




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				:






       def __init__(      self:    Tuple				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict=2				,    _lowerCAmelCase:    int=32				,    _lowerCAmelCase:    Any=16				,    _lowerCAmelCase:    int=3				,    _lowerCAmelCase:    List[Any]=True				,    _lowerCAmelCase:    int=True				,    _lowerCAmelCase:    Dict=32				,    _lowerCAmelCase:    Any=4				,    _lowerCAmelCase:    Optional[Any]=[0, 1, 2, 3]				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    str=37				,    _lowerCAmelCase:    Optional[Any]="gelu"				,    _lowerCAmelCase:    Dict=0.1				,    _lowerCAmelCase:    Union[str, Any]=0.1				,    _lowerCAmelCase:    Union[str, Any]=0.02				,    _lowerCAmelCase:    Tuple=3				,    _lowerCAmelCase:    int=[1, 3_84, 24, 24]				,    _lowerCAmelCase:    Any=True				,    _lowerCAmelCase:    List[str]=None				,    ):
         lowercase			:Any							=  parent
         lowercase			:Optional[Any]							=  batch_size
         lowercase			:Tuple							=  image_size
         lowercase			:str							=  patch_size
         lowercase			:Optional[Any]							=  num_channels
         lowercase			:Any							=  is_training
         lowercase			:Optional[Any]							=  use_labels
         lowercase			:int							=  hidden_size
         lowercase			:Union[str, Any]							=  num_hidden_layers
         lowercase			:str							=  backbone_out_indices
         lowercase			:List[Any]							=  num_attention_heads
         lowercase			:Union[str, Any]							=  intermediate_size
         lowercase			:List[str]							=  hidden_act
         lowercase			:str							=  hidden_dropout_prob
         lowercase			:List[Any]							=  attention_probs_dropout_prob
         lowercase			:int							=  initializer_range
         lowercase			:Dict							=  num_labels
         lowercase			:int							=  backbone_featmap_shape
         lowercase			:Any							=  scope
         lowercase			:Optional[int]							=  is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         lowercase			:Optional[Any]							=  (image_size // patch_size) ** 2
         lowercase			:List[str]							=  num_patches + 1






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Union[str, Any]							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         lowercase			:List[str]							=  None
         if self.use_labels:
           lowercase			:int							=  ids_tensor([self.batch_size, self.image_size, self.image_size]				,    self.num_labels )

         lowercase			:List[Any]							=  self.get_config()

         return config, pixel_values, labels






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase			:List[Any]							=  {
             "global_padding": "same",
             "layer_type": "bottleneck",
             "depths": [3, 4, 9],
             "out_features": ["stage1", "stage2", "stage3"],
             "embedding_dynamic_padding": True,
             "hidden_sizes": [96, 1_92, 3_84, 7_68],
             "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=_lowerCAmelCase				,    initializer_range=self.initializer_range				,    is_hybrid=self.is_hybrid				,    backbone_config=_lowerCAmelCase				,    backbone_featmap_shape=self.backbone_featmap_shape				,    )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[int]				,    _lowerCAmelCase:    Dict ):
         lowercase			:str							=  DPTModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Any							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    int ):
         lowercase			:List[str]							=  self.num_labels
         lowercase			:Optional[int]							=  DPTForDepthEstimation(_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:int							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.predicted_depth.shape				,    (self.batch_size, self.image_size, self.image_size) )






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






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







@require_torch
class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase					,       unittest.TestCase):
       _a           =  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       _a           =  (
           {
               '''depth-estimation''': DPTForDepthEstimation,
               '''feature-extraction''': DPTModel,
               '''image-segmentation''': DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       _a           =  False
       _a           =  False
       _a           =  False






       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         lowercase			:List[str]							=  DPTModelTester(self )
         lowercase			:List[Any]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    has_text_modality=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






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






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs_for_common()

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






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           lowercase			:List[Any]							=  model_class(_lowerCAmelCase )
           lowercase			:str							=  inspect.signature(model.forward )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowercase			:Union[str, Any]							=  [*signature.parameters.keys()]

           lowercase			:Dict							=  ["pixel_values"]
           self.assertListEqual(arg_names[:1]				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:List[Any]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase )






