


def   _lowercase(					__a				:						Optional[Any]     ):
							if not head:
														return True
							# split the list to two parts
							a__    ,     a__      =head.next, head
							while fast and fast.next:
														a__      =fast.next.next
														a__      =slow.next
							a__      =slow.next
							a__      =None  # Don't forget here! But forget still works!
							# reverse the second part
							a__      =None
							while second:
														a__      =second.next
														a__      =node
														a__      =second
														a__      =nxt
							# compare two parts
							# second part has the same or one less node
							while node:
														if node.val != head.val:
																					return False
														a__      =node.next
														a__      =head.next
							return True






def   _lowercase(					__a				:						Dict     ):
							if not head or not head.next:
														return True

							# 1. Get the midpoint (slow)
							a__      =a__      =a__      =head
							while fast and fast.next:
														a__    ,     a__      =fast.next.next, slow.next

							# 2. Push the second half into the stack
							a__      =[slow.val]
							while slow.next:
														a__      =slow.next
														stack.append(slow.val     )

							# 3. Comparison
							while stack:
														if stack.pop() != cur.val:
																					return False
														a__      =cur.next

							return True







def   _lowercase(					__a				:						Optional[int]     ):
							if not head or not head.next:
														return True
							a__      ={}
							a__      =0
							while head:
														if head.val in d:
																					d[head.val].append(__a     )
														else:
																					a__      =[pos]
														a__      =head.next
														pos += 1
							a__      =pos - 1
							a__      =0
							for v in d.values():
														if len(__a     ) % 2 != 0:
																					middle += 1
														else:
																					a__      =0
																					for i in range(0    ,     len(__a     )     ):
																												if v[i] + v[len(__a     ) - 1 - step] != checksum:
																																			return False
																												step += 1
														if middle > 1:
																					return False
							return True



"""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


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

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





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_vision_model'''



     def __init__(							self :int      ,  _lowerCamelCase :Optional[int]=1_408      ,  _lowerCamelCase :int=6_144      ,  _lowerCamelCase :str=39      ,  _lowerCamelCase :Tuple=16      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :int=14      ,  _lowerCamelCase :int="gelu"      ,  _lowerCamelCase :Optional[Any]=1E-6      ,  _lowerCamelCase :str=0.0      ,  _lowerCamelCase :Optional[Any]=1E-1_0      ,  _lowerCamelCase :str=True      ,  **_lowerCamelCase :Optional[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Dict   	=hidden_size
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[int]   	=num_hidden_layers
      UpperCamelCase_     :					Union[str, Any]   	=num_attention_heads
      UpperCamelCase_     :					Optional[Any]   	=patch_size
      UpperCamelCase_     :					List[Any]   	=image_size
      UpperCamelCase_     :					Dict   	=initializer_range
      UpperCamelCase_     :					List[Any]   	=attention_dropout
      UpperCamelCase_     :					List[str]   	=layer_norm_eps
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=qkv_bias




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

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





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_qformer'''



     def __init__(							self :Dict      ,  _lowerCamelCase :List[Any]=30_522      ,  _lowerCamelCase :Optional[int]=768      ,  _lowerCamelCase :Tuple=12      ,  _lowerCamelCase :List[str]=12      ,  _lowerCamelCase :int=3_072      ,  _lowerCamelCase :Tuple="gelu"      ,  _lowerCamelCase :Dict=0.1      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Optional[int]=512      ,  _lowerCamelCase :List[Any]=0.02      ,  _lowerCamelCase :int=1E-1_2      ,  _lowerCamelCase :str=0      ,  _lowerCamelCase :Dict="absolute"      ,  _lowerCamelCase :Optional[int]=2      ,  _lowerCamelCase :Union[str, Any]=1_408      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(pad_token_id=_lowerCamelCase      ,  **_lowerCamelCase   )

      UpperCamelCase_     :					Optional[Any]   	=vocab_size
      UpperCamelCase_     :					Tuple   	=hidden_size
      UpperCamelCase_     :					Union[str, Any]   	=num_hidden_layers
      UpperCamelCase_     :					List[str]   	=num_attention_heads
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[Any]   	=hidden_dropout_prob
      UpperCamelCase_     :					List[str]   	=attention_probs_dropout_prob
      UpperCamelCase_     :					List[str]   	=max_position_embeddings
      UpperCamelCase_     :					List[str]   	=initializer_range
      UpperCamelCase_     :					str   	=layer_norm_eps
      UpperCamelCase_     :					int   	=position_embedding_type
      UpperCamelCase_     :					Tuple   	=cross_attention_frequency
      UpperCamelCase_     :					List[Any]   	=encoder_hidden_size




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					List[Any]   	=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(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip'''
     UpperCAmelCase__         =      True



     def __init__(							self :str      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Union[str, Any]=32      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      if vision_config is None:
       UpperCamelCase_     :					Optional[int]   	={}
       logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'   )

      if qformer_config is None:
       UpperCamelCase_     :					Union[str, Any]   	={}
       logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'   )

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

      UpperCamelCase_     :					Any   	=InstructBlipVisionConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=InstructBlipQFormerConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					str   	=text_config['model_type'] if 'model_type' in text_config else 'opt'
      UpperCamelCase_     :					int   	=CONFIG_MAPPING[text_model_type](**_lowerCamelCase   )

      UpperCamelCase_     :					Tuple   	=self.text_config.tie_word_embeddings
      UpperCamelCase_     :					Any   	=self.text_config.is_encoder_decoder

      UpperCamelCase_     :					List[Any]   	=num_query_tokens
      UpperCamelCase_     :					Tuple   	=self.vision_config.hidden_size
      UpperCamelCase_     :					Tuple   	=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      UpperCamelCase_     :					int   	=1.0
      UpperCamelCase_     :					List[str]   	=0.02



     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :InstructBlipVisionConfig      ,  _lowerCamelCase :InstructBlipQFormerConfig      ,  _lowerCamelCase :PretrainedConfig      ,  **_lowerCamelCase :Dict      ,  ):







      '''simple docstring'''



      return cls(
          vision_config=vision_config.to_dict()      ,  qformer_config=qformer_config.to_dict()      ,  text_config=text_config.to_dict()      ,  **_lowerCamelCase      ,  )




     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=copy.deepcopy(self.__dict__   )
      UpperCamelCase_     :					Dict   	=self.vision_config.to_dict()
      UpperCamelCase_     :					Tuple   	=self.qformer_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.text_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.__class__.model_type
      return output
