



import inspect
import unittest

from transformers 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
		from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import GLPNImageProcessor


class 						lowerCamelCase__			(		_a ):


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							str									=self.config_class(**self.inputs_dict      )
													self.parent.assertTrue(hasattr(_a   ,							"hidden_sizes"      )      )
													self.parent.assertTrue(hasattr(_a   ,							"num_attention_heads"      )      )
													self.parent.assertTrue(hasattr(_a   ,							"num_encoder_blocks"      )      )


class 						lowerCamelCase__			:


						def __init__(			self    :	Union[str, Any]   ,							_a    :	int   ,							_a    :	Dict=1_3   ,							_a    :	str=6_4   ,							_a    :	Any=3   ,							_a    :	List[Any]=4   ,							_a    :	Optional[Any]=[2, 2, 2, 2]   ,							_a    :	Tuple=[8, 4, 2, 1]   ,							_a    :	Any=[1_6, 3_2, 6_4, 1_2_8]   ,							_a    :	Optional[int]=[1, 4, 8, 1_6]   ,							_a    :	Optional[int]=[1, 2, 4, 8]   ,							_a    :	str=True   ,							_a    :	List[str]=True   ,							_a    :	Optional[Any]="gelu"   ,							_a    :	str=0.1   ,							_a    :	int=0.1   ,							_a    :	Dict=0.0_2   ,							_a    :	Union[str, Any]=1_6   ,							_a    :	Union[str, Any]=3   ,							_a    :	List[Any]=None   ,							):
													a__:							Optional[int]									=parent
													a__:							str									=batch_size
													a__:							Optional[Any]									=image_size
													a__:							Tuple									=num_channels
													a__:							Union[str, Any]									=num_encoder_blocks
													a__:							Union[str, Any]									=sr_ratios
													a__:							Optional[Any]									=depths
													a__:							List[Any]									=hidden_sizes
													a__:							int									=downsampling_rates
													a__:							Tuple									=num_attention_heads
													a__:							Union[str, Any]									=is_training
													a__:							int									=use_labels
													a__:							Any									=hidden_act
													a__:							str									=hidden_dropout_prob
													a__:							Union[str, Any]									=attention_probs_dropout_prob
													a__:							Any									=initializer_range
													a__:							Optional[int]									=decoder_hidden_size
													a__:							Union[str, Any]									=num_labels
													a__:							Any									=scope


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													a__:							int									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

													a__:							str									=self.get_config()
													return config, pixel_values, labels


						def 	_lowerCamelCase      (			self    :	int      ):
													return GLPNConfig(
													    image_size=self.image_size   ,							num_channels=self.num_channels   ,							num_encoder_blocks=self.num_encoder_blocks   ,							depths=self.depths   ,							hidden_sizes=self.hidden_sizes   ,							num_attention_heads=self.num_attention_heads   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							initializer_range=self.initializer_range   ,							decoder_hidden_size=self.decoder_hidden_size   ,							)


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	Optional[Any]   ,							_a    :	Optional[Any]   ,							_a    :	List[Any]      ):
													a__:							List[str]									=GLPNModel(config=_a      )
													model.to(_a      )
													model.eval()
													a__:							Optional[int]									=model(_a      )
													a__:							List[str]									=self.image_size // (self.downsampling_rates[-1] * 2)
													self.parent.assertEqual(
													    result.last_hidden_state.shape   ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)      )


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Tuple   ,							_a    :	Dict   ,							_a    :	List[str]      ):
													a__:							Dict									=self.num_labels
													a__:							int									=GLPNForDepthEstimation(_a      )
													model.to(_a      )
													model.eval()
													a__:							List[Any]									=model(_a      )
													self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size)      )
													a__:							List[Any]									=model(_a   ,							labels=_a      )
													self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size)      )







						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Tuple									=self.prepare_config_and_inputs()
													a__				,      a__				,      a__:							Tuple									=config_and_inputs
													a__:							Union[str, Any]									={"pixel_values": pixel_values}
													return config, inputs_dict


