





'''simple docstring'''

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  a_					:

    '''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       snake_case_(		self	)		->       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       snake_case_(		self	)		->       Optional[Any]:
        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       snake_case_(		self			,		A			,		A			,		A	)		->       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       snake_case_(		self			,		A			,		A			,		A	)		->       Optional[Any]:
        _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       snake_case_(		self			,		A			,		A			,		A	)		->       int:
        _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       snake_case_(		self	)		->       Optional[int]:
        _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  a_					(    snake_case_				,   snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    UpperCamelCase			   =			(
        {
            '''depth-estimation''': DPTForDepthEstimation,
            '''feature-extraction''': DPTModel,
            '''image-segmentation''': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    UpperCamelCase			   =			False
    UpperCamelCase			   =			False
    UpperCamelCase			   =			False


    def       snake_case_(		self	)		->       List[Any]:
        _SCREAMING_SNAKE_CASE     =      DPTModelTester(self	)
        _SCREAMING_SNAKE_CASE     =      ConfigTester(self			,		config_class=A			,		has_text_modality=A			,		hidden_size=37	)


    def       snake_case_(		self	)		->       Dict:
        self.config_tester.run_common_tests()


    @unittest.skip(reason="""DPT does not use inputs_embeds"""	)
    def       snake_case_(		self	)		->       Any:
        pass


    def       snake_case_(		self	)		->       Optional[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       snake_case_(		self	)		->       Union[str, 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       snake_case_(		self	)		->       int:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A	)


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*A	)


    def       snake_case_(		self	)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*A	)


    def       snake_case_(		self	)		->       Optional[int]:
        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       snake_case_(		self	)		->       Optional[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       snake_case_(		self	)		->       Dict:
        _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       snake_case_(		self	)		->       int:
        pass





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





def lowerCamelCase						(					)						->Union[str, Any]:
    _SCREAMING_SNAKE_CASE     =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
    return image






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

    '''simple docstring'''


    def       snake_case_(		self	)		->       str:
        _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       snake_case_(		self	)		->       str:
        _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       snake_case_(		self	)		->       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	)

        _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	)










'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



