

"""simple docstring"""




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    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      int:
						SCREAMING_SNAKE_CASE__									= self.config_class(**self.inputs_dict    )
						self.parent.assertTrue(hasattr(__UpperCAmelCase      ,			"""hidden_sizes"""    )    )
						self.parent.assertTrue(hasattr(__UpperCAmelCase      ,			"""num_attention_heads"""    )    )
						self.parent.assertTrue(hasattr(__UpperCAmelCase      ,			"""num_encoder_blocks"""    )    )





class 				lowerCamelCase  :

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Tuple      ,			__UpperCAmelCase			:  Dict=1_3      ,			__UpperCAmelCase			:  Union[str, Any]=6_4      ,			__UpperCAmelCase			:  List[str]=3      ,			__UpperCAmelCase			:  Dict=4      ,			__UpperCAmelCase			:  Any=[2, 2, 2, 2]      ,			__UpperCAmelCase			:  Any=[8, 4, 2, 1]      ,			__UpperCAmelCase			:  Dict=[1_6, 3_2, 6_4, 1_2_8]      ,			__UpperCAmelCase			:  Optional[int]=[1, 4, 8, 1_6]      ,			__UpperCAmelCase			:  Optional[int]=[1, 2, 4, 8]      ,			__UpperCAmelCase			:  List[Any]=True      ,			__UpperCAmelCase			:  Union[str, Any]=True      ,			__UpperCAmelCase			:  Tuple="gelu"      ,			__UpperCAmelCase			:  Union[str, Any]=0.1      ,			__UpperCAmelCase			:  int=0.1      ,			__UpperCAmelCase			:  Optional[Any]=0.02      ,			__UpperCAmelCase			:  Optional[int]=1_6      ,			__UpperCAmelCase			:  List[str]=3      ,			__UpperCAmelCase			:  Optional[Any]=None      ,			)				->      Dict:
						SCREAMING_SNAKE_CASE__									= parent
						SCREAMING_SNAKE_CASE__									= batch_size
						SCREAMING_SNAKE_CASE__									= image_size
						SCREAMING_SNAKE_CASE__									= num_channels
						SCREAMING_SNAKE_CASE__									= num_encoder_blocks
						SCREAMING_SNAKE_CASE__									= sr_ratios
						SCREAMING_SNAKE_CASE__									= depths
						SCREAMING_SNAKE_CASE__									= hidden_sizes
						SCREAMING_SNAKE_CASE__									= downsampling_rates
						SCREAMING_SNAKE_CASE__									= num_attention_heads
						SCREAMING_SNAKE_CASE__									= is_training
						SCREAMING_SNAKE_CASE__									= use_labels
						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__									= decoder_hidden_size
						SCREAMING_SNAKE_CASE__									= num_labels
						SCREAMING_SNAKE_CASE__									= scope

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

						SCREAMING_SNAKE_CASE__									= self.get_config()
						return config, pixel_values, labels

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Dict:
						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    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Dict    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= GLPNModel(config=__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						SCREAMING_SNAKE_CASE__									= SCREAMING_SNAKE_CASE__									= 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    SCREAMING_SNAKE_CASE			(   self			:  Tuple      ,			__UpperCAmelCase			:  Any      ,			__UpperCAmelCase			:  Dict      ,			__UpperCAmelCase			:  List[str]    )				->      Dict:
						SCREAMING_SNAKE_CASE__									= self.num_labels
						SCREAMING_SNAKE_CASE__									= GLPNForDepthEstimation(__UpperCAmelCase    )
						model.to(__UpperCAmelCase    )
						model.eval()
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase    )
						self.parent.assertEqual(result.predicted_depth.shape      ,			(self.batch_size, self.image_size, self.image_size)    )
						SCREAMING_SNAKE_CASE__									= model(__UpperCAmelCase      ,			labels=__UpperCAmelCase    )
						self.parent.assertEqual(result.predicted_depth.shape      ,			(self.batch_size, self.image_size, self.image_size)    )





			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= config_and_inputs
						SCREAMING_SNAKE_CASE__									= {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class 				lowerCamelCase  (A__     ,A__     ,unittest.TestCase     ):
			lowerCamelCase__				:						List[str]   						=							(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
			lowerCamelCase__				:						Tuple   						=							(
			    {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
			)

			lowerCamelCase__				:						List[Any]   						=							False
			lowerCamelCase__				:						Tuple   						=							False
			lowerCamelCase__				:						Union[str, Any]   						=							False

			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]    )				->      Tuple:
						SCREAMING_SNAKE_CASE__									= GLPNModelTester(self    )
						SCREAMING_SNAKE_CASE__									= GLPNConfigTester(self      ,			config_class=__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      Dict:
						self.config_tester.run_common_tests()

			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      str:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase    )

			@unittest.skip("""GLPN does not use inputs_embeds"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      Tuple:
						pass