@require_torch
class 						lowerCamelCase__			(		_a					,       _a					,       unittest.TestCase ):
						_lowerCAmelCase             =			(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
						_lowerCAmelCase             =			(
						    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
						)

						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False
						_lowerCAmelCase             =			False


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													a__:							Tuple									=GLPNModelTester(self      )
													a__:							Any									=GLPNConfigTester(self   ,							config_class=_a      )


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.config_tester.run_common_tests()


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													a__:							Dict									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_a      )


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							int									=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_depth_estimation(*_a      )


						@unittest.skip("GLPN does not use inputs_embeds"      )
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													pass


						@unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"      )
						def 	_lowerCamelCase      (			self    :	List[str]      ):
													pass


						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__				,      a__:							Tuple									=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a__:							Optional[Any]									=model_class(_a      )
																				a__:							List[Any]									=inspect.signature(model.forward      )
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				a__:							Tuple									=[*signature.parameters.keys()]

																				a__:							List[Any]									=["pixel_values"]
																				self.assertListEqual(arg_names[:1]   ,							_a      )


						def 	_lowerCamelCase      (			self    :	str      ):
													a__				,      a__:							Dict									=self.model_tester.prepare_config_and_inputs_for_common()
													a__:							List[Any]									=True

													for model_class in self.all_model_classes:
																				a__:							int									=True
																				a__:							Tuple									=False
																				a__:							int									=True
																				a__:							Optional[int]									=model_class(_a      )
																				model.to(_a      )
																				model.eval()
																				with torch.no_grad():
																											a__:							List[str]									=model(**self._prepare_for_class(_a   ,							_a      )      )
																				a__:							int									=outputs.attentions

																				a__:							int									=sum(self.model_tester.depths      )
																				self.assertEqual(len(_a      )   ,							_a      )

																				# check that output_attentions also work using config
																				del inputs_dict["output_attentions"]
																				a__:							Union[str, Any]									=True
																				a__:							Any									=model_class(_a      )
																				model.to(_a      )
																				model.eval()
																				with torch.no_grad():
																											a__:							Tuple									=model(**self._prepare_for_class(_a   ,							_a      )      )
																				a__:							List[Any]									=outputs.attentions

																				self.assertEqual(len(_a      )   ,							_a      )

																				# verify the first attentions (first block, first layer)
																				a__:							Optional[int]									=(self.model_tester.image_size // 4) ** 2
																				a__:							Union[str, Any]									=(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																				self.assertListEqual(
																				    list(attentions[0].shape[-3:]      )   ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,							)

																				# verify the last attentions (last block, last layer)
																				a__:							Optional[int]									=(self.model_tester.image_size // 3_2) ** 2
																				a__:							Optional[int]									=(self.model_tester.image_size // (3_2 * self.model_tester.sr_ratios[-1])) ** 2
																				self.assertListEqual(
																				    list(attentions[-1].shape[-3:]      )   ,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,							)
																				a__:							Union[str, Any]									=len(_a      )

																				# Check attention is always last and order is fine
																				a__:							Optional[Any]									=True
																				a__:							Optional[Any]									=True
																				a__:							Dict									=model_class(_a      )
																				model.to(_a      )
																				model.eval()
																				with torch.no_grad():
																											a__:							Union[str, Any]									=model(**self._prepare_for_class(_a   ,							_a      )      )

																				self.assertEqual(out_len + 1   ,							len(_a      )      )

																				a__:							Any									=outputs.attentions

																				self.assertEqual(len(_a      )   ,							_a      )
																				# verify the first attentions (first block, first layer)
																				a__:							Dict									=(self.model_tester.image_size // 4) ** 2
																				a__:							Tuple									=(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
																				self.assertListEqual(
																				    list(self_attentions[0].shape[-3:]      )   ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,							)


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													def check_hidden_states_output(_a    :	Tuple   ,							_a    :	Any   ,							_a    :	str      ):
																				a__:							Tuple									=model_class(_a      )
																				model.to(_a      )
																				model.eval()

																				with torch.no_grad():
																											a__:							List[str]									=model(**self._prepare_for_class(_a   ,							_a      )      )

																				a__:							Any									=outputs.hidden_states

																				a__:							Dict									=self.model_tester.num_encoder_blocks
																				self.assertEqual(len(_a      )   ,							_a      )

