






'''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  :       Dict  ,     a  :       str  ,     a  :       str=2  ,     a  :       Optional[Any]=3_2  ,     a  :       int=1_6  ,     a  :       Dict=3  ,     a  :       Tuple=True  ,     a  :       Any=True  ,     a  :       List[Any]=3_2  ,     a  :       Optional[int]=4  ,     a  :       str=[0, 1, 2, 3]  ,     a  :       Optional[int]=4  ,     a  :       List[Any]=3_7  ,     a  :       Dict="gelu"  ,     a  :       Optional[int]=0.1  ,     a  :       Dict=0.1  ,     a  :       Optional[Any]=0.02  ,     a  :       List[Any]=3  ,     a  :       int=[1, 3_8_4, 2_4, 2_4]  ,     a  :       Any=True  ,     a  :       Union[str, Any]=None  ,     ):






       '''simple docstring'''






       lowercase_	:    Dict           =     parent
       lowercase_	:    Any           =     batch_size
       lowercase_	:    Optional[int]           =     image_size
       lowercase_	:    Dict           =     patch_size
       lowercase_	:    int           =     num_channels
       lowercase_	:    int           =     is_training
       lowercase_	:    List[str]           =     use_labels
       lowercase_	:    Optional[Any]           =     hidden_size
       lowercase_	:    Optional[Any]           =     num_hidden_layers
       lowercase_	:    Any           =     backbone_out_indices
       lowercase_	:    int           =     num_attention_heads
       lowercase_	:    List[Any]           =     intermediate_size
       lowercase_	:    List[str]           =     hidden_act
       lowercase_	:    str           =     hidden_dropout_prob
       lowercase_	:    List[Any]           =     attention_probs_dropout_prob
       lowercase_	:    List[Any]           =     initializer_range
       lowercase_	:    Optional[int]           =     num_labels
       lowercase_	:    Tuple           =     backbone_featmap_shape
       lowercase_	:    List[str]           =     scope
       lowercase_	:    Optional[int]           =     is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       lowercase_	:    Optional[int]           =     (image_size // patch_size) ** 2
       lowercase_	:    List[str]           =     num_patches + 1




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






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

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

       lowercase_	:    int           =     self.get_config()

       return config, pixel_values, labels




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    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 				lowerCAmelCase__				(			self  :       int  ,     a  :       Optional[int]  ,     a  :       Optional[Any]  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     DPTModel(config=a			)
       model.to(a			)
       model.eval()
       lowercase_	:    int           =     model(a			)
       self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)			)




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Any  ,     a  :       Union[str, Any]  ,     a  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.num_labels
       lowercase_	:    List[Any]           =     DPTForDepthEstimation(a			)
       model.to(a			)
       model.eval()
       lowercase_	:    Any           =     model(a			)
       self.parent.assertEqual(result.predicted_depth.shape  ,     (self.batch_size, self.image_size, self.image_size)			)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Dict  ,     a  :       Union[str, Any]  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.num_labels
       lowercase_	:    Any           =     DPTForSemanticSegmentation(a			)
       model.to(a			)
       model.eval()
       lowercase_	:    Tuple           =     model(a  ,     labels=a			)
       self.parent.assertEqual(
           result.logits.shape  ,     (self.batch_size, self.num_labels, self.image_size, self.image_size)			)





    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.prepare_config_and_inputs()
       lowercase_    ,		lowercase_    ,		lowercase_	:    Optional[Any]           =     config_and_inputs
       lowercase_	:    List[Any]           =     {"pixel_values": pixel_values}
       return config, inputs_dict


@require_torch
class        _UpperCAmelCase						(		snake_case				,					snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Optional[Any]		       =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    __lowerCamelCase:   Optional[int]		       =    (
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    __lowerCamelCase:   Tuple		       =    False
    __lowerCamelCase:   Tuple		       =    False
    __lowerCamelCase:   List[Any]		       =    False




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     DPTModelTester(self			)
       lowercase_	:    str           =     ConfigTester(self  ,     config_class=a  ,     has_text_modality=a  ,     hidden_size=3_7			)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    @unittest.skip(reason="DPT does not use inputs_embeds"			)
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       pass




