


"""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  :
							'''simple docstring'''







							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=0.0_2    ,   __a=3    ,   __a=[1, 3_84, 24, 24]    ,   __a=True    ,   __a=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, 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 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			DPTModel(config=__a	)
										model.to(__a	)
										model.eval()
										__lowerCAmelCase				    =			model(__a	)
										self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_labels
										__lowerCAmelCase				    =			DPTForDepthEstimation(__a	)
										model.to(__a	)
										model.eval()
										__lowerCAmelCase				    =			model(__a	)
										self.parent.assertEqual(result.predicted_depth.shape    ,   (self.batch_size, self.image_size, self.image_size)	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			self.num_labels
										__lowerCAmelCase				    =			DPTForSemanticSegmentation(__a	)
										model.to(__a	)
										model.eval()
										__lowerCAmelCase				    =			model(__a    ,   labels=__a	)
										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   _UpperCamelCase  (   lowerCAmelCase__       ,lowerCAmelCase__       ,unittest.TestCase		):
							'''simple docstring'''







							__UpperCAmelCase						:    int				      =(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
							__UpperCAmelCase						:    List[Any]				      =(
							    {
							        """depth-estimation""": DPTForDepthEstimation,
							        """feature-extraction""": DPTModel,
							        """image-segmentation""": DPTForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase						:    Any				      =False
							__UpperCAmelCase						:    Optional[int]				      =False
							__UpperCAmelCase						:    str				      =False

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			DPTModelTester(self	)
										__lowerCAmelCase				    =			ConfigTester(self    ,   config_class=__a    ,   has_text_modality=__a    ,   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(__a	)
													self.assertIsInstance(model.get_input_embeddings()    ,   (nn.Module)	)
													__lowerCAmelCase				    =			model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(__a    ,   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(__a	)
													__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]    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__a	)

							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(__a	):
																continue

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

							def 			snake_case				(     self	):
										__lowerCAmelCase    ,	__lowerCAmelCase				    =			self.model_tester.prepare_config_and_inputs_for_common()

										__lowerCAmelCase				    =			_config_zero_init(__a	)
										for model_class in self.all_model_classes:
													__lowerCAmelCase				    =			model_class(config=__a	)
													# 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(__a	)
													self.assertIsNotNone(__a	)



							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(__a	):
													__lowerCAmelCase				    =			DPTForDepthEstimation(__a	)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
			return image


@require_torch
@require_vision
@slow
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
										__lowerCAmelCase				    =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(__a	)

										__lowerCAmelCase				    =			prepare_img()
										__lowerCAmelCase				    =			image_processor(images=__a    ,   return_tensors="pt"	).to(__a	)

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

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

										__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(__a	)

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






from decimal import Decimal, getcontext
from math import ceil, factorial


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		if not isinstance(lowercase    ,       lowercase ):
				raise TypeError('Undefined for non-integers' )
		elif precision < 1:
				raise ValueError('Undefined for non-natural numbers' )

		UpperCamelCase		=     precision
		UpperCamelCase		=     ceil(precision / 14 )
		UpperCamelCase		=     426_880 * Decimal(10_005 ).sqrt()
		UpperCamelCase		=     1
		UpperCamelCase		=     13_591_409
		UpperCamelCase		=     Decimal(lowercase )
		for k in range(1    ,       lowercase ):
				UpperCamelCase		=     factorial(6 * k ) // (factorial(3 * k ) * factorial(lowercase ) ** 3)
				linear_term += 545_140_134
				exponential_term *= -262_537_412_640_768_000
				partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
		return str(constant_term / partial_sum )[:-1]


if __name__ == "__main__":
					_UpperCAmelCase					: Dict      =       50
					print(F'''The first {n} digits of pi is: {pi(n)}''')
