
'''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     _SCREAMING_SNAKE_CASE							(		_a							):





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  List[str]   =				self.config_class(**self.inputs_dict							)
								self.parent.assertTrue(hasattr(lowerCamelCase__					,      '''hidden_sizes'''							)							)
								self.parent.assertTrue(hasattr(lowerCamelCase__					,      '''num_attention_heads'''							)							)
								self.parent.assertTrue(hasattr(lowerCamelCase__					,      '''num_encoder_blocks'''							)							)



class     _SCREAMING_SNAKE_CASE							:





				def __init__(		self :		str					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Tuple=13					,      snake_case_ :		Optional[int]=64					,      snake_case_ :		Dict=3					,      snake_case_ :		Union[str, Any]=4					,      snake_case_ :		List[Any]=[2, 2, 2, 2]					,      snake_case_ :		Optional[Any]=[8, 4, 2, 1]					,      snake_case_ :		Any=[16, 32, 64, 128]					,      snake_case_ :		Optional[int]=[1, 4, 8, 16]					,      snake_case_ :		int=[1, 2, 4, 8]					,      snake_case_ :		str=True					,      snake_case_ :		int=True					,      snake_case_ :		Dict="gelu"					,      snake_case_ :		int=0.1					,      snake_case_ :		Union[str, Any]=0.1					,      snake_case_ :		Any=0.02					,      snake_case_ :		Tuple=16					,      snake_case_ :		Dict=3					,      snake_case_ :		List[str]=None					,      ):


								"""simple docstring"""

								A     :  Dict   =				parent
								A     :  Dict   =				batch_size
								A     :  List[Any]   =				image_size
								A     :  Dict   =				num_channels
								A     :  Union[str, Any]   =				num_encoder_blocks
								A     :  List[str]   =				sr_ratios
								A     :  Optional[int]   =				depths
								A     :  str   =				hidden_sizes
								A     :  List[str]   =				downsampling_rates
								A     :  Optional[Any]   =				num_attention_heads
								A     :  Optional[Any]   =				is_training
								A     :  int   =				use_labels
								A     :  Optional[Any]   =				hidden_act
								A     :  Dict   =				hidden_dropout_prob
								A     :  str   =				attention_probs_dropout_prob
								A     :  int   =				initializer_range
								A     :  Optional[int]   =				decoder_hidden_size
								A     :  Dict   =				num_labels
								A     :  Any   =				scope





				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								A     :  int   =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

								A     :  int   =				self.get_config()
								return config, pixel_values, labels





				def        _UpperCAmelCase						(		self :		Union[str, Any]							):


								"""simple docstring"""

								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        _UpperCAmelCase						(		self :		Optional[Any]					,      snake_case_ :		int					,      snake_case_ :		str					,      snake_case_ :		Tuple							):


								"""simple docstring"""

								A     :  Tuple   =				GLPNModel(config=lowerCamelCase__							)
								model.to(lowerCamelCase__							)
								model.eval()
								A     :  Any   =				model(lowerCamelCase__							)
								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        _UpperCAmelCase						(		self :		Any					,      snake_case_ :		Union[str, Any]					,      snake_case_ :		Tuple					,      snake_case_ :		int							):


								"""simple docstring"""

								A     :  Dict   =				self.num_labels
								A     :  Optional[int]   =				GLPNForDepthEstimation(lowerCamelCase__							)
								model.to(lowerCamelCase__							)
								model.eval()
								A     :  str   =				model(lowerCamelCase__							)
								self.parent.assertEqual(result.predicted_depth.shape					,      (self.batch_size, self.image_size, self.image_size)							)
								A     :  Any   =				model(lowerCamelCase__					,      labels=lowerCamelCase__							)
								self.parent.assertEqual(result.predicted_depth.shape					,      (self.batch_size, self.image_size, self.image_size)							)






				def        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				self.prepare_config_and_inputs()
								A     :  List[Any]   =				config_and_inputs
								A     :  List[str]   =				{"pixel_values": pixel_values}
								return config, inputs_dict



@require_torch
class     _SCREAMING_SNAKE_CASE							(		_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        _UpperCAmelCase						(		self :		int							):


								"""simple docstring"""

								A     :  Union[str, Any]   =				GLPNModelTester(self							)
								A     :  List[str]   =				GLPNConfigTester(self					,      config_class=lowerCamelCase__							)





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								self.config_tester.run_common_tests()





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								A     :  Tuple   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase__							)





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A     :  str   =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__							)





				@unittest.skip('''GLPN does not use inputs_embeds'''							)
				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								pass





				@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''							)
				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

								pass





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

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

								for model_class in self.all_model_classes:
												A     :  Union[str, Any]   =				model_class(lowerCamelCase__							)
												A     :  List[Any]   =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												A     :  Any   =				[*signature.parameters.keys()]

												A     :  str   =				["pixel_values"]
												self.assertListEqual(arg_names[:1]					,      lowerCamelCase__							)





				def        _UpperCAmelCase						(		self :		Any							):


								"""simple docstring"""

								A     :  Any   =				self.model_tester.prepare_config_and_inputs_for_common()
								A     :  Optional[int]   =				True

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

												A     :  Union[str, Any]   =				sum(self.model_tester.depths							)
												self.assertEqual(len(lowerCamelCase__							)					,      lowerCamelCase__							)

