


"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__				:  int						= logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__				:  Optional[Any]						= {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
SCREAMING_SNAKE_CASE__				:  Optional[Any]						= {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

SCREAMING_SNAKE_CASE__				:  Dict						= {
    "RUCAIBox/mvp": 1024,
}




@lru_cache()
def        A_					(					)     ->     Dict:
   a   :	List[str]						=    (
       list(range(ord('!'				)       ,   ord('~'				) + 1				)				) + list(range(ord('¡'				)       ,   ord('¬'				) + 1				)				) + list(range(ord('®'				)       ,   ord('ÿ'				) + 1				)				)
   )
   a   :	Dict						=    bs[:]
   a   :	str						=    0
   for b in range(2**8				):
      if b not in bs:
         bs.append(lowerCamelCase__				)
         cs.append(2**8 + n				)
         n += 1
   a   :	str						=    [chr(lowerCamelCase__				) for n in cs]
   return dict(zip(lowerCamelCase__       ,   lowerCamelCase__				)				)




def        A_					(					UpperCAmelCase__				)     ->     Any:
   a   :	Union[str, Any]						=    set()
   a   :	str						=    word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)				)
      a   :	List[str]						=    char
   return pairs


class 	A_       (     _a   ):





  """simple docstring"""

  lowercase		:	Any   =						VOCAB_FILES_NAMES
  lowercase		:	Dict   =						PRETRAINED_VOCAB_FILES_MAP
  lowercase		:	Union[str, Any]   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase		:	Any   =						['input_ids', 'attention_mask']
  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase="replace"  ,					__UpperCAmelCase="<s>"  ,					__UpperCAmelCase="</s>"  ,					__UpperCAmelCase="</s>"  ,					__UpperCAmelCase="<s>"  ,					__UpperCAmelCase="<unk>"  ,					__UpperCAmelCase="<pad>"  ,					__UpperCAmelCase="<mask>"  ,					__UpperCAmelCase=False  ,					**__UpperCAmelCase  ,					)				->					List[Any]:
     a   :	int						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else bos_token
     a   :	Any						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else eos_token
     a   :	List[Any]						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else sep_token
     a   :	Optional[int]						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else cls_token
     a   :	str						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else unk_token
     a   :	Tuple						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     a   :	Tuple						=    AddedToken(__UpperCAmelCase  ,					lstrip=__UpperCAmelCase  ,					rstrip=__UpperCAmelCase						) if isinstance(__UpperCAmelCase  ,					__UpperCAmelCase						) else mask_token

     super().__init__(
         errors=__UpperCAmelCase  ,					bos_token=__UpperCAmelCase  ,					eos_token=__UpperCAmelCase  ,					unk_token=__UpperCAmelCase  ,					sep_token=__UpperCAmelCase  ,					cls_token=__UpperCAmelCase  ,					pad_token=__UpperCAmelCase  ,					mask_token=__UpperCAmelCase  ,					add_prefix_space=__UpperCAmelCase  ,					**__UpperCAmelCase  ,					)
     with open(__UpperCAmelCase  ,					encoding='utf-8'						) as vocab_handle:
        a   :	int						=    json.load(__UpperCAmelCase						)
     a   :	Optional[int]						=    {v: k for k, v in self.encoder.items()}
     a   :	int						=    errors  # how to handle errors in decoding
     a   :	Dict						=    bytes_to_unicode()
     a   :	int						=    {v: k for k, v in self.byte_encoder.items()}
     with open(__UpperCAmelCase  ,					encoding='utf-8'						) as merges_handle:
        a   :	int						=    merges_handle.read().split('\n'						)[1:-1]
     a   :	List[str]						=    [tuple(merge.split()						) for merge in bpe_merges]
     a   :	List[Any]						=    dict(zip(__UpperCAmelCase  ,					range(len(__UpperCAmelCase						)						)						)						)
     a   :	List[Any]						=    {}
     a   :	Any						=    add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     a   :	List[str]						=    re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'						)
  @property
  def 							lowercase_     (						self						)				->					Dict:
     return len(self.encoder						)
  def 							lowercase_     (						self						)				->					Tuple:
     return dict(self.encoder  ,					**self.added_tokens_encoder						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[str]:
     if token in self.cache:
        return self.cache[token]
     a   :	str						=    tuple(__UpperCAmelCase						)
     a   :	Dict						=    get_pairs(__UpperCAmelCase						)

     if not pairs:
        return token

     while True:
        a   :	Tuple						=    min(__UpperCAmelCase  ,					key=lambda __UpperCAmelCase						: self.bpe_ranks.get(__UpperCAmelCase  ,					float('inf'						)						)						)
        if bigram not in self.bpe_ranks:
           break
        a	, a   :	List[str]						=    bigram
        a   :	Any						=    []
        a   :	str						=    0
        while i < len(__UpperCAmelCase						):
           try:
              a   :	int						=    word.index(__UpperCAmelCase  ,					__UpperCAmelCase						)
           except ValueError:
              new_word.extend(word[i:]						)
              break
           else:
              new_word.extend(word[i:j]						)
              a   :	List[str]						=    j

