone_out_indices
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob
							_UpperCamelCase									=    initializer_range
							_UpperCamelCase									=    num_labels
							_UpperCamelCase									=    backbone_featmap_shape
							_UpperCamelCase									=    scope
							_UpperCamelCase									=    is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							_UpperCamelCase									=    (image_size // patch_size) ** 2
							_UpperCamelCase									=    num_patches + 1



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.image_size, self.image_size]							,						self.num_labels)

							_UpperCamelCase									=    self.get_config()

							return config, pixel_values, labels



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''global_padding''': '''same''',
							    '''layer_type''': '''bottleneck''',
							    '''depths''': [3, 4, 9],
							    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
							    '''embedding_dynamic_padding''': True,
							    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
							    '''num_groups''': 2,
							}

							return DPTConfig(
							    image_size=self.image_size							,						patch_size=self.patch_size							,						num_channels=self.num_channels							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						backbone_out_indices=self.backbone_out_indices							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						is_decoder=__a							,						initializer_range=self.initializer_range							,						is_hybrid=self.is_hybrid							,						backbone_config=__a							,						backbone_featmap_shape=self.backbone_featmap_shape							,						)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    DPTModel(config=__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    DPTForDepthEstimation(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.predicted_depth.shape							,						(self.batch_size, self.image_size, self.image_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    DPTForSemanticSegmentation(__a)
							model.to(__a)
							model.eval()
							_UpperCamelCase									=    model(__a							,						labels=__a)
							self.parent.assertEqual(
							    result.logits.shape							,						(self.batch_size, self.num_labels, self.image_size, self.image_size))




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






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    config_and_inputs
							_UpperCamelCase									=    {'''pixel_values''': pixel_values}
							return config, inputs_dict






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		lowercase__         = (
		    {
		        'depth-estimation': DPTForDepthEstimation,
		        'feature-extraction': DPTModel,
		        'image-segmentation': DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    DPTModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a							,						hidden_size=37)



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






							'''simple docstring'''
							self.config_tester.run_common_tests()



		@unittest.skip(reason='''DPT does not use inputs_embeds''')
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												self.assertIsInstance(model.get_input_embeddings()							,						(nn.Module))
												_UpperCamelCase									=    model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__a							,						nn.Linear))



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)
												_UpperCamelCase									=    inspect.signature(model.forward)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_UpperCamelCase									=    [*signature.parameters.keys()]

												_UpperCamelCase									=    ['''pixel_values''']
												self.assertListEqual(arg_names[:1]							,						__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



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






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																	continue

												_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
												_UpperCamelCase									=    True

												if model_class in get_values(__a):
																	continue

												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.train()
												_UpperCamelCase									=    self._prepare_for_class(__a							,						__a							,						return_labels=__a)
												_UpperCamelCase									=    model(**__a).loss
												loss.backward()



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																	continue

												_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
												_UpperCamelCase									=    False
												_UpperCamelCase									=    True

												if model_class in get_values(__a) or not model_class.supports_gradient_checkpointing:
																	continue
												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.gradient_checkpointing_enable()
												model.train()
												_UpperCamelCase									=    self._prepare_for_class(__a							,						__a							,						return_labels=__a)
												_UpperCamelCase									=    model(**__a).loss
												loss.backward()



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






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

							_UpperCamelCase									=    _config_zero_init(__a)
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(config=__a)
												# Skip the check for the backbone
												_UpperCamelCase									=    []
												for name, module in model.named_modules():
																	if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																						_UpperCamelCase									=    [F'''{name}.{key}''' for key in module.state_dict().keys()]
																						break

												for name, param in model.named_parameters():
																	if param.requires_grad:
																						if name in backbone_params:
																											continue
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item()							,						[0.0, 1.0]							,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,						)



		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							pass



		@slow
		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												_UpperCamelCase									=    DPTModel.from_pretrained(__a)
												self.assertIsNotNone(__a)




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase									=    '''add'''
							with self.assertRaises(__a):
												_UpperCamelCase									=    DPTForDepthEstimation(__a)





def  lowerCamelCase__						(     )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image






@require_torch
@require_vision
@slow
class        _UpperCAmelCase(       unittest.TestCase      ):



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






							'''simple docstring'''
							_UpperCamelCase									=    DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas''')
							_UpperCamelCase									=    DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas''').to(__a)

							_UpperCamelCase									=    prepare_img()
							_UpperCamelCase									=    image_processor(images=__a							,						return_tensors='''pt''').to(__a)

							# forward pass
							with torch.no_grad():
												_UpperCamelCase									=    model(**__a)
												_UpperCamelCase									=    outputs.predicted_depth

							# verify the predicted depth
							_UpperCamelCase									=    torch.Size((1, 3_84, 3_84))
							self.assertEqual(predicted_depth.shape							,						__a)

							_UpperCamelCase									=    torch.tensor(
							    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(__a)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00							,						__a							,						atol=1e-4))








"""simple docstring"""





from __future__ import annotations

from functools import lru_cache
from math import ceil

_a								=						100

_a								=						set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a								=						42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
				if prime not in primes:
								continue
				primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00      )
def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					if number_to_partition < 0:
										return set()
					elif number_to_partition == 0:
										return {1}

					_UpperCamelCase									=    set()
					_UpperCamelCase									=    42
					_UpperCamelCase									=    42

					for prime in primes:
										if prime > number_to_partition:
															continue
										for sub in partition(number_to_partition - prime      ):
															ret.add(sub * prime      )

					return ret



def  lowerCamelCase__						(     __snake_case = 50_00      )				->						int | None:


					"""simple docstring"""





					for number_to_partition in range(1,	__snake_case      ):
										if len(partition(__snake_case      )      ) > number_unique_partitions:
															return number_to_partition
					return None


if __name__ == "__main__":
				print(F"""{solution() = }""")






