

'''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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  Optional[int]=2      ,lowercase__     :  Optional[Any]=3_2      ,lowercase__     :  Optional[int]=1_6      ,lowercase__     :  Optional[Any]=3      ,lowercase__     :  List[Any]=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Union[str, Any]=3_2      ,lowercase__     :  Optional[int]=4      ,lowercase__     :  List[str]=[0, 1, 2, 3]      ,lowercase__     :  List[str]=4      ,lowercase__     :  List[str]=3_7      ,lowercase__     :  List[Any]="gelu"      ,lowercase__     :  Optional[Any]=0.1      ,lowercase__     :  Any=0.1      ,lowercase__     :  int=0.0_2      ,lowercase__     :  List[Any]=3      ,lowercase__     :  Optional[int]=[1, 3_8_4, 2_4, 2_4]      ,lowercase__     :  Optional[int]=True      ,lowercase__     :  Tuple=None      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				image_size
   __lowercase													=				patch_size
   __lowercase													=				num_channels
   __lowercase													=				is_training
   __lowercase													=				use_labels
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				backbone_out_indices
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				initializer_range
   __lowercase													=				num_labels
   __lowercase													=				backbone_featmap_shape
   __lowercase													=				scope
   __lowercase													=				is_hybrid
   # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
   __lowercase													=				(image_size // patch_size) ** 2
   __lowercase													=				num_patches + 1






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

   __lowercase													=				self.get_config()

   return config, pixel_values, labels






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				{
       '''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=lowercase__      ,initializer_range=self.initializer_range      ,is_hybrid=self.is_hybrid      ,backbone_config=lowercase__      ,backbone_featmap_shape=self.backbone_featmap_shape      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict    ):
   __lowercase													=				DPTModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Dict      ,lowercase__     :  List[Any]      ,lowercase__     :  str    ):
   __lowercase													=				self.num_labels
   __lowercase													=				DPTForDepthEstimation(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.predicted_depth.shape      ,(self.batch_size, self.image_size, self.image_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  str      ,lowercase__     :  str      ,lowercase__     :  int    ):
   __lowercase													=				self.num_labels
   __lowercase													=				DPTForSemanticSegmentation(lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(
       result.logits.shape      ,(self.batch_size, self.num_labels, self.image_size, self.image_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.prepare_config_and_inputs()
   __lowercase						,			__lowercase						,			__lowercase													=				config_and_inputs
   __lowercase													=				{'''pixel_values''': pixel_values}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[str]           =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE					:						Dict           =    (
      {
          'depth-estimation': DPTForDepthEstimation,
          'feature-extraction': DPTModel,
          'image-segmentation': DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE					:						str           =    False
  SCREAMING_SNAKE_CASE					:						int           =    False
  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				DPTModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,has_text_modality=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   self.config_tester.run_common_tests()






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






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

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






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

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

    __lowercase													=				['''pixel_values''']
    self.assertListEqual(arg_names[:1]      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_depth_estimation(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for model_class in self.all_model_classes:
    if model_class.__name__ == "DPTForDepthEstimation":
     continue

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

    if model_class in get_values(lowercase__    ):
     continue

    __lowercase													=				model_class(lowercase__    )
    model.to(lowercase__    )
    model.train()
    __lowercase													=				self._prepare_for_class(lowercase__      ,lowercase__      ,return_labels=lowercase__    )
    __lowercase													=				model(**lowercase__    ).loss
    loss.backward()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   for model_class in self.all_model_classes:
    if model_class.__name__ == "DPTForDepthEstimation":
     continue

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

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()

   __lowercase													=				_config_zero_init(lowercase__    )
   for model_class in self.all_model_classes:
    __lowercase													=				model_class(config=lowercase__    )
    # Skip the check for the backbone
    __lowercase													=				[]
    for name, module in model.named_modules():
     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
      __lowercase													=				[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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   pass






