





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       __A     :


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=2   , UpperCAmelCase_=32   , UpperCAmelCase_=16   , UpperCAmelCase_=3   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=32   , UpperCAmelCase_=4   , UpperCAmelCase_=[0, 1, 2, 3]   , UpperCAmelCase_=4   , UpperCAmelCase_=37   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.1   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=3   , UpperCAmelCase_=[1, 384, 24, 24]   , UpperCAmelCase_=True   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =image_size
								lowerCamelCase	 =patch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =is_training
								lowerCamelCase	 =use_labels
								lowerCamelCase	 =hidden_size
								lowerCamelCase	 =num_hidden_layers
								lowerCamelCase	 =backbone_out_indices
								lowerCamelCase	 =num_attention_heads
								lowerCamelCase	 =intermediate_size
								lowerCamelCase	 =hidden_act
								lowerCamelCase	 =hidden_dropout_prob
								lowerCamelCase	 =attention_probs_dropout_prob
								lowerCamelCase	 =initializer_range
								lowerCamelCase	 =num_labels
								lowerCamelCase	 =backbone_featmap_shape
								lowerCamelCase	 =scope
								lowerCamelCase	 =is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								lowerCamelCase	 =(image_size // patch_size) ** 2
								lowerCamelCase	 =num_patches + 1


				def _snake_case       (		self							):
								lowerCamelCase	 =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

								lowerCamelCase	 =self.get_config()

								return config, pixel_values, labels


				def _snake_case       (		self							):
								lowerCamelCase	 ={
								    """global_padding""": """same""",
								    """layer_type""": """bottleneck""",
								    """depths""": [3, 4, 9],
								    """out_features""": ["""stage1""", """stage2""", """stage3"""],
								    """embedding_dynamic_padding""": True,
								    """hidden_sizes""": [96, 192, 384, 768],
								    """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=UpperCAmelCase_   , initializer_range=self.initializer_range   , is_hybrid=self.is_hybrid   , backbone_config=UpperCAmelCase_   , backbone_featmap_shape=self.backbone_featmap_shape   , )


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =DPTModel(config=UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =self.num_labels
								lowerCamelCase	 =DPTForDepthEstimation(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_							)
								self.parent.assertEqual(result.predicted_depth.shape   , (self.batch_size, self.image_size, self.image_size)							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							):
								lowerCamelCase	 =self.num_labels
								lowerCamelCase	 =DPTForSemanticSegmentation(UpperCAmelCase_							)
								model.to(UpperCAmelCase_							)
								model.eval()
								lowerCamelCase	 =model(UpperCAmelCase_   , labels=UpperCAmelCase_							)
								self.parent.assertEqual(
								    result.logits.shape   , (self.batch_size, self.num_labels, self.image_size, self.image_size)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.prepare_config_and_inputs()
								lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase	 =config_and_inputs
								lowerCamelCase	 ={"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_torch
class       __A     (						a    ,					a    ,					unittest.TestCase    ):
				__A      =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
				__A      =				(
				    {
				        """depth-estimation""": DPTForDepthEstimation,
				        """feature-extraction""": DPTModel,
				        """image-segmentation""": DPTForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				__A      =				False
				__A      =				False
				__A      =				False


				def _snake_case       (		self							):
								lowerCamelCase	 =DPTModelTester(self							)
								lowerCamelCase	 =ConfigTester(self   , config_class=UpperCAmelCase_   , has_text_modality=UpperCAmelCase_   , hidden_size=37							)


				def _snake_case       (		self							):
								self.config_tester.run_common_tests()


				@unittest.skip(reason="""DPT does not use inputs_embeds"""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

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


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCamelCase	 =["""pixel_values"""]
												self.assertListEqual(arg_names[:1]   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*UpperCAmelCase_							)


				def _snake_case       (		self							):
								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

												if model_class in get_values(UpperCAmelCase_							):
																continue

												lowerCamelCase	 =model_class(UpperCAmelCase_							)
												model.to(UpperCAmelCase_							)
												model.train()
												lowerCamelCase	 =self._prepare_for_class(UpperCAmelCase_   , UpperCAmelCase_   , return_labels=UpperCAmelCase_							)
												lowerCamelCase	 =model(**UpperCAmelCase_							).loss
												loss.backward()


				def _snake_case       (		self							):
								for model_class in self.all_model_classes:
												if model_class.__name__ == "DPTForDepthEstimation":
																continue

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

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


				def _snake_case       (		self							):
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()

								lowerCamelCase	 =_config_zero_init(UpperCAmelCase_							)
								for model_class in self.all_model_classes:
												lowerCamelCase	 =model_class(config=UpperCAmelCase_							)
												# Skip the check for the backbone
												lowerCamelCase	 =[]
												for name, module in model.named_modules():
																if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																				lowerCamelCase	 =[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 _snake_case       (		self							):
								pass


				@slow
				def _snake_case       (		self							):
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
												lowerCamelCase	 =DPTModel.from_pretrained(UpperCAmelCase_							)
												self.assertIsNotNone(UpperCAmelCase_							)


				def _snake_case       (		self							):
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								lowerCamelCase ,  lowerCamelCase	 =self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase	 ="""add"""
								with self.assertRaises(UpperCAmelCase_							):
												lowerCamelCase	 =DPTForDepthEstimation(UpperCAmelCase_							)





def      _lowercase       (						)						->  Union[str, Any]:
				lowerCamelCase	 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
				return image




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


				def _snake_case       (		self							):
								lowerCamelCase	 =DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""							)
								lowerCamelCase	 =DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""							).to(UpperCAmelCase_							)

								lowerCamelCase	 =prepare_img()
								lowerCamelCase	 =image_processor(images=UpperCAmelCase_   , return_tensors="""pt"""							).to(UpperCAmelCase_							)

								# forward pass
								with torch.no_grad():
												lowerCamelCase	 =model(**UpperCAmelCase_							)
												lowerCamelCase	 =outputs.predicted_depth

								# verify the predicted depth
								lowerCamelCase	 =torch.Size((1, 384, 384)							)
								self.assertEqual(predicted_depth.shape   , UpperCAmelCase_							)

								lowerCamelCase	 =torch.tensor(
								    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]							).to(UpperCAmelCase_							)

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100   , UpperCAmelCase_   , atol=1E-4							)							)












import math
from collections.abc import Iterator
from itertools import takewhile





def      _lowercase					(						_UpperCAmelCase				)						->  bool:

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or number % 2 == 0 or number % 3 == 0:
								# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
								return False

				# All primes number are in format of 6k +/- 1
				for i in range(5			,      int(math.sqrt(_UpperCAmelCase				) + 1				)			,      6				):
								if number % i == 0 or number % (i + 2) == 0:
												return False
				return True





def      _lowercase					(						)						->  Iterator[int]:
				lowerCamelCase	 =2
				while True:
								if is_prime(_UpperCAmelCase				):
												yield num
								num += 1





def      _lowercase					(						_UpperCAmelCase = 2_00_00_00				)						->  int:
				return sum(takewhile(lambda _UpperCAmelCase				: x < n			,      prime_generator()				)				)


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





