


import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__=2   ,UpperCAmelCase__=True   ,UpperCAmelCase__=False							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = Accelerator(step_scheduler_with_optimizer=UpperCAmelCase__   ,split_batches=UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = torch.nn.Linear(2   ,4							)
				_SCREAMING_SNAKE_CASE      = torch.optim.AdamW(model.parameters()   ,lr=1.0							)
				_SCREAMING_SNAKE_CASE      = torch.optim.lr_scheduler.OneCycleLR(UpperCAmelCase__   ,max_lr=0.0_1   ,steps_per_epoch=2   ,epochs=1							)
				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = accelerator.prepare(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							)

				# Optimizer has stepped
				scheduler.step()
				if step_scheduler_with_optimizer or (num_processes == 1):
								assert (
								    scheduler.scheduler.last_epoch == num_processes
								), f'''Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'''
				else:
								assert (
								    scheduler.scheduler.last_epoch != num_processes
								), f'''Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'''


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__=2   ,UpperCAmelCase__=True   ,UpperCAmelCase__=False							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = Accelerator(step_scheduler_with_optimizer=UpperCAmelCase__   ,split_batches=UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = torch.nn.Linear(2   ,4							)
				_SCREAMING_SNAKE_CASE      = torch.optim.AdamW(model.parameters()   ,lr=1.0							)
				_SCREAMING_SNAKE_CASE      = torch.optim.lr_scheduler.LambdaLR(UpperCAmelCase__   ,lr_lambda=lambda UpperCAmelCase__							: 1 - n / 10							)
				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = accelerator.prepare(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							)

				# Optimizer has stepped
				_SCREAMING_SNAKE_CASE      = False
				scheduler.step()
				_SCREAMING_SNAKE_CASE      = 1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), f'''Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''

				# Optimizer has not stepped
				_SCREAMING_SNAKE_CASE      = True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								_SCREAMING_SNAKE_CASE      = 1 - 2 / 10
				assert (
				    scheduler.get_last_lr()[0] == expected_lr
				), f'''Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'''


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__ = 2							):

				"""simple docstring"""






				from transformers import get_linear_schedule_with_warmup

				_SCREAMING_SNAKE_CASE      = [1, 2, 4]
				for num_steps in steps:
								_SCREAMING_SNAKE_CASE      = GradientAccumulationPlugin(num_steps=UpperCAmelCase__   ,adjust_scheduler=num_steps > 1							)
								_SCREAMING_SNAKE_CASE      = Accelerator(gradient_accumulation_plugin=UpperCAmelCase__							)
								_SCREAMING_SNAKE_CASE      = torch.nn.Linear(2   ,4							)
								_SCREAMING_SNAKE_CASE      = torch.optim.AdamW(model.parameters()   ,lr=1_0.0							)
								_SCREAMING_SNAKE_CASE      = get_linear_schedule_with_warmup(optimizer=UpperCAmelCase__   ,num_warmup_steps=0   ,num_training_steps=20							)

								_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = accelerator.prepare(UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							)

								for i in range(10 * num_steps							):
												with accelerator.accumulate(UpperCAmelCase__							):
																optimizer.step()
																scheduler.step()

												if i == (10 * num_steps - 2):
																assert (
																    scheduler.get_last_lr()[0] != 0
																), f'''Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'''
								assert (
								    scheduler.get_last_lr()[0] == 0
								), f'''Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'''
								GradientState._reset_state()



@require_cpu
class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	List[str]:
											debug_launcher(partial(A_				, num_processes=1	)				, num_processes=1	)
											debug_launcher(partial(A_				, num_processes=1				, split_batches=A_	)				, num_processes=1	)





							def 		__magic_name__			(    self	)->	Any:
											debug_launcher(partial(A_				, num_processes=1	)				, num_processes=1	)
											debug_launcher(partial(A_				, num_processes=1				, split_batches=A_	)				, num_processes=1	)





							def 		__magic_name__			(    self	)->	Optional[Any]:
											debug_launcher(partial(A_				, num_processes=1				, step_scheduler_with_optimizer=A_	)				, num_processes=1	)





							def 		__magic_name__			(    self	)->	Tuple:
											debug_launcher(partial(A_				, num_processes=1				, step_scheduler_with_optimizer=A_	)				, num_processes=1	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											AcceleratorState._reset_state(A_	)
											debug_launcher(A_	)
											debug_launcher(partial(A_				, num_processes=1				, split_batches=A_	)				, num_processes=1	)





							def 		__magic_name__			(    self	)->	Tuple:
											AcceleratorState._reset_state(A_	)
											debug_launcher(A_	)
											debug_launcher(partial(A_				, num_processes=1				, split_batches=A_	)				, num_processes=1	)





							def 		__magic_name__			(    self	)->	str:
											AcceleratorState._reset_state(A_	)
											debug_launcher(partial(A_				, step_scheduler_with_optimizer=A_	)	)





							def 		__magic_name__			(    self	)->	Dict:
											AcceleratorState._reset_state(A_	)
											debug_launcher(partial(A_				, step_scheduler_with_optimizer=A_	)	)





							@require_huggingface_suite
							def 		__magic_name__			(    self	)->	Union[str, Any]:
											AcceleratorState._reset_state(A_	)
											debug_launcher(partial(A_				, num_processes=1	)	)
											debug_launcher(A_	)




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    __lowercase  :

