'''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 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				Optional[Any]						,					A						:				Any=2						,					A						:				Union[str, Any]=32						,					A						:				Any=16						,					A						:				Any=3						,					A						:				Optional[Any]=True						,					A						:				Optional[Any]=True						,					A						:				Any=32						,					A						:				Tuple=4						,					A						:				int=[0, 1, 2, 3]						,					A						:				Tuple=4						,					A						:				Union[str, Any]=37						,					A						:				List[Any]="gelu"						,					A						:				Any=0.1						,					A						:				Optional[Any]=0.1						,					A						:				Tuple=0.02						,					A						:				int=3						,					A						:				Union[str, Any]=[1, 384, 24, 24]						,					A						:				Optional[Any]=True						,					A						:				Optional[int]=None						,					):
             _UpperCAmelCase     :  Union[str, Any]							      =    parent
             _UpperCAmelCase     :  Union[str, Any]							      =    batch_size
             _UpperCAmelCase     :  int							      =    image_size
             _UpperCAmelCase     :  Tuple							      =    patch_size
             _UpperCAmelCase     :  int							      =    num_channels
             _UpperCAmelCase     :  Union[str, Any]							      =    is_training
             _UpperCAmelCase     :  int							      =    use_labels
             _UpperCAmelCase     :  List[Any]							      =    hidden_size
             _UpperCAmelCase     :  List[str]							      =    num_hidden_layers
             _UpperCAmelCase     :  List[str]							      =    backbone_out_indices
             _UpperCAmelCase     :  List[Any]							      =    num_attention_heads
             _UpperCAmelCase     :  Union[str, Any]							      =    intermediate_size
             _UpperCAmelCase     :  Optional[int]							      =    hidden_act
             _UpperCAmelCase     :  Tuple							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Optional[Any]							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Optional[int]							      =    initializer_range
             _UpperCAmelCase     :  int							      =    num_labels
             _UpperCAmelCase     :  Tuple							      =    backbone_featmap_shape
             _UpperCAmelCase     :  List[str]							      =    scope
             _UpperCAmelCase     :  List[str]							      =    is_hybrid
             # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
             _UpperCAmelCase     :  List[Any]							      =    (image_size // patch_size) ** 2
             _UpperCAmelCase     :  Optional[Any]							      =    num_patches + 1




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Any							      =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

             _UpperCAmelCase     :  str							      =    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						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    {
                 "global_padding": "same",
                 "layer_type": "bottleneck",
                 "depths": [3, 4, 9],
                 "out_features": ["stage1", "stage2", "stage3"],
                 "embedding_dynamic_padding": True,
                 "hidden_sizes": [96, 192, 384, 768],
                 "num_groups": 2,
             }

             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						,					backbone_config=A						,					backbone_featmap_shape=self.backbone_featmap_shape						,					)




      def      _A	(       self						:				Any						,					A						:				Optional[int]						,					A						:				List[Any]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    DPTModel(config=A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Tuple							      =    model(A       )
             self.parent.assertEqual(result.last_hidden_state.shape						,					(self.batch_size, self.seq_length, self.hidden_size)       )




      def      _A	(       self						:				Dict						,					A						:				Union[str, Any]						,					A						:				List[Any]						,					A						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.num_labels
             _UpperCAmelCase     :  int							      =    DPTForDepthEstimation(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  List[Any]							      =    model(A       )
             self.parent.assertEqual(result.predicted_depth.shape						,					(self.batch_size, self.image_size, self.image_size)       )




      def      _A	(       self						:				int						,					A						:				List[str]						,					A						:				Tuple						,					A						:				List[str]       ):
             _UpperCAmelCase     :  Dict							      =    self.num_labels
             _UpperCAmelCase     :  List[Any]							      =    DPTForSemanticSegmentation(A       )
             model.to(A       )
             model.eval()
             _UpperCAmelCase     :  Union[str, Any]							      =    model(A						,					labels=A       )
             self.parent.assertEqual(
                 result.logits.shape						,					(self.batch_size, self.num_labels, self.image_size, self.image_size)       )





      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.prepare_config_and_inputs()
             _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase     :  List[str]							      =    config_and_inputs
             _UpperCAmelCase     :  Optional[Any]							      =    {"pixel_values": pixel_values}
             return config, inputs_dict

