

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		:       Tuple						    =	logging.get_logger(__name__)

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







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      '''instructblip_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=1e-6				,       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 InstructBlipConfig
											if config_dict.get('model_type'	) == "instructblip":
																		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 :			Tuple	      =      '''instructblip_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 InstructBlipConfig
											if config_dict.get('model_type'	) == "instructblip":
																		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 :			Tuple	      =      '''instructblip'''
				__SCREAMING_SNAKE_CASE :			Optional[Any]	      =      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 InstructBlipVisionConfig with default values.'	)

											if qformer_config is None:
																		snake_case_          =   {}
																		logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig 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_          =   InstructBlipVisionConfig(**snake_case	)
											snake_case_          =   InstructBlipQFormerConfig(**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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase		:       int						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Optional[Any]						    =	{
    """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
    """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
    """junnyu/roformer_chinese_char_small""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
    ),
    """junnyu/roformer_chinese_char_base""": (
        """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_discriminator""": (
        """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
    ),
    """junnyu/roformer_small_generator""": (
        """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      '''roformer'''




				def __init__( self				,       snake_case=5_0000				,       snake_case=None				,       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=1536				,       snake_case=2				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=0				,       snake_case=False				,       snake_case=True				,       **snake_case				,       ):
											super().__init__(pad_token_id=snake_case				,       **snake_case	)

											snake_case_          =   vocab_size
											snake_case_          =   hidden_size if embedding_size is None else embedding_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_          =   type_vocab_size
											snake_case_          =   initializer_range
											snake_case_          =   layer_norm_eps
											snake_case_          =   rotary_value
											snake_case_          =   use_cache







class 			lowercase    (	lowercase_      ):




				@property
				def    a							( self	):
											if self.task == "multiple-choice":
																		snake_case_          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		snake_case_          =   {0: 'batch', 1: 'sequence'}
											snake_case_          =   {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											        ('token_type_ids', dynamic_axis),
											    ]	)




