
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 				lowercase					:



		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_=[1, 384, 24, 24]				,      A_=True				,      A_=None				,      )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     patch_size
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     backbone_out_indices
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     hidden_dropout_prob
				UpperCamelCase		=     attention_probs_dropout_prob
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     backbone_featmap_shape
				UpperCamelCase		=     scope
				UpperCamelCase		=     is_hybrid
				# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
				UpperCamelCase		=     (image_size // patch_size) ** 2
				UpperCamelCase		=     num_patches + 1



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

				UpperCamelCase		=     self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {
				    '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 __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     DPTModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.seq_length, self.hidden_size)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     DPTForDepthEstimation(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				self.parent.assertEqual(result.predicted_depth.shape				,      (self.batch_size, self.image_size, self.image_size)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     DPTForSemanticSegmentation(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(
				    result.logits.shape				,      (self.batch_size, self.num_labels, self.image_size, self.image_size)       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 				lowercase					(		__UpperCAmelCase  ,   __UpperCAmelCase  ,   unittest.TestCase   ):
		__lowercase       :       Tuple	     =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		__lowercase       :       Tuple	     =				(
		    {
		        '''depth-estimation''': DPTForDepthEstimation,
		        '''feature-extraction''': DPTModel,
		        '''image-segmentation''': DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       str	     =				False
		__lowercase       :       Optional[int]	     =				False
		__lowercase       :       int	     =				False



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     DPTModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='DPT does not use inputs_embeds'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

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



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase		=     ['pixel_values']
						self.assertListEqual(arg_names[:1]				,      A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_depth_estimation(*A_       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_semantic_segmentation(*A_       )



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""

				for model_class in self.all_model_classes:
						if model_class.__name__ == "DPTForDepthEstimation":
								continue

						UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase		=     True

						if model_class in get_values(A_       ):
								continue

						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.train()
						UpperCamelCase		=     self._prepare_for_class(A_				,      A_				,      return_labels=A_       )
						UpperCamelCase		=     model(**A_       ).loss
						loss.backward()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""

				for model_class in self.all_model_classes:
						if model_class.__name__ == "DPTForDepthEstimation":
								continue

						UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCamelCase		=     False
						UpperCamelCase		=     True

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



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				UpperCamelCase		=     _config_zero_init(A_       )
				for model_class in self.all_model_classes:
						UpperCamelCase		=     model_class(config=A_       )
						# Skip the check for the backbone
						UpperCamelCase		=     []
						for name, module in model.named_modules():
								if module.__class__.__name__ == "DPTViTHybridEmbeddings":
										UpperCamelCase		=     [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 __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""

				for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
						UpperCamelCase		=     DPTModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase		=     'add'
				with self.assertRaises(A_       ):
						UpperCamelCase		=     DPTForDepthEstimation(A_       )






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

		UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image


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



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'       )
				UpperCamelCase		=     DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'       ).to(A_       )

				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       ).to(A_       )

				# forward pass
				with torch.no_grad():
						UpperCamelCase		=     model(**A_       )
						UpperCamelCase		=     outputs.predicted_depth

				# verify the predicted depth
				UpperCamelCase		=     torch.Size((1, 384, 384)       )
				self.assertEqual(predicted_depth.shape				,      A_       )

				UpperCamelCase		=     torch.tensor(
				    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]       ).to(A_       )

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







import argparse
import os

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_task_guides.py
lowercase      :       List[str]									=  """src/transformers"""
lowercase      :       Optional[int]									=  """docs/source/en/tasks"""



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              end_index += 1
       end_index -= 1

       while len(lines[start_index]				) <= 1:
              start_index += 1
       while len(lines[end_index]				) <= 1:
              end_index -= 1
       end_index += 1
       return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

lowercase      :       Optional[Any]									=  {
    """asr.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    """audio_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    """language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    """image_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    """masked_language_modeling.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    """multiple_choice.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    """object_detection.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    """question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    """semantic_segmentation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    """sequence_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    """summarization.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """token_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    """translation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    """video_classification.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    """document_question_answering.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    """monocular_depth_estimation.md""": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
lowercase      :       Optional[Any]									=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      set()				)
       a__						:	Optional[Any] =				{
           code: name
           for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
           if (code in model_maping_names or code in special_model_types)
       }
       return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()]				) + "\n"



def A_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

       if current_list != new_list:
              if overwrite:
                     with open(os.path.join(A__		,      A__				)		,      'w'		,      encoding='utf-8'		,      newline='\n'				) as f:
                            f.writelines(lines[:start_index] + [new_list] + lines[end_index:]				)
              else:
                     raise ValueError(
                         F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
                         ' to fix this.'				)


if __name__ == "__main__":
    lowercase      :       Dict									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    for task_guide in TASK_GUIDE_TO_MODELS.keys():
        check_model_list_for_task(task_guide, args.fix_and_overwrite)


