import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
	pass

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


lowerCamelCase_    :		Dict							=	logging.get_logger(__name__)

lowerCamelCase_    :		int							=	{
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}


class 						a__							(       __snake_case		):
			A__    :       Any        =      'align_text_model'



			def __init__(						self				,						UpperCAmelCase=3_0_5_2_2				,						UpperCAmelCase=7_6_8				,						UpperCAmelCase=1_2				,						UpperCAmelCase=1_2				,						UpperCAmelCase=3_0_7_2				,						UpperCAmelCase="gelu"				,						UpperCAmelCase=0.1				,						UpperCAmelCase=0.1				,						UpperCAmelCase=5_1_2				,						UpperCAmelCase=2				,						UpperCAmelCase=0.02				,						UpperCAmelCase=1e-12				,						UpperCAmelCase=0				,						UpperCAmelCase="absolute"				,						UpperCAmelCase=True				,						**UpperCAmelCase				,						)			->   Dict:
								super().__init__(**UpperCAmelCase   )

								__a		 =       vocab_size
								__a		 =       hidden_size
								__a		 =       num_hidden_layers
								__a		 =       num_attention_heads
								__a		 =       hidden_act
								__a		 =       intermediate_size
								__a		 =       hidden_dropout_prob
								__a		 =       attention_probs_dropout_prob
								__a		 =       max_position_embeddings
								__a		 =       type_vocab_size
								__a		 =       initializer_range
								__a		 =       layer_norm_eps
								__a		 =       position_embedding_type
								__a		 =       use_cache
								__a		 =       pad_token_id







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   "PretrainedConfig":
								cls._set_token_in_kwargs(UpperCAmelCase   )

								__a ,				__a		 =       cls.get_config_dict(UpperCAmelCase				,						**UpperCAmelCase   )

								# get the text config dict if we are loading from AlignConfig
								if config_dict.get('model_type'   ) == "align":
													__a		 =       config_dict['text_config']

								if "model_type" in config_dict and hasattr(cls				,						'model_type'   ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
													    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

								return cls.from_dict(UpperCAmelCase				,						**UpperCAmelCase   )






class 						a__							(       __snake_case		):
			A__    :       Tuple        =      'align_vision_model'



			def __init__(						self				,						UpperCAmelCase = 3				,						UpperCAmelCase = 6_0_0				,						UpperCAmelCase = 2.0				,						UpperCAmelCase = 3.1				,						UpperCAmelCase = 8				,						UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]				,						UpperCAmelCase = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]				,						UpperCAmelCase = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]				,						UpperCAmelCase = []				,						UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]				,						UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]				,						UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]				,						UpperCAmelCase = 0.25				,						UpperCAmelCase = "swish"				,						UpperCAmelCase = 2_5_6_0				,						UpperCAmelCase = "mean"				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 0.001				,						UpperCAmelCase = 0.99				,						UpperCAmelCase = 0.2				,						**UpperCAmelCase				,						)			->   Optional[int]:
								super().__init__(**UpperCAmelCase   )

								__a		 =       num_channels
								__a		 =       image_size
								__a		 =       width_coefficient
								__a		 =       depth_coefficient
								__a		 =       depth_divisor
								__a		 =       kernel_sizes
								__a		 =       in_channels
								__a		 =       out_channels
								__a		 =       depthwise_padding
								__a		 =       strides
								__a		 =       num_block_repeats
								__a		 =       expand_ratios
								__a		 =       squeeze_expansion_ratio
								__a		 =       hidden_act
								__a		 =       hidden_dim
								__a		 =       pooling_type
								__a		 =       initializer_range
								__a		 =       batch_norm_eps
								__a		 =       batch_norm_momentum
								__a		 =       drop_connect_rate
								__a		 =       sum(UpperCAmelCase   ) * 4







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   "PretrainedConfig":
								cls._set_token_in_kwargs(UpperCAmelCase   )

								__a ,				__a		 =       cls.get_config_dict(UpperCAmelCase				,						**UpperCAmelCase   )

								# get the vision config dict if we are loading from AlignConfig
								if config_dict.get('model_type'   ) == "align":
													__a		 =       config_dict['vision_config']

								if "model_type" in config_dict and hasattr(cls				,						'model_type'   ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
													    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

								return cls.from_dict(UpperCAmelCase				,						**UpperCAmelCase   )






class 						a__							(       __snake_case		):
			A__    :       List[Any]        =      'align'
			A__    :       Optional[Any]        =      True



			def __init__(						self				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=6_4_0				,						UpperCAmelCase=1.0				,						UpperCAmelCase=0.02				,						**UpperCAmelCase				,						)			->   str:
								super().__init__(**UpperCAmelCase   )

								if text_config is None:
													__a		 =       {}
													logger.info('text_config is None. Initializing the AlignTextConfig with default values.'   )

								if vision_config is None:
													__a		 =       {}
													logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'   )

								__a		 =       AlignTextConfig(**UpperCAmelCase   )
								__a		 =       AlignVisionConfig(**UpperCAmelCase   )

								__a		 =       projection_dim
								__a		 =       temperature_init_value
								__a		 =       initializer_range



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								return cls(text_config=text_config.to_dict()				,						vision_config=vision_config.to_dict()				,						**UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       copy.deepcopy(self.__dict__   )
								__a		 =       self.text_config.to_dict()
								__a		 =       self.vision_config.to_dict()
								__a		 =       self.__class__.model_type
								return output


from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




