import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class    lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	def __init__(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE=None		,			__SCREAMING_SNAKE_CASE=True		,			__SCREAMING_SNAKE_CASE=None		,			**__SCREAMING_SNAKE_CASE				)						->Optional[Any]:
								lowerCAmelCase			=		parent
								lowerCAmelCase			=		config_class
								lowerCAmelCase			=		has_text_modality
								lowerCAmelCase			=		kwargs
								lowerCAmelCase			=		common_properties


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Dict:
								lowerCAmelCase			=		self.config_class(**self.inputs_dict				)
								lowerCAmelCase			=		(
								    ['''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(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				)		,			msg=F"`{prop}` does not exist"				)

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


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Optional[Any]:
								lowerCAmelCase			=		self.config_class(**self.inputs_dict				)
								lowerCAmelCase			=		json.loads(config.to_json_string()				)
								for key, value in self.inputs_dict.items():
															self.parent.assertEqual(obj[key]		,			__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Any:
								lowerCAmelCase			=		self.config_class(**self.inputs_dict				)

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

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


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[str]:
								lowerCAmelCase			=		self.config_class(**self.inputs_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
															config_first.save_pretrained(__SCREAMING_SNAKE_CASE				)
															lowerCAmelCase			=		self.config_class.from_pretrained(__SCREAMING_SNAKE_CASE				)

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


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->str:
								lowerCAmelCase			=		self.config_class(**self.inputs_dict				)

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

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


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								lowerCAmelCase			=		self.config_class(**self.inputs_dict		,			num_labels=5				)
								self.parent.assertEqual(len(config.idalabel				)		,			5				)
								self.parent.assertEqual(len(config.labelaid				)		,			5				)

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


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



								if self.config_class.is_composition:
															return
								lowerCAmelCase			=		self.config_class()
								self.parent.assertIsNotNone(__SCREAMING_SNAKE_CASE				)


	def 			SCREAMING_SNAKE_CASE_				(							self				)						->Tuple:
								lowerCAmelCase			=		copy.deepcopy(__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		self.config_class(**__SCREAMING_SNAKE_CASE				)
								lowerCAmelCase			=		[]
								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(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				) != value:
																						wrong_values.append((key, getattr(__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE				), value)				)

								if len(__SCREAMING_SNAKE_CASE				) > 0:
															lowerCAmelCase			=		'''\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 			SCREAMING_SNAKE_CASE_				(							self				)						->List[Any]:
								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