			@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""    )
			def    SCREAMING_SNAKE_CASE			(   self			:  Tuple    )				->      Optional[Any]:
						pass

			def    SCREAMING_SNAKE_CASE			(   self			:  List[Any]    )				->      int:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__									= ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      List[str]:
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__									= True

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= False
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )
									SCREAMING_SNAKE_CASE__									= outputs.attentions

									SCREAMING_SNAKE_CASE__									= sum(self.model_tester.depths    )
									self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )
									SCREAMING_SNAKE_CASE__									= outputs.attentions

									self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )

									# verify the first attentions (first block, first layer)
									SCREAMING_SNAKE_CASE__									= (self.model_tester.image_size // 4) ** 2
									SCREAMING_SNAKE_CASE__									= (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)
									SCREAMING_SNAKE_CASE__									= (self.model_tester.image_size // 3_2) ** 2
									SCREAMING_SNAKE_CASE__									= (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]      ,			)
									SCREAMING_SNAKE_CASE__									= len(__UpperCAmelCase    )

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= True
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()
									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )

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

									SCREAMING_SNAKE_CASE__									= outputs.attentions

									self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )
									# verify the first attentions (first block, first layer)
									SCREAMING_SNAKE_CASE__									= (self.model_tester.image_size // 4) ** 2
									SCREAMING_SNAKE_CASE__									= (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    SCREAMING_SNAKE_CASE			(   self			:  Dict    )				->      Any:
						def check_hidden_states_output(__UpperCAmelCase			:  Optional[Any]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Optional[Any]    ):
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.eval()

									with torch.no_grad():
												SCREAMING_SNAKE_CASE__									= model(**self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase    )    )

									SCREAMING_SNAKE_CASE__									= outputs.hidden_states

									SCREAMING_SNAKE_CASE__									= self.model_tester.num_encoder_blocks
									self.assertEqual(len(__UpperCAmelCase    )      ,			__UpperCAmelCase    )

									# 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,
									    ]      ,			)

						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE__									= True
									check_hidden_states_output(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE__									= True

									check_hidden_states_output(__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase    )

			def    SCREAMING_SNAKE_CASE			(   self			:  Any    )				->      List[Any]:
						if not self.model_tester.is_training:
									return

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

						for model_class in self.all_model_classes:
									if model_class in get_values(__UpperCAmelCase    ):
												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":
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= inputs_dict["""pixel_values"""].shape
												SCREAMING_SNAKE_CASE__									= torch.zeros(
												    [self.model_tester.batch_size, height, width]      ,			device=__UpperCAmelCase    ).long()
									SCREAMING_SNAKE_CASE__									= model_class(__UpperCAmelCase    )
									model.to(__UpperCAmelCase    )
									model.train()
									SCREAMING_SNAKE_CASE__									= self._prepare_for_class(__UpperCAmelCase      ,			__UpperCAmelCase      ,			return_labels=__UpperCAmelCase    )
									SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    ).loss
									loss.backward()





			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  str    )				->      int:
						for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__									= GLPNModel.from_pretrained(__UpperCAmelCase    )
									self.assertIsNotNone(__UpperCAmelCase    )

def 		A    (       ):




			'''simple docstring'''


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





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

			@slow
			def    SCREAMING_SNAKE_CASE			(   self			:  Union[str, Any]    )				->      Union[str, Any]:
						SCREAMING_SNAKE_CASE__									= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )
						SCREAMING_SNAKE_CASE__									= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= prepare_img()
						SCREAMING_SNAKE_CASE__									= image_processor(images=__UpperCAmelCase      ,			return_tensors="""pt"""    ).to(__UpperCAmelCase    )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__									= model(**__UpperCAmelCase    )

						# verify the predicted depth
						SCREAMING_SNAKE_CASE__									= torch.Size([1, 4_8_0, 6_4_0]    )
						self.assertEqual(outputs.predicted_depth.shape      ,			__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= torch.tensor(
						    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]    ).to(__UpperCAmelCase    )

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





"""simple docstring"""




from .hash_table import HashTable





class 				lowerCamelCase  (A__     ):

			def __init__(   self			:  List[str]      ,			*__UpperCAmelCase			:  Union[str, Any]      ,			**__UpperCAmelCase			:  Any    )				->      Optional[int]:
						super().__init__(*__UpperCAmelCase      ,			**__UpperCAmelCase    )





			def    SCREAMING_SNAKE_CASE			(   self			:  List[str]      ,			__UpperCAmelCase			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=None    )				->      Optional[int]:
						SCREAMING_SNAKE_CASE__									= 1
						SCREAMING_SNAKE_CASE__									= self.hash_function(key + i * i    )

						while self.values[new_key] is not None and self.values[new_key] != key:
									i += 1
									SCREAMING_SNAKE_CASE__									= (
									    self.hash_function(key + i * i    )
									    if not self.balanced_factor() >= self.lim_charge
									    else None
									)

									if new_key is None:
												break

						return new_key