           if word[i] == first and i < len(__UpperCAmelCase						) - 1 and word[i + 1] == second:
              new_word.append(first + second						)
              i += 2
           else:
              new_word.append(word[i]						)
              i += 1
        a   :	Optional[Any]						=    tuple(__UpperCAmelCase						)
        a   :	Tuple						=    new_word
        if len(__UpperCAmelCase						) == 1:
           break
        else:
           a   :	Optional[int]						=    get_pairs(__UpperCAmelCase						)
     a   :	Optional[int]						=    ' '.join(__UpperCAmelCase						)
     a   :	List[str]						=    word
     return word
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Any:
     a   :	Any						=    []
     for token in re.findall(self.pat  ,					__UpperCAmelCase						):
        a   :	List[str]						=    ''.join(
            self.byte_encoder[b] for b in token.encode('utf-8'						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__UpperCAmelCase						).split(' '						)						)
     return bpe_tokens
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Dict:
     return self.encoder.get(__UpperCAmelCase  ,					self.encoder.get(self.unk_token						)						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					str:
     return self.decoder.get(__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[str]:
     a   :	List[str]						=    ''.join(__UpperCAmelCase						)
     a   :	str						=    bytearray([self.byte_decoder[c] for c in text]						).decode('utf-8'  ,					errors=self.errors						)
     return text
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None						)				->					Tuple[str]:
     if not os.path.isdir(__UpperCAmelCase						):
        logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
        return
     a   :	Dict						=    os.path.join(
         __UpperCAmelCase  ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
     a   :	Dict						=    os.path.join(
         __UpperCAmelCase  ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']						)

     with open(__UpperCAmelCase  ,					'w'  ,					encoding='utf-8'						) as f:
        f.write(json.dumps(self.encoder  ,					indent=2  ,					sort_keys=__UpperCAmelCase  ,					ensure_ascii=__UpperCAmelCase						) + '\n'						)

     a   :	Any						=    0
     with open(__UpperCAmelCase  ,					'w'  ,					encoding='utf-8'						) as writer:
        writer.write('#version: 0.2\n'						)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,					key=lambda __UpperCAmelCase						: kv[1]						):
           if index != token_index:
              logger.warning(
                  f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                  ' Please check that the tokenizer is not corrupted!'						)
              a   :	str						=    token_index
           writer.write(' '.join(__UpperCAmelCase						) + '\n'						)
           index += 1

     return vocab_file, merge_file
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None						)				->					List[int]:
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     a   :	Optional[Any]						=    [self.cls_token_id]
     a   :	int						=    [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = False						)				->					List[int]:
     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__UpperCAmelCase  ,					token_ids_a=__UpperCAmelCase  ,					already_has_special_tokens=__UpperCAmelCase						)

     if token_ids_a is None:
        return [1] + ([0] * len(__UpperCAmelCase						)) + [1]
     return [1] + ([0] * len(__UpperCAmelCase						)) + [1, 1] + ([0] * len(__UpperCAmelCase						)) + [1]
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None						)				->					List[int]:
     a   :	Union[str, Any]						=    [self.sep_token_id]
     a   :	Any						=    [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep						) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=False  ,					**__UpperCAmelCase						)				->					Dict:
     a   :	Optional[int]						=    kwargs.pop('add_prefix_space'  ,					self.add_prefix_space						)
     if (is_split_into_words or add_prefix_space) and (len(__UpperCAmelCase						) > 0 and not text[0].isspace()):
        a   :	Union[str, Any]						=    ' ' + text
     return (text, kwargs)






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




    def     __lowerCamelCase			(       self	: List[Any] )						->			str:
      a__  		=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

      a__  		=  self.get_config()

      return config, pixel_values, labels




    def     __lowerCamelCase			(       self	: List[Any] )						->			Union[str, Any]:
      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   ,   )




    def     __lowerCamelCase			(       self	: Optional[int]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[Any]   ,   lowerCAmelCase	: Optional[Any] )						->			Optional[Any]:
      a__  		=  DPTModel(config=lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size) )




    def     __lowerCamelCase			(       self	: Union[str, Any]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Dict   ,   lowerCAmelCase	: str )						->			Optional[Any]:
      a__  		=  self.num_labels
      a__  		=  DPTForDepthEstimation(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()
      a__  		=  model(lowerCAmelCase )
      self.parent.assertEqual(result.predicted_depth.shape   ,   (self.batch_size, self.image_size, self.image_size) )




    def     __lowerCamelCase			(       self	: str   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Optional[int] )						->			List[str]:
      a__  		=  self.num_labels
      a__  		=  DPTForSemanticSegmentation(lowerCAmelCase )
      model.to(lowerCAmelCase )
      model.eval()
      a__  		=  model(lowerCAmelCase   ,   labels=lowerCAmelCase )
      self.parent.assertEqual(
          result.logits.shape   ,   (self.batch_size, self.num_labels, self.image_size, self.image_size) )




    def     __lowerCamelCase			(       self	: int )						->			Any:
      a__  		=  self.prepare_config_and_inputs()
      a__		,a__		,a__  		=  config_and_inputs
      a__  		=  {"pixel_values": pixel_values}
      return config, inputs_dict







