

"""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_    :		List[str]							     =  logging.get_logger(__name__)

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



class     a  (				_lowerCamelCase ):



    """simple docstring"""



    UpperCAmelCase         =    "blip_2_vision_model"

    def __init__(   self:       Dict			,  UpperCamelCase:       Dict=14_08			,  UpperCamelCase:       str=61_44			,  UpperCamelCase:       List[str]=39			,  UpperCamelCase:       List[str]=16			,  UpperCamelCase:       Union[str, Any]=2_24			,  UpperCamelCase:       List[Any]=14			,  UpperCamelCase:       Union[str, Any]="gelu"			,  UpperCamelCase:       str=0.00_001			,  UpperCamelCase:       Dict=0.0			,  UpperCamelCase:       Optional[Any]=1e-1_0			,  UpperCamelCase:       List[str]=True			,  **UpperCamelCase:       Dict			,  ):






         """simple docstring"""


         super().__init__(**UpperCamelCase				)

         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]			,  UpperCamelCase:       Union[str, os.PathLike]			,  **UpperCamelCase:       str				):






         """simple docstring"""


         cls._set_token_in_kwargs(UpperCamelCase				)

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

         # 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(UpperCamelCase			,  **UpperCamelCase				)



class     a  (				_lowerCamelCase ):



    """simple docstring"""



    UpperCAmelCase         =    "blip_2_qformer"

    def __init__(   self:       int			,  UpperCamelCase:       Dict=3_05_22			,  UpperCamelCase:       List[Any]=7_68			,  UpperCamelCase:       Optional[Any]=12			,  UpperCamelCase:       Optional[int]=12			,  UpperCamelCase:       Union[str, Any]=30_72			,  UpperCamelCase:       Any="gelu"			,  UpperCamelCase:       List[str]=0.1			,  UpperCamelCase:       List[Any]=0.1			,  UpperCamelCase:       Any=5_12			,  UpperCamelCase:       Any=0.02			,  UpperCamelCase:       Tuple=1e-1_2			,  UpperCamelCase:       Dict=0			,  UpperCamelCase:       int="absolute"			,  UpperCamelCase:       List[Any]=2			,  UpperCamelCase:       Union[str, Any]=14_08			,  **UpperCamelCase:       Optional[int]			,  ):






         """simple docstring"""


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

         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:       Any			,  UpperCamelCase:       Union[str, os.PathLike]			,  **UpperCamelCase:       Optional[Any]				):






         """simple docstring"""


         cls._set_token_in_kwargs(UpperCamelCase				)

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

         # 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(UpperCamelCase			,  **UpperCamelCase				)



class     a  (				_lowerCamelCase ):



    """simple docstring"""



    UpperCAmelCase         =    "blip-2"
    UpperCAmelCase         =    True

    def __init__(   self:       Optional[int]			,  UpperCamelCase:       int=None			,  UpperCamelCase:       Optional[int]=None			,  UpperCamelCase:       List[str]=None			,  UpperCamelCase:       Dict=32			,  **UpperCamelCase:       str				):






         """simple docstring"""


         super().__init__(**UpperCamelCase				)

         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(**UpperCamelCase				)
         A__								=     BlipaQFormerConfig(**UpperCamelCase				)
         A__								=     text_config['model_type'] if 'model_type' in text_config else 'opt'
         A__								=     CONFIG_MAPPING[text_model_type](**UpperCamelCase				)

         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__								=     0.02

    @classmethod
    def  UpperCamelCase		(   cls:       List[Any]			,  UpperCamelCase:       BlipaVisionConfig			,  UpperCamelCase:       BlipaQFormerConfig			,  UpperCamelCase:       PretrainedConfig			,  **UpperCamelCase:       Union[str, Any]			,  ):






         """simple docstring"""


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





    def  UpperCamelCase		(   self:       Tuple				):






         """simple docstring"""


         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








from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )