





'''simple docstring'''

from collections.abc import Generator
from math import sin





def 		UpperCamelCase							(				_lowerCamelCase   :   bytes			):
    if len(_lowerCamelCase			) != 32:
        raise ValueError("Input must be of length 32"			)

    A__							=     b""
    for i in [3, 2, 1, 0]:
        little_endian += string_aa[8 * i : 8 * i + 8]
    return little_endian





def 		UpperCamelCase							(				_lowerCamelCase   :   int			):
    if i < 0:
        raise ValueError("Input must be non-negative"			)

    A__							=     format(_lowerCamelCase		,			"08x"			)[-8:]
    A__							=     b""
    for i in [3, 2, 1, 0]:
        little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"			)
    return little_endian_hex





def 		UpperCamelCase							(				_lowerCamelCase   :   bytes			):
    A__							=     b""
    for char in message:
        bit_string += format(_lowerCamelCase		,			"08b"			).encode("utf-8"			)
    A__							=     format(len(_lowerCamelCase			)		,			"064b"			).encode("utf-8"			)

    # Pad bit_string to a multiple of 512 chars
    bit_string += b"1"
    while len(_lowerCamelCase			) % 5_12 != 4_48:
        bit_string += b"0"
    bit_string += to_little_endian(start_len[32:]			) + to_little_endian(start_len[:32]			)

    return bit_string





def 		UpperCamelCase							(				_lowerCamelCase   :   bytes			):
    if len(_lowerCamelCase			) % 5_12 != 0:
        raise ValueError("Input must have length that's a multiple of 512"			)

    for pos in range(0		,			len(_lowerCamelCase			)		,			5_12			):
        A__							=     bit_string[pos : pos + 5_12]
        A__							=     []
        for i in range(0		,			5_12		,			32			):
            block_words.append(int(to_little_endian(block[i : i + 32]			)		,			2			)			)
        yield block_words





def 		UpperCamelCase							(				_lowerCamelCase   :   int			):
    if i < 0:
        raise ValueError("Input must be non-negative"			)

    A__							=     format(_lowerCamelCase		,			"032b"			)
    A__							=     ""
    for c in i_str:
        new_str += "1" if c == "0" else "0"
    return int(_lowerCamelCase		,			2			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return (a + b) % 2**32





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    if i < 0:
        raise ValueError("Input must be non-negative"			)
    if shift < 0:
        raise ValueError("Shift must be non-negative"			)
    return ((i << shift) ^ (i >> (32 - shift))) % 2**32





def 		UpperCamelCase							(				_lowerCamelCase   :   bytes			):
    A__							=     preprocess(_lowerCamelCase			)

    A__							=     [int(2**32 * abs(sin(i + 1			)			)			) for i in range(64			)]

    # Starting states
    A__							=     0X67_45_23_01
    A__							=     0Xef_cd_ab_89
    A__							=     0X98_ba_dc_fe
    A__							=     0X10_32_54_76

    A__							=     [
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
    ]

    # Process bit string in chunks, each with 16 32-char words
    for block_words in get_block_words(_lowerCamelCase			):
        A__							=     aa
        A__							=     ba
        A__							=     ca
        A__							=     da

        # Hash current chunk
        for i in range(64			):
            if i <= 15:
                # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                A__							=     d ^ (b & (c ^ d))
                A__							=     i
            elif i <= 31:
                # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                A__							=     c ^ (d & (b ^ c))
                A__							=     (5 * i + 1) % 16
            elif i <= 47:
                A__							=     b ^ c ^ d
                A__							=     (3 * i + 5) % 16
            else:
                A__							=     c ^ (b | not_aa(_lowerCamelCase			))
                A__							=     (7 * i) % 16
            A__							=     (f + a + added_consts[i] + block_words[g]) % 2**32
            A__							=     d
            A__							=     c
            A__							=     b
            A__							=     sum_aa(_lowerCamelCase		,			left_rotate_aa(_lowerCamelCase		,			shift_amounts[i]			)			)

        # Add hashed chunk to running total
        A__							=     sum_aa(_lowerCamelCase		,			_lowerCamelCase			)
        A__							=     sum_aa(_lowerCamelCase		,			_lowerCamelCase			)
        A__							=     sum_aa(_lowerCamelCase		,			_lowerCamelCase			)
        A__							=     sum_aa(_lowerCamelCase		,			_lowerCamelCase			)

