

import re





def       __lowerCamelCase			(				UpperCamelCase__   ):







       '''simple docstring'''






       snake_case_           =					re.compile(
           r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$'   )

       return bool(re.search(UpperCamelCase__     ,							UpperCamelCase__   )   )


if __name__ == "__main__":
     _UpperCAmelCase	:     Optional[Any]		       = """0094702343221"""

     print(is_sri_lankan_phone_number(phone))







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCAmelCase	:     Dict		       = logging.get_logger(__name__)

_UpperCAmelCase	:     Dict		       = {
    """salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''blip_2_vision_model'''




				def __init__(      self   ,     snake_case=1408   ,     snake_case=6144   ,     snake_case=39   ,     snake_case=16   ,     snake_case=224   ,     snake_case=14   ,     snake_case="gelu"   ,     snake_case=0.0_00_01   ,     snake_case=0.0   ,     snake_case=1e-1_0   ,     snake_case=True   ,     **snake_case   ,     ):
											super().__init__(**snake_case )

											snake_case_           =					hidden_size
											snake_case_           =					intermediate_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					patch_size
											snake_case_           =					image_size
											snake_case_           =					initializer_range
											snake_case_           =					attention_dropout
											snake_case_           =					layer_norm_eps
											snake_case_           =					hidden_act
											snake_case_           =					qkv_bias




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     **snake_case ):
											cls._set_token_in_kwargs(snake_case )

											snake_case_       ,					snake_case_           =					cls.get_config_dict(snake_case   ,     **snake_case )

											# get the vision config dict if we are loading from Blip2Config
											if config_dict.get('model_type' ) == "blip-2":
																		snake_case_           =					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(snake_case   ,     **snake_case )







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Any     =    '''blip_2_qformer'''




				def __init__(      self   ,     snake_case=3_0522   ,     snake_case=768   ,     snake_case=12   ,     snake_case=12   ,     snake_case=3072   ,     snake_case="gelu"   ,     snake_case=0.1   ,     snake_case=0.1   ,     snake_case=512   ,     snake_case=0.02   ,     snake_case=1e-1_2   ,     snake_case=0   ,     snake_case="absolute"   ,     snake_case=2   ,     snake_case=1408   ,     **snake_case   ,     ):
											super().__init__(pad_token_id=snake_case   ,     **snake_case )

											snake_case_           =					vocab_size
											snake_case_           =					hidden_size
											snake_case_           =					num_hidden_layers
											snake_case_           =					num_attention_heads
											snake_case_           =					hidden_act
											snake_case_           =					intermediate_size
											snake_case_           =					hidden_dropout_prob
											snake_case_           =					attention_probs_dropout_prob
											snake_case_           =					max_position_embeddings
											snake_case_           =					initializer_range
											snake_case_           =					layer_norm_eps
											snake_case_           =					position_embedding_type
											snake_case_           =					cross_attention_frequency
											snake_case_           =					encoder_hidden_size




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     **snake_case ):
											cls._set_token_in_kwargs(snake_case )

											snake_case_       ,					snake_case_           =					cls.get_config_dict(snake_case   ,     **snake_case )

											# get the qformer config dict if we are loading from Blip2Config
											if config_dict.get('model_type' ) == "blip-2":
																		snake_case_           =					config_dict['qformer_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(snake_case   ,     **snake_case )







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''blip-2'''
				__SCREAMING_SNAKE_CASE      :				Dict     =    True




				def __init__(      self   ,     snake_case=None   ,     snake_case=None   ,     snake_case=None   ,     snake_case=32   ,     **snake_case ):
											super().__init__(**snake_case )

											if vision_config is None:
																		snake_case_           =					{}
																		logger.info('vision_config is None. initializing the Blip2VisionConfig with default values.' )

											if qformer_config is None:
																		snake_case_           =					{}
																		logger.info('qformer_config is None. Initializing the Blip2QFormerConfig with default values.' )

											if text_config is None:
																		snake_case_           =					{}
																		logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )

											snake_case_           =					BlipaVisionConfig(**snake_case )
											snake_case_           =					BlipaQFormerConfig(**snake_case )
											snake_case_           =					text_config['model_type'] if 'model_type' in text_config else 'opt'
											snake_case_           =					CONFIG_MAPPING[text_model_type](**snake_case )

											snake_case_           =					self.text_config.tie_word_embeddings
											snake_case_           =					self.text_config.is_encoder_decoder

											snake_case_           =					num_query_tokens
											snake_case_           =					self.vision_config.hidden_size
											snake_case_           =					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
											snake_case_           =					1.0
											snake_case_           =					0.02




				@classmethod
				def 					a							(      cls   ,     snake_case   ,     snake_case   ,     snake_case   ,     **snake_case   ,     ):
											return cls(
											    vision_config=vision_config.to_dict()   ,     qformer_config=qformer_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **snake_case   ,     )




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					self.vision_config.to_dict()
											snake_case_           =					self.qformer_config.to_dict()
											snake_case_           =					self.text_config.to_dict()
											snake_case_           =					self.__class__.model_type
											return output




