



'''simple docstring'''





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A ,   A=None ,   A=True ,   A=None ,   **A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  parent
      _a      =  config_class
      _a      =  has_text_modality
      _a      =  kwargs
      _a      =  common_properties



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.config_class(**self.inputs_dict       )
      _a      =  (
          ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
          if self.common_properties is None
          else self.common_properties
      )

      # Add common fields for text models
      if self.has_text_modality:
           common_properties.extend(['''vocab_size''']       )

      # Test that config has the common properties as getters
      for prop in common_properties:
           self.parent.assertTrue(hasattr(A ,   A       ) ,   msg=f'''`{prop}` does not exist'''       )

      # Test that config has the common properties as setter
      for idx, name in enumerate(A       ):
           try:
                setattr(A ,   A ,   A       )
                self.parent.assertEqual(
                    getattr(A ,   A       ) ,   A ,   msg=f'''`{name} value {idx} expected, but was {getattr(A ,   A       )}'''       )
           except NotImplementedError:
                # Some models might not be able to implement setters for common_properties
                # In that case, a NotImplementedError is raised
                pass

        # Test if config class can be called with Config(prop_name=..)
      for idx, name in enumerate(A       ):
           try:
                _a      =  self.config_class(**{name: idx}       )
                self.parent.assertEqual(
                    getattr(A ,   A       ) ,   A ,   msg=f'''`{name} value {idx} expected, but was {getattr(A ,   A       )}'''       )
           except NotImplementedError:
                # Some models might not be able to implement setters for common_properties
                # In that case, a NotImplementedError is raised
                pass



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  self.config_class(**self.inputs_dict       )
      _a      =  json.loads(config.to_json_string()       )
      for key, value in self.inputs_dict.items():
           self.parent.assertEqual(obj[key] ,   A       )



 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
           _a      =  os.path.join(A ,   '''config.json'''       )
           config_first.to_json_file(A       )
           _a      =  self.config_class.from_json_file(A       )

      self.parent.assertEqual(config_second.to_dict() ,   config_first.to_dict()       )



 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      _a      =  self.config_class(**self.inputs_dict       )

      with tempfile.TemporaryDirectory() as tmpdirname:
           config_first.save_pretrained(A       )
           _a      =  self.config_class.from_pretrained(A       )

      self.parent.assertEqual(config_second.to_dict() ,   config_first.to_dict()       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.config_class(**self.inputs_dict       )

      _a      =  '''test'''
      with tempfile.TemporaryDirectory() as tmpdirname:
           _a      =  os.path.join(A ,   A       )
           config_first.save_pretrained(A       )
           _a      =  self.config_class.from_pretrained(A ,   subfolder=A       )

      self.parent.assertEqual(config_second.to_dict() ,   config_first.to_dict()       )



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  self.config_class(**self.inputs_dict ,   num_labels=5       )
      self.parent.assertEqual(len(config.idalabel       ) ,   5       )
      self.parent.assertEqual(len(config.labelaid       ) ,   5       )

      _a      =  3
      self.parent.assertEqual(len(config.idalabel       ) ,   3       )
      self.parent.assertEqual(len(config.labelaid       ) ,   3       )



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      if self.config_class.is_composition:
           return
      _a      =  self.config_class()
      self.parent.assertIsNotNone(A       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  copy.deepcopy(A       )
      _a      =  self.config_class(**A       )
      _a      =  []
      for key, value in config_common_kwargs.items():
           if key == "torch_dtype":
                if not is_torch_available():
                     continue
                else:
                     import torch

                     if config.torch_dtype != torch.floataa:
                          wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)       )
           elif getattr(A ,   A       ) != value:
                wrong_values.append((key, getattr(A ,   A       ), value)       )

      if len(A       ) > 0:
           _a      =  '''\n'''.join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]       )
           raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''       )



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      self.create_and_test_config_common_properties()
      self.create_and_test_config_to_json_string()
      self.create_and_test_config_to_json_file()
      self.create_and_test_config_from_and_save_pretrained()
      self.create_and_test_config_from_and_save_pretrained_subfolder()
      self.create_and_test_config_with_num_labels()
      self.check_config_can_be_init_without_params()
      self.check_config_arguments_init()








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       'glpn'



 def __init__(self ,   A=3 ,   A=4 ,   A=[2, 2, 2, 2] ,   A=[8, 4, 2, 1] ,   A=[32, 64, 160, 256] ,   A=[7, 3, 3, 3] ,   A=[4, 2, 2, 2] ,   A=[1, 2, 5, 8] ,   A=[4, 4, 4, 4] ,   A="gelu" ,   A=0.0 ,   A=0.0 ,   A=0.02 ,   A=0.1 ,   A=1E-6 ,   A=64 ,   A=10 ,   A=-1 ,   **A ,   )    ->     Any:


      """simple docstring"""



      super().__init__(**A       )

      _a      =  num_channels
      _a      =  num_encoder_blocks
      _a      =  depths
      _a      =  sr_ratios
      _a      =  hidden_sizes
      _a      =  patch_sizes
      _a      =  strides
      _a      =  mlp_ratios
      _a      =  num_attention_heads
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  initializer_range
      _a      =  drop_path_rate
      _a      =  layer_norm_eps
      _a      =  decoder_hidden_size
      _a      =  max_depth
      _a      =  head_in_index



