






'''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 				snake_case__     (			snake_case__):




					def 						A     (       self       :						Optional[Any]     )  ->    List[Any]:
								UpperCAmelCase_     :   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 				snake_case__     :




					def __init__(       self       :						Dict							,				_A       :						Optional[Any]							,				_A       :						Optional[Any]=13							,				_A       :						Any=64							,				_A       :						Optional[int]=3							,				_A       :						int=4							,				_A       :						List[str]=[2, 2, 2, 2]							,				_A       :						Tuple=[8, 4, 2, 1]							,				_A       :						List[str]=[16, 32, 64, 1_28]							,				_A       :						Dict=[1, 4, 8, 16]							,				_A       :						Tuple=[1, 2, 4, 8]							,				_A       :						str=True							,				_A       :						List[Any]=True							,				_A       :						str="gelu"							,				_A       :						Optional[Any]=0.1							,				_A       :						Union[str, Any]=0.1							,				_A       :						Dict=0.02							,				_A       :						Optional[int]=16							,				_A       :						int=3							,				_A       :						List[Any]=None							,				)  ->    Optional[Any]:
								UpperCAmelCase_     :   Optional[int]       =   parent
								UpperCAmelCase_     :   Optional[Any]       =   batch_size
								UpperCAmelCase_     :   Tuple       =   image_size
								UpperCAmelCase_     :   Any       =   num_channels
								UpperCAmelCase_     :   Union[str, Any]       =   num_encoder_blocks
								UpperCAmelCase_     :   Tuple       =   sr_ratios
								UpperCAmelCase_     :   Dict       =   depths
								UpperCAmelCase_     :   Any       =   hidden_sizes
								UpperCAmelCase_     :   List[str]       =   downsampling_rates
								UpperCAmelCase_     :   Optional[Any]       =   num_attention_heads
								UpperCAmelCase_     :   List[str]       =   is_training
								UpperCAmelCase_     :   Optional[int]       =   use_labels
								UpperCAmelCase_     :   Optional[int]       =   hidden_act
								UpperCAmelCase_     :   Optional[Any]       =   hidden_dropout_prob
								UpperCAmelCase_     :   int       =   attention_probs_dropout_prob
								UpperCAmelCase_     :   List[Any]       =   initializer_range
								UpperCAmelCase_     :   Union[str, Any]       =   decoder_hidden_size
								UpperCAmelCase_     :   Optional[int]       =   num_labels
								UpperCAmelCase_     :   List[str]       =   scope




					def 						A     (       self       :						List[Any]     )  ->    Tuple:
								UpperCAmelCase_     :   str       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

								UpperCAmelCase_     :   Any       =   self.get_config()
								return config, pixel_values, labels




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





					def 						A     (       self       :						Tuple     )  ->    int:
								UpperCAmelCase_     :   str       =   self.prepare_config_and_inputs()
								UpperCAmelCase_     :   Dict       =   config_and_inputs
								UpperCAmelCase_     :   List[Any]       =   {'pixel_values': pixel_values}
								return config, inputs_dict






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

					a_       =			False
					a_       =			False
					a_       =			False




					def 						A     (       self       :						Optional[int]     )  ->    Tuple:
								UpperCAmelCase_     :   Union[str, Any]       =   GLPNModelTester(self     )
								UpperCAmelCase_     :   List[Any]       =   GLPNConfigTester(self							,				config_class=_A     )




					def 						A     (       self       :						Union[str, Any]     )  ->    Optional[int]:
								self.config_tester.run_common_tests()




					def 						A     (       self       :						str     )  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A     )




					def 						A     (       self       :						Optional[int]     )  ->    Union[str, Any]:
								UpperCAmelCase_     :   List[str]       =   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 						A     (       self       :						List[Any]     )  ->    Optional[Any]:
								pass




					@unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''     )
					def 						A     (       self       :						Union[str, Any]     )  ->    Any:
								pass




					def 						A     (       self       :						Dict     )  ->    Any:
								UpperCAmelCase_     :   Dict       =   self.model_tester.prepare_config_and_inputs_for_common()

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

											UpperCAmelCase_     :   Dict       =   ['pixel_values']
											self.assertListEqual(arg_names[:1]							,				_A     )




					def 						A     (       self       :						int     )  ->    Tuple:
								UpperCAmelCase_     :   Tuple       =   self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_     :   Optional[int]       =   True

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

											UpperCAmelCase_     :   Dict       =   sum(self.model_tester.depths     )
											self.assertEqual(len(_A     )							,				_A     )

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

											self.assertEqual(len(_A     )							,				_A     )

