

"""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							:      Any, UpperCAmelCase__							:      Tuple, UpperCAmelCase__							:      List[Any]=2, UpperCAmelCase__							:      Optional[Any]=3_2, UpperCAmelCase__							:      int=1_6, UpperCAmelCase__							:      Optional[int]=3, UpperCAmelCase__							:      Dict=True, UpperCAmelCase__							:      Any=True, UpperCAmelCase__							:      str=3_2, UpperCAmelCase__							:      Any=4, UpperCAmelCase__							:      int=[0, 1, 2, 3], UpperCAmelCase__							:      List[Any]=4, UpperCAmelCase__							:      Optional[Any]=3_7, UpperCAmelCase__							:      Optional[Any]="gelu", UpperCAmelCase__							:      Any=0.1, UpperCAmelCase__							:      Tuple=0.1, UpperCAmelCase__							:      List[str]=0.02, UpperCAmelCase__							:      List[Any]=3, UpperCAmelCase__							:      Union[str, Any]=[1, 3_8_4, 2_4, 2_4], UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      int=None, ):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						image_size
    __lowercase								=						patch_size
    __lowercase								=						num_channels
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						backbone_out_indices
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						initializer_range
    __lowercase								=						num_labels
    __lowercase								=						backbone_featmap_shape
    __lowercase								=						scope
    __lowercase								=						is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    __lowercase								=						(image_size // patch_size) ** 2
    __lowercase								=						num_patches + 1


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

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

    __lowercase								=						self.get_config()

    return config, pixel_values, labels


 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						{
        "global_padding": "same",
        "layer_type": "bottleneck",
        "depths": [3, 4, 9],
        "out_features": ["stage1", "stage2", "stage3"],
        "embedding_dynamic_padding": True,
        "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
        "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=_lowercase, initializer_range=self.initializer_range, is_hybrid=self.is_hybrid, backbone_config=_lowercase, backbone_featmap_shape=self.backbone_featmap_shape, )


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      List[str]     ):
    __lowercase								=						DPTModel(config=_lowercase     )
    model.to(_lowercase     )
    model.eval()
    __lowercase								=						model(_lowercase     )
    self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)     )


 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Dict     ):
    __lowercase								=						self.num_labels
    __lowercase								=						DPTForDepthEstimation(_lowercase     )
    model.to(_lowercase     )
    model.eval()
    __lowercase								=						model(_lowercase     )
    self.parent.assertEqual(result.predicted_depth.shape, (self.batch_size, self.image_size, self.image_size)     )


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Any     ):
    __lowercase								=						self.num_labels
    __lowercase								=						DPTForSemanticSegmentation(_lowercase     )
    model.to(_lowercase     )
    model.eval()
    __lowercase								=						model(_lowercase, labels=_lowercase     )
    self.parent.assertEqual(
        result.logits.shape, (self.batch_size, self.num_labels, self.image_size, self.image_size)     )



 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						self.prepare_config_and_inputs()
    __lowercase		,__lowercase		,__lowercase								=						config_and_inputs
    __lowercase								=						{"pixel_values": pixel_values}
    return config, inputs_dict







@require_torch
class  _lowerCAmelCase  (     lowercase					,lowercase					,unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Dict       		=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 __UpperCAmelCase     : Any       		=     (
     {
         """depth-estimation""": DPTForDepthEstimation,
         """feature-extraction""": DPTModel,
         """image-segmentation""": DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __UpperCAmelCase     : List[Any]       		=     False
 __UpperCAmelCase     : Dict       		=     False
 __UpperCAmelCase     : Tuple       		=     False


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						DPTModelTester(self     )
    __lowercase								=						ConfigTester(self, config_class=_lowercase, has_text_modality=_lowercase, hidden_size=3_7     )


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


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


 def  _lowercase			(      self							:      Tuple     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=						model_class(_lowercase     )
       self.assertIsInstance(model.get_input_embeddings(), (nn.Module)     )
       __lowercase								=						model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(_lowercase, nn.Linear     )     )


 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase								=						model_class(_lowercase     )
       __lowercase								=						inspect.signature(model.forward     )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase								=						[*signature.parameters.keys()]

       __lowercase								=						["pixel_values"]
       self.assertListEqual(arg_names[:1], _lowercase     )


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowercase     )


 def  _lowercase			(      self							:      int     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_lowercase     )


 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase     )


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

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

       if model_class in get_values(_lowercase     ):
          continue

       __lowercase								=						model_class(_lowercase     )
       model.to(_lowercase     )
       model.train()
       __lowercase								=						self._prepare_for_class(_lowercase, _lowercase, return_labels=_lowercase     )
       __lowercase								=						model(**_lowercase     ).loss
       loss.backward()


 def  _lowercase			(      self							:      Optional[Any]     ):
    for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
          continue

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

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


 def  _lowercase			(      self							:      List[str]     ):
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()

    __lowercase								=						_config_zero_init(_lowercase     )
    for model_class in self.all_model_classes:
       __lowercase								=						model_class(config=_lowercase     )
       # Skip the check for the backbone
       __lowercase								=						[]
       for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
             __lowercase								=						[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  _lowercase			(      self							:      Any     ):
    pass