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
    __lowercase													=				DPTModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
   __lowercase						,			__lowercase													=				self.model_tester.prepare_config_and_inputs_for_common()
   __lowercase													=				'''add'''
   with self.assertRaises(lowercase__    ):
    __lowercase													=				DPTForDepthEstimation(lowercase__    )


def 	_A  (					):






 """simple docstring"""



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






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




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''    )
   __lowercase													=				DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''    ).to(lowercase__    )

   __lowercase													=				prepare_img()
   __lowercase													=				image_processor(images=lowercase__      ,return_tensors='''pt'''    ).to(lowercase__    )

   # forward pass
   with torch.no_grad():
    __lowercase													=				model(**lowercase__    )
    __lowercase													=				outputs.predicted_depth

   # verify the predicted depth
   __lowercase													=				torch.Size((1, 3_8_4, 3_8_4)    )
   self.assertEqual(predicted_depth.shape      ,lowercase__    )

   __lowercase													=				torch.tensor(
       [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]    ).to(lowercase__    )

   self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0      ,lowercase__      ,atol=1e-4    )    )




'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			{'''vocab_file''': '''sentencepiece.model'''}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

lowerCAmelCase__      		=			{
    '''google/rembert''': 256,
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(   self     :  str      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]=False      ,lowercase__     :  Dict=True      ,lowercase__     :  List[str]=True      ,lowercase__     :  Dict="[CLS]"      ,lowercase__     :  Union[str, Any]="[SEP]"      ,lowercase__     :  List[str]="[UNK]"      ,lowercase__     :  int="[SEP]"      ,lowercase__     :  List[str]="[PAD]"      ,lowercase__     :  Optional[int]="[CLS]"      ,lowercase__     :  List[Any]="[MASK]"      ,**lowercase__     :  int      ,):
   super().__init__(
       do_lower_case=lowercase__      ,remove_space=lowercase__      ,keep_accents=lowercase__      ,bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,sep_token=lowercase__      ,pad_token=lowercase__      ,cls_token=lowercase__      ,mask_token=lowercase__      ,**lowercase__      ,)

   __lowercase													=				do_lower_case
   __lowercase													=				remove_space
   __lowercase													=				keep_accents
   __lowercase													=				vocab_file

   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(lowercase__    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   return len(self.sp_model    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				{self.convert_ids_to_tokens(lowercase__    ): i for i in range(self.vocab_size    )}
   vocab.update(self.added_tokens_encoder    )
   return vocab






  def __getstate__(   self     :  List[Any]    ):
   __lowercase													=				self.__dict__.copy()
   __lowercase													=				None
   return state






  def __setstate__(   self     :  str      ,lowercase__     :  Optional[int]    ):
   __lowercase													=				d
   __lowercase													=				spm.SentencePieceProcessor()
   self.sp_model.Load(self.vocab_file    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]=False    ):
   __lowercase													=				self.sp_model.EncodeAsPieces(lowercase__    )
   return pieces






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]    ):
   return self.sp_model.PieceToId(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  str    ):
   return self.sp_model.IdToPiece(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Tuple    ):
   __lowercase													=				self.sp_model.decode_pieces(lowercase__    )
   return out_string






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None      ,lowercase__     :  bool = False    ):

   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         '''You should not supply a second sequence if the provided sequence of '''
         '''ids is already formatted with special tokens for the model.'''    )
    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

   if token_ids_a is not None:
    return [1] + ([0] * len(lowercase__    )) + [1] + ([0] * len(lowercase__    )) + [1]
   return [1] + ([0] * len(lowercase__    )) + [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[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    ) * [0] + len(token_ids_a + sep    ) * [1]






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   if not os.path.isdir(lowercase__    ):
    logger.error('''Vocabulary path ({}) should be a directory'''.format(lowercase__    )    )
    return
   __lowercase													=				os.path.join(
       lowercase__      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

   if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowercase__    ):
    copyfile(self.vocab_file      ,lowercase__    )

   return (out_vocab_file,)