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

    UpperCAmelCase_      :							List[str]    			=				False
    UpperCAmelCase_      :							int    			=				False
    UpperCAmelCase_      :							Optional[Any]    			=				False




    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__  		=  DPTModelTester(self )
      a__  		=  ConfigTester(self   ,   config_class=lowerCAmelCase   ,   has_text_modality=lowerCAmelCase   ,   hidden_size=37 )




    def     __lowerCamelCase			(       self	: int )						->			List[Any]:
      self.config_tester.run_common_tests()




    @unittest.skip(reason="DPT does not use inputs_embeds" )
    def     __lowerCamelCase			(       self	: str )						->			Dict:
      pass




    def     __lowerCamelCase			(       self	: List[str] )						->			int:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()

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




    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			Any:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()

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

        a__  		=  ["pixel_values"]
        self.assertListEqual(arg_names[:1]   ,   lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Optional[int] )						->			Optional[Any]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Tuple )						->			List[str]:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: List[Any] )						->			Any:
      a__  		=  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase )




    def     __lowerCamelCase			(       self	: Tuple )						->			Tuple:
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()
        a__  		=  True

        if model_class in get_values(lowerCAmelCase ):
          continue

        a__  		=  model_class(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.train()
        a__  		=  self._prepare_for_class(lowerCAmelCase   ,   lowerCAmelCase   ,   return_labels=lowerCAmelCase )
        a__  		=  model(**lowerCAmelCase ).loss
        loss.backward()




    def     __lowerCamelCase			(       self	: Any )						->			Dict:
      for model_class in self.all_model_classes:
        if model_class.__name__ == "DPTForDepthEstimation":
          continue

        a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()
        a__  		=  False
        a__  		=  True

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




    def     __lowerCamelCase			(       self	: List[str] )						->			int:
      a__		,a__  		=  self.model_tester.prepare_config_and_inputs_for_common()

      a__  		=  _config_zero_init(lowerCAmelCase )
      for model_class in self.all_model_classes:
        a__  		=  model_class(config=lowerCAmelCase )
        # Skip the check for the backbone
        a__  		=  []
        for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
            a__  		=  [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] )						->			Union[str, Any]:
      pass




    @slow
    def     __lowerCamelCase			(       self	: List[str] )						->			Dict:
      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a__  		=  DPTModel.from_pretrained(lowerCAmelCase )
        self.assertIsNotNone(lowerCAmelCase )

def 					__lowercase						(    )					->int:

  '''simple docstring'''




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







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




    def     __lowerCamelCase			(       self	: Any )						->			Union[str, Any]:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large" )
      a__  		=  DPTForDepthEstimation.from_pretrained("Intel/dpt-large" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

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

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

      a__  		=  torch.tensor(
          [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]] ).to(lowerCAmelCase )

      self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,   lowerCAmelCase   ,   atol=1e-4 ) )




    def     __lowerCamelCase			(       self	: Union[str, Any] )						->			str:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
      a__  		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

      # forward pass
      with torch.no_grad():
        a__  		=  model(**lowerCAmelCase )

      # verify the logits
      a__  		=  torch.Size((1, 150, 480, 480) )
      self.assertEqual(outputs.logits.shape   ,   lowerCAmelCase )

      a__  		=  torch.tensor(
          [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]] ).to(lowerCAmelCase )

      self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,   lowerCAmelCase   ,   atol=1e-4 ) )




    def     __lowerCamelCase			(       self	: str )						->			Optional[int]:
      a__  		=  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade" )
      a__  		=  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade" ).to(lowerCAmelCase )

      a__  		=  prepare_img()
      a__  		=  image_processor(images=lowerCAmelCase   ,   return_tensors="pt" ).to(lowerCAmelCase )

      # forward pass
      with torch.no_grad():
        a__  		=  model(**lowerCAmelCase )

      a__  		=  outputs.logits.detach().cpu()

      a__  		=  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase   ,   target_sizes=[(500, 300)] )
      a__  		=  torch.Size((500, 300) )
      self.assertEqual(segmentation[0].shape   ,   lowerCAmelCase )

      a__  		=  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase )
      a__  		=  torch.Size((480, 480) )
      self.assertEqual(segmentation[0].shape   ,   lowerCAmelCase )