    A__							=     reformat_hex(_lowerCamelCase			) + reformat_hex(_lowerCamelCase			) + reformat_hex(_lowerCamelCase			) + reformat_hex(_lowerCamelCase			)
    return digest


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''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


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      List[Any]						      ={
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_vision_model"""
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=14_08		,			lowercase_ :int=61_44		,			lowercase_ :List[str]=39		,			lowercase_ :List[Any]=16		,			lowercase_ :Union[str, Any]=2_24		,			lowercase_ :List[str]=14		,			lowercase_ :Dict="gelu"		,			lowercase_ :Tuple=0.0_0_0_0_1		,			lowercase_ :Dict=0.0		,			lowercase_ :Optional[int]=1E-10		,			lowercase_ :Optional[Any]=True		,			**lowercase_ :List[str]		,			)->				int:
     super().__init__(**lowercase_				)

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



 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :Optional[Any]				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

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






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip_2_qformer"""
 def __init__(							self :Dict		,			lowercase_ :int=3_05_22		,			lowercase_ :Union[str, Any]=7_68		,			lowercase_ :Tuple=12		,			lowercase_ :Union[str, Any]=12		,			lowercase_ :str=30_72		,			lowercase_ :Any="gelu"		,			lowercase_ :Optional[int]=0.1		,			lowercase_ :List[str]=0.1		,			lowercase_ :Dict=5_12		,			lowercase_ :Optional[int]=0.0_2		,			lowercase_ :Dict=1E-12		,			lowercase_ :List[Any]=0		,			lowercase_ :str="absolute"		,			lowercase_ :List[str]=2		,			lowercase_ :Tuple=14_08		,			**lowercase_ :Tuple		,			)->				List[Any]:
     super().__init__(pad_token_id=lowercase_		,			**lowercase_				)

     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__							=     initializer_range
     A__							=     layer_norm_eps
     A__							=     position_embedding_type
     A__							=     cross_attention_frequency
     A__							=     encoder_hidden_size



 @classmethod
 def 	UpperCAmelCase_				(							cls :int		,			lowercase_ :Union[str, os.PathLike]		,			**lowercase_ :str				)->				"PretrainedConfig":
     cls._set_token_in_kwargs(lowercase_				)

     A__,      A__							=     cls.get_config_dict(lowercase_		,			**lowercase_				)

     # get the qformer config dict if we are loading from Blip2Config
     if config_dict.get("model_type"				) == "blip-2":
         A__							=     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(lowercase_		,			**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""blip-2"""
 __lowercase  						=							True
 def __init__(							self :int		,			lowercase_ :List[Any]=None		,			lowercase_ :Dict=None		,			lowercase_ :Tuple=None		,			lowercase_ :List[str]=32		,			**lowercase_ :Any				)->				Union[str, Any]:
     super().__init__(**lowercase_				)

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

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

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

     A__							=     BlipaVisionConfig(**lowercase_				)
     A__							=     BlipaQFormerConfig(**lowercase_				)
     A__							=     text_config["model_type"] if "model_type" in text_config else "opt"
     A__							=     CONFIG_MAPPING[text_model_type](**lowercase_				)

     A__							=     self.text_config.tie_word_embeddings
     A__							=     self.text_config.is_encoder_decoder

     A__							=     num_query_tokens
     A__							=     self.vision_config.hidden_size
     A__							=     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     A__							=     1.0
     A__							=     0.0_2
 @classmethod
 def 	UpperCAmelCase_				(							cls :Optional[Any]		,			lowercase_ :BlipaVisionConfig		,			lowercase_ :BlipaQFormerConfig		,			lowercase_ :PretrainedConfig		,			**lowercase_ :Any		,			)->				Optional[int]:
     return cls(
         vision_config=vision_config.to_dict()		,			qformer_config=qformer_config.to_dict()		,			text_config=text_config.to_dict()		,			**lowercase_		,			)



 def 	UpperCAmelCase_				(							self :Dict				)->				Any:
     A__							=     copy.deepcopy(self.__dict__				)
     A__							=     self.vision_config.to_dict()
     A__							=     self.qformer_config.to_dict()
     A__							=     self.text_config.to_dict()
     A__							=     self.__class__.model_type
     return output