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

												self.assertEqual(len(lowerCamelCase__							)					,      lowerCamelCase__							)

												# verify the first attentions (first block, first layer)
												A     :  Optional[Any]   =				(self.model_tester.image_size // 4) ** 2
												A     :  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     :  int   =				(self.model_tester.image_size // 32) ** 2
												A     :  Tuple   =				(self.model_tester.image_size // (32 * 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     :  Tuple   =				len(lowerCamelCase__							)

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

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

												A     :  Union[str, Any]   =				outputs.attentions

												self.assertEqual(len(lowerCamelCase__							)					,      lowerCamelCase__							)
												# verify the first attentions (first block, first layer)
												A     :  int   =				(self.model_tester.image_size // 4) ** 2
												A     :  List[str]   =				(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        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

								def check_hidden_states_output(snake_case_ :		Tuple					,      snake_case_ :		List[str]					,      snake_case_ :		List[str]							):
												A     :  int   =				model_class(lowerCamelCase__							)
												model.to(lowerCamelCase__							)
												model.eval()

												with torch.no_grad():
																A     :  Dict   =				model(**self._prepare_for_class(lowerCamelCase__					,      lowerCamelCase__							)							)

												A     :  str   =				outputs.hidden_states

												A     :  Union[str, Any]   =				self.model_tester.num_encoder_blocks
												self.assertEqual(len(lowerCamelCase__							)					,      lowerCamelCase__							)

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

								for model_class in self.all_model_classes:
												A     :  Any   =				True
												check_hidden_states_output(lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__							)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												A     :  str   =				True

												check_hidden_states_output(lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__							)





				def        _UpperCAmelCase						(		self :		Optional[int]							):


								"""simple docstring"""

								if not self.model_tester.is_training:
												return

								A     :  Union[str, Any]   =				self.model_tester.prepare_config_and_inputs_for_common()
								A     :  List[Any]   =				True

								for model_class in self.all_model_classes:
												if model_class in get_values(lowerCamelCase__							):
																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     :  int   =				inputs_dict["pixel_values"].shape
																A     :  Optional[int]   =				torch.zeros(
																    [self.model_tester.batch_size, height, width]					,      device=lowerCamelCase__							).long()
												A     :  Optional[Any]   =				model_class(lowerCamelCase__							)
												model.to(lowerCamelCase__							)
												model.train()
												A     :  Any   =				self._prepare_for_class(lowerCamelCase__					,      lowerCamelCase__					,      return_labels=lowerCamelCase__							)
												A     :  Union[str, Any]   =				model(**lowerCamelCase__							).loss
												loss.backward()






				@slow
				def        _UpperCAmelCase						(		self :		List[Any]							):


								"""simple docstring"""

								for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A     :  List[Any]   =				GLPNModel.from_pretrained(lowerCamelCase__							)
												self.assertIsNotNone(lowerCamelCase__							)





def 							_lowerCamelCase			(					):



				'''simple docstring'''







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






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





				@slow
				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A     :  List[str]   =				GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
								A     :  List[str]   =				GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(lowerCamelCase__							)

								A     :  str   =				prepare_img()
								A     :  Tuple   =				image_processor(images=lowerCamelCase__					,      return_tensors='''pt'''							).to(lowerCamelCase__							)

								# forward pass
								with torch.no_grad():
												A     :  int   =				model(**lowerCamelCase__							)

								# verify the predicted depth
								A     :  Any   =				torch.Size([1, 480, 640]							)
								self.assertEqual(outputs.predicted_depth.shape					,      lowerCamelCase__							)

								A     :  int   =				torch.tensor(
								    [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]							).to(lowerCamelCase__							)

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

UpperCamelCase_  =	{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

UpperCamelCase_  =	{1_45: 1_45}

UpperCamelCase_  =	{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}





def 							_lowerCamelCase			(					lowerCamelCase_:  int		):



				'''simple docstring'''







				if n in CACHE_SUM_DIGIT_FACTORIALS:
								return CACHE_SUM_DIGIT_FACTORIALS[n]
				A     :  Tuple   =				sum(DIGIT_FACTORIALS[let] for let in str(lowerCamelCase_		)		)
				A     :  Dict   =				ret
				return ret





def 							_lowerCamelCase			(					lowerCamelCase_:  int				,	lowerCamelCase_:  set | None = None		):



				'''simple docstring'''







				A     :  Optional[int]   =				previous or set()
				if n in CHAIN_LENGTH_CACHE:
								return CHAIN_LENGTH_CACHE[n]
				A     :  Dict   =				sum_digit_factorials(lowerCamelCase_		)
				if next_number in previous:
								A     :  Optional[int]   =				0
								return 0
				else:
								previous.add(lowerCamelCase_		)
								A     :  str   =				1 + chain_length(lowerCamelCase_				,	lowerCamelCase_		)
								A     :  Any   =				ret
								return ret





def 							_lowerCamelCase			(					lowerCamelCase_:  int = 60				,	lowerCamelCase_:  int = 100_0000		):



				'''simple docstring'''







				return sum(1 for i in range(1				,	lowerCamelCase_		) if chain_length(lowerCamelCase_		) == num_terms		)


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