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

           lowercase		,			lowercase			:Any							=  self.model_tester.prepare_config_and_inputs_for_common()
           lowercase			:List[str]							=  True

           if model_class in get_values(_lowerCAmelCase ):
             continue

           lowercase			:str							=  model_class(_lowerCAmelCase )
           model.to(_lowerCAmelCase )
           model.train()
           lowercase			:Tuple							=  self._prepare_for_class(_lowerCAmelCase				,    _lowerCAmelCase				,    return_labels=_lowerCAmelCase )
           lowercase			:Union[str, Any]							=  model(**_lowerCAmelCase ).loss
           loss.backward()






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

           lowercase		,			lowercase			:List[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
           lowercase			:str							=  False
           lowercase			:Optional[Any]							=  True

           if model_class in get_values(_lowerCAmelCase ) or not model_class.supports_gradient_checkpointing:
             continue
           lowercase			:str							=  model_class(_lowerCAmelCase )
           model.to(_lowerCAmelCase )
           model.gradient_checkpointing_enable()
           model.train()
           lowercase			:Optional[Any]							=  self._prepare_for_class(_lowerCAmelCase				,    _lowerCAmelCase				,    return_labels=_lowerCAmelCase )
           lowercase			:Dict							=  model(**_lowerCAmelCase ).loss
           loss.backward()






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase		,			lowercase			:Any							=  self.model_tester.prepare_config_and_inputs_for_common()

         lowercase			:List[Any]							=  _config_zero_init(_lowerCAmelCase )
         for model_class in self.all_model_classes:
           lowercase			:Optional[int]							=  model_class(config=_lowerCAmelCase )
           # Skip the check for the backbone
           lowercase			:List[str]							=  []
           for name, module in model.named_modules():
             if module.__class__.__name__ == "DPTViTHybridEmbeddings":
               lowercase			:Tuple							=  [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     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         pass






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
           lowercase			:Tuple							=  DPTModel.from_pretrained(_lowerCAmelCase )
           self.assertIsNotNone(_lowerCAmelCase )






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







def 					UpperCAmelCase__     (   ):
  lowercase			:List[str]							=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
  return image







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






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:List[str]							=  DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
         lowercase			:str							=  DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(_lowerCAmelCase )

         lowercase			:Tuple							=  prepare_img()
         lowercase			:Optional[Any]							=  image_processor(images=_lowerCAmelCase				,    return_tensors="pt" ).to(_lowerCAmelCase )

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

         # verify the predicted depth
         lowercase			:List[str]							=  torch.Size((1, 3_84, 3_84) )
         self.assertEqual(predicted_depth.shape				,    _lowerCAmelCase )

         lowercase			:int							=  torch.tensor(
             [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]] ).to(_lowerCAmelCase )

         self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				,    _lowerCAmelCase				,    atol=1e-4 ) )









from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase		:  Tuple	=   {
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  '''ernie_m'''
       _a           =  {"dropout": "classifier_dropout", "num_classes": "num_labels"}






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    int = 25_00_02				,    _lowerCAmelCase:    int = 7_68				,    _lowerCAmelCase:    int = 12				,    _lowerCAmelCase:    int = 12				,    _lowerCAmelCase:    int = 30_72				,    _lowerCAmelCase:    str = "gelu"				,    _lowerCAmelCase:    float = 0.1				,    _lowerCAmelCase:    float = 0.1				,    _lowerCAmelCase:    int = 5_14				,    _lowerCAmelCase:    float = 0.02				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    float = 1e-0_5				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Optional[int]=False				,    _lowerCAmelCase:    List[str]=0.0				,    **_lowerCAmelCase:    Tuple				,    ):
         super().__init__(pad_token_id=_lowerCAmelCase				,    **_lowerCAmelCase )
         lowercase			:Tuple							=  vocab_size
         lowercase			:List[str]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Optional[Any]							=  num_attention_heads
         lowercase			:Optional[Any]							=  intermediate_size
         lowercase			:Optional[Any]							=  hidden_act
         lowercase			:Any							=  hidden_dropout_prob
         lowercase			:int							=  attention_probs_dropout_prob
         lowercase			:Dict							=  max_position_embeddings
         lowercase			:Optional[Any]							=  initializer_range
         lowercase			:Any							=  layer_norm_eps
         lowercase			:Union[str, Any]							=  classifier_dropout
         lowercase			:int							=  is_decoder
         lowercase			:List[str]							=  act_dropout



