



"""simple docstring"""




import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase    : List[str]							=							"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase    : Union[str, Any]							=							direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowerCAmelCase    : Dict							=							transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowerCAmelCase    : List[Any]							=							re.compile(r"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


_lowerCAmelCase    : int							=							{
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Any    )  ->       Dict:





     '''simple docstring'''

     _UpperCAmelCase      :     Dict        =   None

     # source code of `config_class`
     _UpperCAmelCase      :     List[Any]        =   inspect.getsource(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     List[str]        =   _re_checkpoint.findall(SCREAMING_SNAKE_CASE__    )

     # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
     # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
     for ckpt_name, ckpt_link in checkpoints:
          # allow the link to end with `/`
          if ckpt_link.endswith("/"    ):
               _UpperCAmelCase      :     Union[str, Any]        =   ckpt_link[:-1]

          # verify the checkpoint name corresponds to the checkpoint link
          _UpperCAmelCase      :     int        =   f'https://huggingface.co/{ckpt_name}'
          if ckpt_link == ckpt_link_from_name:
               _UpperCAmelCase      :     str        =   ckpt_name
               break

     return checkpoint

def 						__snake_case						(				)  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   []

     for config_class in list(CONFIG_MAPPING.values()    ):
          # Skip deprecated models
          if "models.deprecated" in config_class.__module__:
               continue
          _UpperCAmelCase      :     str        =   get_checkpoint_from_config_class(SCREAMING_SNAKE_CASE__    )

          _UpperCAmelCase      :     List[str]        =   config_class.__name__
          if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(SCREAMING_SNAKE_CASE__    )

     if len(SCREAMING_SNAKE_CASE__    ) > 0:
          _UpperCAmelCase      :     Optional[int]        =   "\n".join(sorted(SCREAMING_SNAKE_CASE__    )    )
          raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'    )


if __name__ == "__main__":
 check_config_docstrings_have_checkpoints()





"""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      UpperCAmelCase_  :



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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Tuple        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

           _UpperCAmelCase      :     str        =   self.get_config()

           return config, pixel_values, labels



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   {
               "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=A					,     initializer_range=self.initializer_range					,     is_hybrid=self.is_hybrid					,     backbone_config=A					,     backbone_featmap_shape=self.backbone_featmap_shape					,     )



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



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



      def 						snake_case_ (					self					:     List[Any]					,     A					:     Any					,     A					:     Optional[int]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   self.num_labels
           _UpperCAmelCase      :     Union[str, Any]        =   DPTForSemanticSegmentation(A						)
           model.to(A						)
           model.eval()
           _UpperCAmelCase      :     int        =   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					:     Union[str, Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.prepare_config_and_inputs()
           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   config_and_inputs
           _UpperCAmelCase      :     int        =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       List[str]								=			(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						:       Dict								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			False



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     List[str]        =   DPTModelTester(self						)
           _UpperCAmelCase      :     Union[str, Any]        =   ConfigTester(self					,     config_class=A					,     has_text_modality=A					,     hidden_size=3_7						)



      def 						snake_case_ (					self					:     int						):
           self.config_tester.run_common_tests()



      @unittest.skip(reason="DPT does not use inputs_embeds"						)
      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

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



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()

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

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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Dict        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A						)



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



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Tuple        =   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

                _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()
                _UpperCAmelCase      :     Optional[int]        =   True

                if model_class in get_values(A						):
                     continue

                _UpperCAmelCase      :     int        =   model_class(A						)
                model.to(A						)
                model.train()
                _UpperCAmelCase      :     Optional[Any]        =   self._prepare_for_class(A					,     A					,     return_labels=A						)
                _UpperCAmelCase      :     Optional[Any]        =   model(**A						).loss
                loss.backward()



      def 						snake_case_ (					self					:     Dict						):
           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      :     Tuple        =   False
                _UpperCAmelCase      :     Tuple        =   True

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



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs_for_common()

           _UpperCAmelCase      :     int        =   _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      :     Dict        =   []
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          _UpperCAmelCase      :     List[str]        =   [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					:     Dict						):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                _UpperCAmelCase      :     Any        =   DPTModel.from_pretrained(A						)
                self.assertIsNotNone(A						)



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







def 						__snake_case						(				)  ->       Optional[int]:





     '''simple docstring'''

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






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



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

           _UpperCAmelCase      :     str        =   prepare_img()
           _UpperCAmelCase      :     Tuple        =   image_processor(images=A					,     return_tensors="pt"						).to(A						)

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

           # verify the predicted depth
           _UpperCAmelCase      :     int        =   torch.Size((1, 3_8_4, 3_8_4)						)
           self.assertEqual(predicted_depth.shape					,     A						)

           _UpperCAmelCase      :     int        =   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] / 1_0_0					,     A					,     atol=1e-4						)						)