 @slow
 def  _lowercase			(      self							:      List[Any]     ):
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       __lowercase								=						DPTModel.from_pretrained(_lowercase     )
       self.assertIsNotNone(_lowercase     )



 def  _lowercase			(      self							:      Optional[int]     ):
    # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
    __lowercase		,__lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    __lowercase								=						"add"
    with self.assertRaises(_lowercase     ):
       __lowercase								=						DPTForDepthEstimation(_lowercase     )







def   _A  (  )     ->						Any:
   '''simple docstring'''


   __lowercase								=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




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







 """simple docstring"""







 def  _lowercase			(      self							:      Any     ):
    __lowercase								=						DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"     )
    __lowercase								=						DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"     ).to(_lowercase     )

    __lowercase								=						prepare_img()
    __lowercase								=						image_processor(images=_lowercase, return_tensors="pt"     ).to(_lowercase     )

    # forward pass
    with torch.no_grad():
       __lowercase								=						model(**_lowercase     )
       __lowercase								=						outputs.predicted_depth

    # verify the predicted depth
    __lowercase								=						torch.Size((1, 3_8_4, 3_8_4)     )
    self.assertEqual(predicted_depth.shape, _lowercase     )

    __lowercase								=						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(_lowercase     )

    self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0, _lowercase, atol=1E-4     )     )




"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a							=							logging.get_logger(__name__)




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     ["pixel_values"]


 def __init__(      self							:      int, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Dict[str, int]] = None, UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BILINEAR, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Union[int, float] = 1 / 2_5_5, UpperCAmelCase__							:      bool = True, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, **UpperCAmelCase__							:      str, ):
    super().__init__(**UpperCAmelCase__     )
    __lowercase								=						size if size is not None else {"shortest_edge": 2_5_6}
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_resize
    __lowercase								=						size
    __lowercase								=						resample
    __lowercase								=						do_center_crop
    __lowercase								=						crop_size
    __lowercase								=						do_rescale
    __lowercase								=						rescale_factor
    __lowercase								=						do_normalize
    __lowercase								=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowercase								=						image_std if image_std is not None else IMAGENET_STANDARD_STD


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      PILImageResampling = PILImageResampling.BICUBIC, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    if "shortest_edge" not in size:
       raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
    __lowercase								=						get_resize_output_image_size(UpperCAmelCase__, size=size["shortest_edge"], default_to_square=UpperCAmelCase__     )
    return resize(UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Dict[str, int], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Dict, ):
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    return center_crop(UpperCAmelCase__, size=(size["height"], size["width"]), data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      float, UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      Union[str, Any]     ):
    return rescale(UpperCAmelCase__, scale=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      np.ndarray, UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Union[float, List[float]], UpperCAmelCase__							:      Optional[Union[str, ChannelDimension]] = None, **UpperCAmelCase__							:      int, ):
    return normalize(UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__, data_format=UpperCAmelCase__, **UpperCAmelCase__     )



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      ImageInput, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      PILImageResampling = None, UpperCAmelCase__							:      bool = None, UpperCAmelCase__							:      Dict[str, int] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[float] = None, UpperCAmelCase__							:      Optional[bool] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[float, List[float]]] = None, UpperCAmelCase__							:      Optional[Union[str, TensorType]] = None, UpperCAmelCase__							:      Union[str, ChannelDimension] = ChannelDimension.FIRST, **UpperCAmelCase__							:      Optional[int], ):
    __lowercase								=						do_resize if do_resize is not None else self.do_resize
    __lowercase								=						size if size is not None else self.size
    __lowercase								=						get_size_dict(UpperCAmelCase__, default_to_square=UpperCAmelCase__     )
    __lowercase								=						resample if resample is not None else self.resample
    __lowercase								=						do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowercase								=						crop_size if crop_size is not None else self.crop_size
    __lowercase								=						get_size_dict(UpperCAmelCase__     )
    __lowercase								=						do_rescale if do_rescale is not None else self.do_rescale
    __lowercase								=						rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowercase								=						do_normalize if do_normalize is not None else self.do_normalize
    __lowercase								=						image_mean if image_mean is not None else self.image_mean
    __lowercase								=						image_std if image_std is not None else self.image_std

    __lowercase								=						make_list_of_images(UpperCAmelCase__     )

    if not valid_images(UpperCAmelCase__     ):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray."     )

    if do_resize and size is None:
       raise ValueError("Size must be specified if do_resize is True."     )

    if do_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop is True."     )

    if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True."     )

    if do_normalize and (image_mean is None or image_std is None):
       raise ValueError("Image mean and std must be specified if do_normalize is True."     )

    # All transformations expect numpy arrays.
    __lowercase								=						[to_numpy_array(UpperCAmelCase__     ) for image in images]

    if do_resize:
       __lowercase								=						[self.resize(image=UpperCAmelCase__, size=UpperCAmelCase__, resample=UpperCAmelCase__     ) for image in images]

    if do_center_crop:
       __lowercase								=						[self.center_crop(image=UpperCAmelCase__, size=UpperCAmelCase__     ) for image in images]

    if do_rescale:
       __lowercase								=						[self.rescale(image=UpperCAmelCase__, scale=UpperCAmelCase__     ) for image in images]

    if do_normalize:
       __lowercase								=						[self.normalize(image=UpperCAmelCase__, mean=UpperCAmelCase__, std=UpperCAmelCase__     ) for image in images]

    __lowercase								=						[to_channel_dimension_format(UpperCAmelCase__, UpperCAmelCase__     ) for image in images]

    __lowercase								=						{"pixel_values": images}
    return BatchFeature(data=UpperCAmelCase__, tensor_type=UpperCAmelCase__     )