																				# verify the first hidden states (first block)
																				self.assertListEqual(
																				    list(hidden_states[0].shape[-3:]      )   ,							[
																				        self.model_tester.hidden_sizes[0],
																				        self.model_tester.image_size // 4,
																				        self.model_tester.image_size // 4,
																				    ]   ,							)

													a__				,      a__:							Union[str, Any]									=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				a__:							Dict									=True
																				check_hidden_states_output(_a   ,							_a   ,							_a      )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				a__:							List[Any]									=True

																				check_hidden_states_output(_a   ,							_a   ,							_a      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													if not self.model_tester.is_training:
																				return

													a__				,      a__:							Dict									=self.model_tester.prepare_config_and_inputs_for_common()
													a__:							Optional[int]									=True

													for model_class in self.all_model_classes:
																				if model_class in get_values(_a      ):
																											continue
																				# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
																				# this can then be incorporated into _prepare_for_class in test_modeling_common.py
																				if model_class.__name__ == "GLPNForDepthEstimation":
																											a__				,      a__				,      a__				,      a__:							Any									=inputs_dict["pixel_values"].shape
																											a__:							Union[str, Any]									=torch.zeros(
																											    [self.model_tester.batch_size, height, width]   ,							device=_a      ).long()
																				a__:							List[Any]									=model_class(_a      )
																				model.to(_a      )
																				model.train()
																				a__:							Union[str, Any]									=self._prepare_for_class(_a   ,							_a   ,							return_labels=_a      )
																				a__:							Optional[int]									=model(**_a      ).loss
																				loss.backward()







						@slow
						def 	_lowerCamelCase      (			self    :	Tuple      ):
													for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				a__:							Tuple									=GLPNModel.from_pretrained(_a      )
																				self.assertIsNotNone(_a      )





def     __lowerCamelCase							(   ):
							a__:							List[Any]									=Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
							return image







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


						@slow
						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							List[Any]									=GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
													a__:							Union[str, Any]									=GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).to(_a      )

													a__:							int									=prepare_img()
													a__:							Optional[int]									=image_processor(images=_a   ,							return_tensors="pt"      ).to(_a      )

													# forward pass
													with torch.no_grad():
																				a__:							Tuple									=model(**_a      )

													# verify the predicted depth
													a__:							Optional[int]									=torch.Size([1, 4_8_0, 6_4_0]      )
													self.assertEqual(outputs.predicted_depth.shape   ,							_a      )

													a__:							Optional[int]									=torch.tensor(
													    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]      ).to(_a      )

													self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,							_a   ,							atol=1e-4      )      )










from __future__ import annotations





def     __lowerCamelCase							(   __magic_name__						:	int    ):
							for r in range(1  ,			__magic_name__    ):
														a__:							List[str]									=[]
														for x in range(n - 1    ):
																					a__:							int									=pow(__magic_name__  ,			__magic_name__  ,			__magic_name__    )
																					if val in li:
																												break
																					li.append(__magic_name__    )
														else:
																					return r
							return None


if __name__ == "__main__":
		__UpperCAmelCase    	=  int(input('''Enter a prime number q: '''))
		__UpperCAmelCase    	=  find_primitive(q)
		if a is None:
				print(f"""Cannot find the primitive for the value: {a!r}""")
		else:
				__UpperCAmelCase    	=  int(input('''Enter private key of A: '''))
				__UpperCAmelCase    	=  pow(a, a_private, q)
				__UpperCAmelCase    	=  int(input('''Enter private key of B: '''))
				__UpperCAmelCase    	=  pow(a, b_private, q)

				__UpperCAmelCase    	=  pow(b_public, a_private, q)
				__UpperCAmelCase    	=  pow(a_public, b_private, q)

				print('''The key value generated by A is: ''', a_secret)
				print('''The key value generated by B is: ''', b_secret)





