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

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

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

     return config, pixel_values, labels
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     SCREAMING_SNAKE_CASE__:   Dict=      {
         '''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=lowerCAmelCase , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=lowerCAmelCase , backbone_featmap_shape=self.backbone_featmap_shape , )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Tuple=      DPTModel(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__:   List[Any]=      model(lowerCAmelCase   )
     self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					List[str]:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      self.num_labels
     SCREAMING_SNAKE_CASE__:   int=      DPTForDepthEstimation(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__:   str=      model(lowerCAmelCase   )
     self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size)   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Any=      self.num_labels
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      DPTForSemanticSegmentation(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     SCREAMING_SNAKE_CASE__:   Dict=      model(lowerCAmelCase , labels=lowerCAmelCase   )
     self.parent.assertEqual(
         result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size)   )



    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      config_and_inputs
     SCREAMING_SNAKE_CASE__:   int=      {'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class 		_lowerCamelCase		(   UpperCamelCase_    ,     UpperCamelCase_    ,     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 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Any=      DPTModelTester(self   )
     SCREAMING_SNAKE_CASE__:   Tuple=      ConfigTester(self , config_class=lowerCAmelCase , has_text_modality=lowerCAmelCase , hidden_size=37   )
    def 						UpperCamelCase_				(  self   )       ->					Tuple:
     self.config_tester.run_common_tests()
    @unittest.skip(reason='''DPT does not use inputs_embeds'''   )
    def 						UpperCamelCase_				(  self   )       ->					int:
     pass
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   int=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__:   List[str]=      model_class(lowerCAmelCase   )
      self.assertIsInstance(model.get_input_embeddings() , (nn.Module)   )
      SCREAMING_SNAKE_CASE__:   Tuple=      model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(lowerCAmelCase , nn.Linear   )   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      self.model_tester.prepare_config_and_inputs_for_common()

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

      SCREAMING_SNAKE_CASE__:   List[Any]=      ['''pixel_values''']
      self.assertListEqual(arg_names[:1] , lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Dict=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase   )
    def 						UpperCamelCase_				(  self   )       ->					Dict:
     for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
       continue

      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[Any]=      self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE__:   Tuple=      True

      if model_class in get_values(lowerCAmelCase   ):
       continue

      SCREAMING_SNAKE_CASE__:   Optional[int]=      model_class(lowerCAmelCase   )
      model.to(lowerCAmelCase   )
      model.train()
      SCREAMING_SNAKE_CASE__:   Dict=      self._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   List[Any]=      model(**lowerCAmelCase   ).loss
      loss.backward()
    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
       continue

      SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      self.model_tester.prepare_config_and_inputs_for_common()
      SCREAMING_SNAKE_CASE__:   Dict=      False
      SCREAMING_SNAKE_CASE__:   Tuple=      True

      if model_class in get_values(lowerCAmelCase   ) or not model_class.supports_gradient_checkpointing:
       continue
      SCREAMING_SNAKE_CASE__:   Optional[int]=      model_class(lowerCAmelCase   )
      model.to(lowerCAmelCase   )
      model.gradient_checkpointing_enable()
      model.train()
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      self._prepare_for_class(lowerCAmelCase , lowerCAmelCase , return_labels=lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Optional[int]=      model(**lowerCAmelCase   ).loss
      loss.backward()
    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   str=      self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__:   int=      _config_zero_init(lowerCAmelCase   )
     for model_class in self.all_model_classes:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      model_class(config=lowerCAmelCase   )
      # Skip the check for the backbone
      SCREAMING_SNAKE_CASE__:   Dict=      []
      for name, module in model.named_modules():
       if module.__class__.__name__ == "DPTViTHybridEmbeddings":
        SCREAMING_SNAKE_CASE__:   Optional[int]=      [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   )       ->					int:
     pass
    @slow
    def 						UpperCamelCase_				(  self   )       ->					Any:
     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
      SCREAMING_SNAKE_CASE__:   Optional[int]=      DPTModel.from_pretrained(lowerCAmelCase   )
      self.assertIsNotNone(lowerCAmelCase   )



    def 						UpperCamelCase_				(  self   )       ->					Union[str, Any]:
     # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE__:   List[str]=      '''add'''
     with self.assertRaises(lowerCAmelCase   ):
      SCREAMING_SNAKE_CASE__:   int=      DPTForDepthEstimation(lowerCAmelCase   )







def 			A__      (	):
 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
 return image






@require_torch
@require_vision
@slow
class 		_lowerCamelCase		(   unittest.TestCase    ):
    def 						UpperCamelCase_				(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   Dict=      DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''   ).to(lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Dict=      prepare_img()
     SCREAMING_SNAKE_CASE__:   Optional[int]=      image_processor(images=lowerCAmelCase , return_tensors='''pt'''   ).to(lowerCAmelCase   )