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    List[Any]           =     self.model_tester.prepare_config_and_inputs_for_common()

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




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Any           =     self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase_	:    List[Any]           =     ["pixel_values"]
          self.assertListEqual(arg_names[:1]  ,     a			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*a			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Dict           =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*a			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






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

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

          if model_class in get_values(a			):
             continue

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




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






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

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

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




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    str           =     self.model_tester.prepare_config_and_inputs_for_common()

       lowercase_	:    Dict           =     _config_zero_init(a			)
       for model_class in self.all_model_classes:
          lowercase_	:    str           =     model_class(config=a			)
          # Skip the check for the backbone
          lowercase_	:    Any           =     []
          for name, module in model.named_modules():
             if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                lowercase_	:    Dict           =     [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 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       pass




    @slow
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
          lowercase_	:    str           =     DPTModel.from_pretrained(a			)
          self.assertIsNotNone(a			)





    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_    ,		lowercase_	:    Optional[int]           =     self.model_tester.prepare_config_and_inputs_for_common()
       lowercase_	:    str           =     "add"
       with self.assertRaises(a			):
          lowercase_	:    Optional[Any]           =     DPTForDepthEstimation(a			)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




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





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




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"			)
       lowercase_	:    Optional[Any]           =     DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"			).to(a			)

       lowercase_	:    Optional[int]           =     prepare_img()
       lowercase_	:    List[str]           =     image_processor(images=a  ,     return_tensors="pt"			).to(a			)

       # forward pass
       with torch.no_grad():
          lowercase_	:    Optional[int]           =     model(**a			)
          lowercase_	:    Tuple           =     outputs.predicted_depth

       # verify the predicted depth
       lowercase_	:    List[Any]           =     torch.Size((1, 3_8_4, 3_8_4)			)
       self.assertEqual(predicted_depth.shape  ,     a			)

       lowercase_	:    int           =     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] / 1_0_0  ,     a  ,     atol=1e-4			)			)












'''simple docstring'''
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   str		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   Dict		       =    'Pix2StructImageProcessor'
    __lowerCamelCase:   Union[str, Any]		       =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(			self  :       str  ,     a  :       Dict  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     False
       super().__init__(a  ,     a			)




    def __call__(			self  :       Tuple  ,     a  :       int=None  ,     a  :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     a  :       bool = True  ,     a  :       Union[bool, str, PaddingStrategy] = False  ,     a  :       Union[bool, str, TruncationStrategy] = None  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = 2_0_4_8  ,     a  :       int = 0  ,     a  :       Optional[int] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = True  ,     a  :       Optional[Union[str, TensorType]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       if images is None and text is None:
          raise ValueError("You have to specify either images or text."			)

       # Get only text
       if images is None and not self.image_processor.is_vqa:
          lowercase_	:    Dict           =     self.tokenizer
          lowercase_	:    Tuple           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )
          return text_encoding

       if not self.image_processor.is_vqa:
          # add pixel_values
          lowercase_	:    Optional[int]           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     **a			)
       else:
          # add pixel_values and bbox
          lowercase_	:    Any           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     header_text=a  ,     **a			)

       if text is not None and not self.image_processor.is_vqa:
          lowercase_	:    int           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )

          if "attention_mask" in text_encoding:
             lowercase_	:    str           =     text_encoding.pop("attention_mask"			)
          if "input_ids" in text_encoding:
             lowercase_	:    Dict           =     text_encoding.pop("input_ids"			)
       else:
          lowercase_	:    str           =     None

       if text_encoding is not None:
          encoding_image_processor.update(a			)

       return encoding_image_processor




    def 				lowerCAmelCase__				(			self  :       Any  ,     *a  :       str  ,     **a  :       Tuple			):






       '''simple docstring'''






       return self.tokenizer.batch_decode(*a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     *a  :       Optional[int]  ,     **a  :       Any			):






       '''simple docstring'''






       return self.tokenizer.decode(*a  ,     **a			)





    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.tokenizer.model_input_names
       lowercase_	:    Dict           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




