
"""simple docstring"""





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


_a								=						logging.get_logger(__name__)

_a								=						{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'instructblip_vision_model'



		def __init__(   self							,						__a=14_08							,						__a=61_44							,						__a=39							,						__a=16							,						__a=2_24							,						__a=14							,						__a="gelu"							,						__a=1e-6							,						__a=0.0							,						__a=1e-10							,						__a=True							,						**__a							,						)			->			int:






							'''simple docstring'''
							super().__init__(**__a)

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




		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						**__a)			->			"PretrainedConfig":






							'''simple docstring'''
							cls._set_token_in_kwargs(__a)

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

							# get the vision config dict if we are loading from InstructBlipConfig
							if config_dict.get('''model_type''') == "instructblip":
												_UpperCamelCase									=    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(__a							,						**__a)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'instructblip_qformer'



		def __init__(   self							,						__a=3_05_22							,						__a=7_68							,						__a=12							,						__a=12							,						__a=30_72							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_12							,						__a=0.02							,						__a=1e-12							,						__a=0							,						__a="absolute"							,						__a=2							,						__a=14_08							,						**__a							,						)			->			Dict:






							'''simple docstring'''
							super().__init__(pad_token_id=__a							,						**__a)

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




		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						**__a)			->			"PretrainedConfig":






							'''simple docstring'''
							cls._set_token_in_kwargs(__a)

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

							# get the qformer config dict if we are loading from InstructBlipConfig
							if config_dict.get('''model_type''') == "instructblip":
												_UpperCamelCase									=    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(__a							,						**__a)






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'instructblip'
		lowercase__         = True



		def __init__(   self							,						__a=None							,						__a=None							,						__a=None							,						__a=32							,						**__a)			->			List[str]:






							'''simple docstring'''
							super().__init__(**__a)

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

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

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

							_UpperCamelCase									=    InstructBlipVisionConfig(**__a)
							_UpperCamelCase									=    InstructBlipQFormerConfig(**__a)
							_UpperCamelCase									=    text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
							_UpperCamelCase									=    CONFIG_MAPPING[text_model_type](**__a)

							_UpperCamelCase									=    self.text_config.tie_word_embeddings
							_UpperCamelCase									=    self.text_config.is_encoder_decoder

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



		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						__a							,						__a							,						**__a							,						)			->			int:






							'''simple docstring'''
							return cls(
							    vision_config=vision_config.to_dict()							,						qformer_config=qformer_config.to_dict()							,						text_config=text_config.to_dict()							,						**__a							,						)




		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    copy.deepcopy(self.__dict__)
							_UpperCamelCase									=    self.vision_config.to_dict()
							_UpperCamelCase									=    self.qformer_config.to_dict()
							_UpperCamelCase									=    self.text_config.to_dict()
							_UpperCamelCase									=    self.__class__.model_type
							return output








"""simple docstring"""





import fire

from utils import calculate_rouge, save_json



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case=None,	**__snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()]
					_UpperCamelCase									=    [x.strip() for x in open(__snake_case      ).readlines()][: len(__snake_case      )]
					_UpperCamelCase									=    calculate_rouge(__snake_case,	__snake_case,	**__snake_case      )
					if save_path is not None:
										save_json(__snake_case,	__snake_case,	indent=__snake_case      )
					return metrics  # these print nicely


if __name__ == "__main__":
				fire.Fire(calculate_rouge_path)