     # forward pass
     with torch.no_grad():
      SCREAMING_SNAKE_CASE__:   Tuple=      model(**lowerCAmelCase   )
      SCREAMING_SNAKE_CASE__:   Tuple=      outputs.predicted_depth

     # verify the predicted depth
     SCREAMING_SNAKE_CASE__:   Any=      torch.Size((1, 384, 384)   )
     self.assertEqual(predicted_depth.shape , lowerCAmelCase   )

     SCREAMING_SNAKE_CASE__:   Tuple=      torch.tensor(
         [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(lowerCAmelCase   )

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


import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_     :   List[str] 				=				{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class 		_lowerCamelCase		(   nn.Module    ):
    def __init__(  self , lowerCAmelCase   )       ->					str:
     super().__init__()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torchvision.models.resnetaaa(pretrained=lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(model.children()   )[:-2]
     SCREAMING_SNAKE_CASE__:   Tuple=      nn.Sequential(*lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
     SCREAMING_SNAKE_CASE__:   str=      self.pool(self.model(lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__:   str=      torch.flatten(lowerCAmelCase , start_dim=2   )
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      out.transpose(1 , 2   ).contiguous()
     return out  # BxNx2048






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase   )       ->					Any:
     SCREAMING_SNAKE_CASE__:   Optional[int]=      [json.loads(lowerCAmelCase   ) for l in open(lowerCAmelCase   )]
     SCREAMING_SNAKE_CASE__:   List[str]=      os.path.dirname(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   int=      tokenizer
     SCREAMING_SNAKE_CASE__:   Any=      labels
     SCREAMING_SNAKE_CASE__:   List[Any]=      len(lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   Any=      max_seq_length

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      transforms
    def __len__(  self   )       ->					Optional[Any]:
     return len(self.data   )
    def __getitem__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.LongTensor(self.tokenizer.encode(self.data[index]['''text'''] , add_special_tokens=lowerCAmelCase   )   )
     SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Tuple=      sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE__:   Tuple=      sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.zeros(self.n_classes   )
     SCREAMING_SNAKE_CASE__:   Dict=      1

     SCREAMING_SNAKE_CASE__:   Optional[int]=      Image.open(os.path.join(self.data_dir , self.data[index]['''img''']   )   ).convert('''RGB'''   )
     SCREAMING_SNAKE_CASE__:   int=      self.transforms(lowerCAmelCase   )

     return {
         "image_start_token": start_token,
         "image_end_token": end_token,
         "sentence": sentence,
         "image": image,
         "label": label,
     }



    def 						UpperCamelCase_				(  self   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      Counter()
     for row in self.data:
      label_freqs.update(row['''label''']   )
     return label_freqs







def 			A__      (	snake_case_ :		int      ):
 SCREAMING_SNAKE_CASE__:   Any=      [len(row['''sentence''']      ) for row in batch]
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[int]=      len(snake_case_      ), max(snake_case_      )

 SCREAMING_SNAKE_CASE__:   Union[str, Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )
 SCREAMING_SNAKE_CASE__:   List[Any]=      torch.zeros(snake_case_       ,				snake_case_       ,				dtype=torch.long      )

 for i_batch, (input_row, length) in enumerate(zip(snake_case_       ,				snake_case_      )      ):
  SCREAMING_SNAKE_CASE__:   Optional[int]=      input_row['''sentence''']
  SCREAMING_SNAKE_CASE__:   Dict=      1

 SCREAMING_SNAKE_CASE__:   Optional[Any]=      torch.stack([row['''image'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.stack([row['''label'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Any=      torch.stack([row['''image_start_token'''] for row in batch]      )
 SCREAMING_SNAKE_CASE__:   Optional[int]=      torch.stack([row['''image_end_token'''] for row in batch]      )

 return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 			A__      (	):
 return [
     "Crime",
     "Drama",
     "Thriller",
     "Action",
     "Comedy",
     "Romance",
     "Documentary",
     "Short",
     "Mystery",
     "History",
     "Family",
     "Adventure",
     "Fantasy",
     "Sci-Fi",
     "Western",
     "Horror",
     "Sport",
     "War",
     "Music",
     "Musical",
     "Animation",
     "Biography",
     "Film-Noir",
 ]







def 			A__      (	):
 return transforms.Compose(
     [
         transforms.Resize(256      ),
         transforms.CenterCrop(224      ),
         transforms.ToTensor(),
         transforms.Normalize(
             mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]       ,				std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]       ,				),
     ]      )