@require_torch
class 	lowerCamelCase_	(snake_case__							, snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  List[Any]						=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      __UpperCamelCase:  Union[str, Any]						=						(
          {
              "depth-estimation": DPTForDepthEstimation,
              "feature-extraction": DPTModel,
              "image-segmentation": DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __UpperCamelCase:  Optional[int]						=						False
      __UpperCamelCase:  str						=						False
      __UpperCamelCase:  Tuple						=						False




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    DPTModelTester(self       )
             _UpperCAmelCase     :  Optional[int]							      =    ConfigTester(self						,					config_class=A						,					has_text_modality=A						,					hidden_size=37       )




      def      _A	(       self						:				Union[str, Any]       ):
             self.config_tester.run_common_tests()




      @unittest.skip(reason="DPT does not use inputs_embeds"       )
      def      _A	(       self						:				int       ):
             pass




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _UpperCAmelCase     :  Union[str, Any]							      =    model_class(A       )
                    self.assertIsInstance(model.get_input_embeddings()						,					(nn.Module)       )
                    _UpperCAmelCase     :  List[str]							      =    model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(A						,					nn.Linear       )       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    self.model_tester.prepare_config_and_inputs_for_common()

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

                    _UpperCAmelCase     :  int							      =    ["pixel_values"]
                    self.assertListEqual(arg_names[:1]						,					A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[Any]							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Dict							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*A       )




      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*A       )




      def      _A	(       self						:				str       ):
             for model_class in self.all_model_classes:
                    if model_class.__name__ == "DPTForDepthEstimation":
                           continue

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

                    if model_class in get_values(A       ):
                           continue

                    _UpperCAmelCase     :  int							      =    model_class(A       )
                    model.to(A       )
                    model.train()
                    _UpperCAmelCase     :  str							      =    self._prepare_for_class(A						,					A						,					return_labels=A       )
                    _UpperCAmelCase     :  Dict							      =    model(**A       ).loss
                    loss.backward()




      def      _A	(       self						:				Tuple       ):
             for model_class in self.all_model_classes:
                    if model_class.__name__ == "DPTForDepthEstimation":
                           continue

                    _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()
                    _UpperCAmelCase     :  Dict							      =    False
                    _UpperCAmelCase     :  List[Any]							      =    True

                    if model_class in get_values(A       ) or not model_class.supports_gradient_checkpointing:
                           continue
                    _UpperCAmelCase     :  Tuple							      =    model_class(A       )
                    model.to(A       )
                    model.gradient_checkpointing_enable()
                    model.train()
                    _UpperCAmelCase     :  List[Any]							      =    self._prepare_for_class(A						,					A						,					return_labels=A       )
                    _UpperCAmelCase     :  Tuple							      =    model(**A       ).loss
                    loss.backward()




      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[Any]							      =    self.model_tester.prepare_config_and_inputs_for_common()

             _UpperCAmelCase     :  List[str]							      =    _config_zero_init(A       )
             for model_class in self.all_model_classes:
                    _UpperCAmelCase     :  List[Any]							      =    model_class(config=A       )
                    # Skip the check for the backbone
                    _UpperCAmelCase     :  Union[str, Any]							      =    []
                    for name, module in model.named_modules():
                           if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                                  _UpperCAmelCase     :  Union[str, Any]							      =    [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      _A	(       self						:				Union[str, Any]       ):
             pass




      @slow
      def      _A	(       self						:				List[Any]       ):
             for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                    _UpperCAmelCase     :  Optional[int]							      =    DPTModel.from_pretrained(A       )
                    self.assertIsNotNone(A       )





      def      _A	(       self						:				Tuple       ):
             # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Tuple							      =    self.model_tester.prepare_config_and_inputs_for_common()
             _UpperCAmelCase     :  str							      =    "add"
             with self.assertRaises(A       ):
                    _UpperCAmelCase     :  List[str]							      =    DPTForDepthEstimation(A       )

def 	UpperCamelCase_							(					)    ->    Any:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[Any]							      =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image