											# verify the first attentions (first block, first layer)
											UpperCAmelCase_     :   List[str]       =   (self.model_tester.image_size // 4) ** 2
											UpperCAmelCase_     :   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)
											UpperCAmelCase_     :   Optional[int]       =   (self.model_tester.image_size // 32) ** 2
											UpperCAmelCase_     :   Any       =   (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]							,				)
											UpperCAmelCase_     :   Any       =   len(_A     )

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

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

											UpperCAmelCase_     :   Optional[int]       =   outputs.attentions

											self.assertEqual(len(_A     )							,				_A     )
											# verify the first attentions (first block, first layer)
											UpperCAmelCase_     :   Optional[Any]       =   (self.model_tester.image_size // 4) ** 2
											UpperCAmelCase_     :   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 						A     (       self       :						str     )  ->    Union[str, Any]:
								def check_hidden_states_output(_A       :						Tuple							,				_A       :						str							,				_A       :						Tuple     ):
											UpperCAmelCase_     :   List[Any]       =   model_class(_A     )
											model.to(_A     )
											model.eval()

											with torch.no_grad():
														UpperCAmelCase_     :   Tuple       =   model(**self._prepare_for_class(_A							,				_A     )     )

											UpperCAmelCase_     :   Union[str, Any]       =   outputs.hidden_states

											UpperCAmelCase_     :   Tuple       =   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,
											    ]							,				)

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

								for model_class in self.all_model_classes:
											UpperCAmelCase_     :   Optional[Any]       =   True
											check_hidden_states_output(_A							,				_A							,				_A     )

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

											check_hidden_states_output(_A							,				_A							,				_A     )




					def 						A     (       self       :						Union[str, Any]     )  ->    Dict:
								if not self.model_tester.is_training:
											return

								UpperCAmelCase_     :   Any       =   self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_     :   Optional[Any]       =   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":
														UpperCAmelCase_     :   List[str]       =   inputs_dict['pixel_values'].shape
														UpperCAmelCase_     :   Union[str, Any]       =   torch.zeros(
														    [self.model_tester.batch_size, height, width]							,				device=_A     ).long()
											UpperCAmelCase_     :   Dict       =   model_class(_A     )
											model.to(_A     )
											model.train()
											UpperCAmelCase_     :   List[Any]       =   self._prepare_for_class(_A							,				_A							,				return_labels=_A     )
											UpperCAmelCase_     :   Optional[Any]       =   model(**_A     ).loss
											loss.backward()





					@slow
					def 						A     (       self       :						int     )  ->    Optional[int]:
								for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase_     :   Dict       =   GLPNModel.from_pretrained(_A     )
											self.assertIsNotNone(_A     )






def __UpperCAmelCase (			)  -> List[Any]:
			UpperCAmelCase_     :   List[str]       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
			return image







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




					@slow
					def 						A     (       self       :						List[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   str       =   GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
								UpperCAmelCase_     :   Optional[int]       =   GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(_A     )

								UpperCAmelCase_     :   List[Any]       =   prepare_img()
								UpperCAmelCase_     :   Optional[int]       =   image_processor(images=_A							,				return_tensors='''pt'''     ).to(_A     )

								# forward pass
								with torch.no_grad():
											UpperCAmelCase_     :   int       =   model(**_A     )

								# verify the predicted depth
								UpperCAmelCase_     :   Any       =   torch.Size([1, 4_80, 6_40]     )
								self.assertEqual(outputs.predicted_depth.shape							,				_A     )

								UpperCAmelCase_     :   str       =   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(_A     )

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














'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference


@slow
@require_torch_gpu
class 				snake_case__     (			unittest.TestCase):




					def 						A     (       self       :						Optional[Any]     )  ->    List[str]:
								UpperCAmelCase_     :   Optional[Any]       =   '''runwayml/stable-diffusion-v1-5'''
								UpperCAmelCase_     :   Optional[int]       =   TextToVideoZeroPipeline.from_pretrained(_A							,				torch_dtype=torch.floataa     ).to('''cuda'''     )
								UpperCAmelCase_     :   Tuple       =   DDIMScheduler.from_config(pipe.scheduler.config     )
								UpperCAmelCase_     :   Tuple       =   torch.Generator(device='''cuda'''     ).manual_seed(0     )

								UpperCAmelCase_     :   List[Any]       =   '''A bear is playing a guitar on Times Square'''
								UpperCAmelCase_     :   List[Any]       =   pipe(prompt=_A							,				generator=_A     ).images

								UpperCAmelCase_     :   Any       =   load_pt(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'''     )

								assert_mean_pixel_difference(_A							,				_A     )


