





"""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 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A=None						,   _A=True						,   _A=None						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			parent
									UpperCamelCase				:		List[str]				   =			config_class
									UpperCamelCase				:		str				   =			has_text_modality
									UpperCamelCase				:		Optional[Any]				   =			kwargs
									UpperCamelCase				:		Optional[Any]				   =			common_properties







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			self.config_class(**self.inputs_dict    )
									UpperCamelCase				:		int				   =			(
									    ["""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:
																					UpperCamelCase				:		Any				   =			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    ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			self.config_class(**self.inputs_dict    )

									with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCamelCase				:		Any				   =			os.path.join(_A						,   """config.json"""    )
															config_first.to_json_file(_A    )
															UpperCamelCase				:		Optional[int]				   =			self.config_class.from_json_file(_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			self.config_class(**self.inputs_dict    )

									with tempfile.TemporaryDirectory() as tmpdirname:
															config_first.save_pretrained(_A    )
															UpperCamelCase				:		Optional[int]				   =			self.config_class.from_pretrained(_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			self.config_class(**self.inputs_dict    )

									UpperCamelCase				:		int				   =			"""test"""
									with tempfile.TemporaryDirectory() as tmpdirname:
															UpperCamelCase				:		Union[str, Any]				   =			os.path.join(_A						,   _A    )
															config_first.save_pretrained(_A    )
															UpperCamelCase				:		str				   =			self.config_class.from_pretrained(_A						,   subfolder=_A    )

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







			def   _a      (      self    ):






									'''simple docstring'''







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

									UpperCamelCase				:		Tuple				   =			3
									self.parent.assertEqual(len(config.idalabel    )						,   3    )
									self.parent.assertEqual(len(config.labelaid    )						,   3    )







			def   _a      (      self    ):






									'''simple docstring'''







									if self.config_class.is_composition:
															return
									UpperCamelCase				:		str				   =			self.config_class()
									self.parent.assertIsNotNone(_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[int]				   =			copy.deepcopy(_A    )
									UpperCamelCase				:		Dict				   =			self.config_class(**_A    )
									UpperCamelCase				:		List[Any]				   =			[]
									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:
															UpperCamelCase				:		Dict				   =			"""\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    ):






									'''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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__magic_name__       :					Any						=			logging.get_logger(__name__)

__magic_name__       :					int						=			{
    """facebook/convnextv2-tiny-1k-224""": """https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json""",
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				str										=  """convnextv2"""







			def __init__(      self						,   _A=3						,   _A=4						,   _A=4						,   _A=None						,   _A=None						,   _A="gelu"						,   _A=0.02						,   _A=1e-1_2						,   _A=0.0						,   _A=2_2_4						,   _A=None						,   _A=None						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

									UpperCamelCase				:		Optional[Any]				   =			num_channels
									UpperCamelCase				:		int				   =			patch_size
									UpperCamelCase				:		Dict				   =			num_stages
									UpperCamelCase				:		Tuple				   =			[9_6, 1_9_2, 3_8_4, 7_6_8] if hidden_sizes is None else hidden_sizes
									UpperCamelCase				:		str				   =			[3, 3, 9, 3] if depths is None else depths
									UpperCamelCase				:		List[str]				   =			hidden_act
									UpperCamelCase				:		List[str]				   =			initializer_range
									UpperCamelCase				:		List[Any]				   =			layer_norm_eps
									UpperCamelCase				:		Any				   =			drop_path_rate
									UpperCamelCase				:		Any				   =			image_size
									UpperCamelCase				:		Union[str, Any]				   =			["""stem"""] + [f"""stage{idx}""" for idx in range(1						,   len(self.depths    ) + 1    )]
									UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			get_aligned_output_features_output_indices(
									    out_features=_A						,   out_indices=_A						,   stage_names=self.stage_names    )