@require_torch
@require_vision
@slow
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  int							      =    DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"       )
             _UpperCAmelCase     :  List[str]							      =    DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"       ).to(A       )

             _UpperCAmelCase     :  List[Any]							      =    prepare_img()
             _UpperCAmelCase     :  int							      =    image_processor(images=A						,					return_tensors="pt"       ).to(A       )

             # forward pass
             with torch.no_grad():
                    _UpperCAmelCase     :  Union[str, Any]							      =    model(**A       )
                    _UpperCAmelCase     :  List[Any]							      =    outputs.predicted_depth

             # verify the predicted depth
             _UpperCAmelCase     :  List[str]							      =    torch.Size((1, 384, 384)       )
             self.assertEqual(predicted_depth.shape						,					A       )

             _UpperCAmelCase     :  Tuple							      =    torch.tensor(
                 [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]       ).to(A       )

             self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100						,					A						,					atol=1E-4       )       )


'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						1




      @register_to_config
      def __init__(       self						:				Optional[int]						,					A						:				int = 1000						,					A						:				Optional[Union[np.ndarray, List[float]]] = None       ):
             # set `betas`, `alphas`, `timesteps`
             self.set_timesteps(A       )

             # standard deviation of the initial noise distribution
             _UpperCAmelCase     :  int							      =    1.0

             # For now we only support F-PNDM, i.e. the runge-kutta method
             # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
             # mainly at formula (9), (12), (13) and the Algorithm 2.
             _UpperCAmelCase     :  int							      =    4

             # running values
             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				int						,					A						:				Union[str, torch.device] = None       ):
             _UpperCAmelCase     :  int							      =    num_inference_steps
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.linspace(1						,					0						,					num_inference_steps + 1       )[:-1]
             _UpperCAmelCase     :  Any							      =    torch.cat([steps, torch.tensor([0.0]       )]       )

             if self.config.trained_betas is not None:
                    _UpperCAmelCase     :  str							      =    torch.tensor(self.config.trained_betas						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  Dict							      =    torch.sin(steps * math.pi / 2       ) ** 2

             _UpperCAmelCase     :  List[Any]							      =    (1.0 - self.betas**2) ** 0.5

             _UpperCAmelCase     :  List[str]							      =    (torch.atana(self.betas						,					self.alphas       ) / math.pi * 2)[:-1]
             _UpperCAmelCase     :  Dict							      =    timesteps.to(A       )

             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				torch.FloatTensor						,					A						:				int						,					A						:				torch.FloatTensor						,					A						:				bool = True						,					):
             if self.num_inference_steps is None:
                    raise ValueError(
                        "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

             _UpperCAmelCase     :  Tuple							      =    (self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase     :  Optional[Any]							      =    timestep_index + 1

             _UpperCAmelCase     :  int							      =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
             self.ets.append(A       )

             if len(self.ets       ) == 1:
                    _UpperCAmelCase     :  List[Any]							      =    self.ets[-1]
             elif len(self.ets       ) == 2:
                    _UpperCAmelCase     :  str							      =    (3 * self.ets[-1] - self.ets[-2]) / 2
             elif len(self.ets       ) == 3:
                    _UpperCAmelCase     :  Tuple							      =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
             else:
                    _UpperCAmelCase     :  Union[str, Any]							      =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

             _UpperCAmelCase     :  Union[str, Any]							      =    self._get_prev_sample(A						,					A						,					A						,					A       )

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				torch.FloatTensor						,					*A						:				Union[str, Any]						,					**A						:				Dict       ):
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[int]						,					A						:				int						,					A						:				Optional[Any]						,					A						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    self.alphas[timestep_index]
             _UpperCAmelCase     :  List[Any]							      =    self.betas[timestep_index]

             _UpperCAmelCase     :  Optional[Any]							      =    self.alphas[prev_timestep_index]
             _UpperCAmelCase     :  Dict							      =    self.betas[prev_timestep_index]

             _UpperCAmelCase     :  Tuple							      =    (sample - sigma * ets) / max(A						,					1E-8       )
             _UpperCAmelCase     :  List[str]							      =    next_alpha * pred + ets * next_sigma

             return prev_sample





      def __len__(       self						:				Union[str, Any]       ):
             return self.config.num_train_timesteps