							"""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_=0.02				, A_=3				, A_=False				, A_=None				, )->	int:
											_SCREAMING_SNAKE_CASE      = parent
											_SCREAMING_SNAKE_CASE      = batch_size
											_SCREAMING_SNAKE_CASE      = image_size
											_SCREAMING_SNAKE_CASE      = patch_size
											_SCREAMING_SNAKE_CASE      = num_channels
											_SCREAMING_SNAKE_CASE      = is_training
											_SCREAMING_SNAKE_CASE      = use_labels
											_SCREAMING_SNAKE_CASE      = hidden_size
											_SCREAMING_SNAKE_CASE      = num_hidden_layers
											_SCREAMING_SNAKE_CASE      = backbone_out_indices
											_SCREAMING_SNAKE_CASE      = num_attention_heads
											_SCREAMING_SNAKE_CASE      = intermediate_size
											_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      = num_labels
											_SCREAMING_SNAKE_CASE      = scope
											_SCREAMING_SNAKE_CASE      = is_hybrid
											# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
											_SCREAMING_SNAKE_CASE      = (image_size // patch_size) ** 2
											_SCREAMING_SNAKE_CASE      = num_patches + 1





							def 		__magic_name__			(    self	)->	Optional[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 		__magic_name__			(    self	)->	Optional[int]:
											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				, )





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	List[Any]:
											_SCREAMING_SNAKE_CASE      = DPTModel(config=A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	Tuple:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_	)
											self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = self.num_labels
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation(A_	)
											model.to(A_	)
											model.eval()
											_SCREAMING_SNAKE_CASE      = model(A_				, labels=A_	)
											self.parent.assertEqual(
											    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)	)





							def 		__magic_name__			(    self	)->	str:
											_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    __lowercase  (       UpperCamelCase      ,     UpperCamelCase      ,     unittest.TestCase							):

							"""simple docstring"""


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

							SCREAMING_SNAKE_CASE   :					Tuple        =						False
							SCREAMING_SNAKE_CASE   :					str        =						False
							SCREAMING_SNAKE_CASE   :					Dict        =						False





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      = DPTModelTester(self	)
											_SCREAMING_SNAKE_CASE      = ConfigTester(self				, config_class=A_				, has_text_modality=A_				, hidden_size=37	)





							def 		__magic_name__			(    self	)->	str:
											self.config_tester.run_common_tests()





							@unittest.skip(reason='DPT does not use inputs_embeds'	)
							def 		__magic_name__			(    self	)->	Optional[Any]:
											pass





							def 		__magic_name__			(    self	)->	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(A_	)
															self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)	)
															_SCREAMING_SNAKE_CASE      = model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(A_				, nn.Linear	)	)





							def 		__magic_name__			(    self	)->	Any:
											_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(A_	)
															_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]				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*A_	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_depth_estimation(*A_	)





							def 		__magic_name__			(    self	)->	Any:
											_SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_semantic_segmentation(*A_	)





							def 		__magic_name__			(    self	)->	List[Any]:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

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

															if model_class in get_values(A_	):
																			continue

															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	Any:
											for model_class in self.all_model_classes:
															if model_class.__name__ == "DPTForDepthEstimation":
																			continue

															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()
															_SCREAMING_SNAKE_CASE      = False
															_SCREAMING_SNAKE_CASE      = True

															if model_class in get_values(A_	) or not model_class.supports_gradient_checkpointing:
																			continue
															_SCREAMING_SNAKE_CASE      = model_class(A_	)
															model.to(A_	)
															model.gradient_checkpointing_enable()
															model.train()
															_SCREAMING_SNAKE_CASE      = self._prepare_for_class(A_				, A_				, return_labels=A_	)
															_SCREAMING_SNAKE_CASE      = model(**A_	).loss
															loss.backward()





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.model_tester.prepare_config_and_inputs_for_common()

											_SCREAMING_SNAKE_CASE      = _config_zero_init(A_	)
											for model_class in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class(config=A_	)
															# Skip the check for the backbone
															_SCREAMING_SNAKE_CASE      = []
															for name, module in model.named_modules():
																			if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																							_SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self	)->	List[Any]:
											pass





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE      = DPTModel.from_pretrained(A_	)
															self.assertIsNotNone(A_	)







def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






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



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

							"""simple docstring"""


							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large'	)
											_SCREAMING_SNAKE_CASE      = DPTForDepthEstimation.from_pretrained('Intel/dpt-large'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)
															_SCREAMING_SNAKE_CASE      = outputs.predicted_depth

											# verify the predicted depth
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 384, 384)	)
											self.assertEqual(predicted_depth.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											# verify the logits
											_SCREAMING_SNAKE_CASE      = torch.Size((1, 150, 480, 480)	)
											self.assertEqual(outputs.logits.shape				, A_	)

											_SCREAMING_SNAKE_CASE      = torch.tensor(
											    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]	).to(A_	)

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





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'	)
											_SCREAMING_SNAKE_CASE      = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'	).to(A_	)

											_SCREAMING_SNAKE_CASE      = prepare_img()
											_SCREAMING_SNAKE_CASE      = image_processor(images=A_				, return_tensors='pt'	).to(A_	)

											# forward pass
											with torch.no_grad():
															_SCREAMING_SNAKE_CASE      = model(**A_	)

											_SCREAMING_SNAKE_CASE      = outputs.logits.detach().cpu()

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_				, target_sizes=[(500, 300)]	)
											_SCREAMING_SNAKE_CASE      = torch.Size((500, 300)	)
											self.assertEqual(segmentation[0].shape				, A_	)

											_SCREAMING_SNAKE_CASE      = image_processor.post_process_semantic_segmentation(outputs=A_	)
											_SCREAMING_SNAKE_CASE      = torch.Size((480, 480)	)
											self.assertEqual(segmentation[0].shape				, A_	)
