

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowerCAmelCase_       	=	logging.get_logger(__name__)  # pylint: disable=invalid-name


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						warnings.warn(
						    '''The preprocess method is deprecated and will be removed in a future version. Please'''
						    ''' use VaeImageProcessor.preprocess instead'''  ,		SCREAMING_SNAKE_CASE__  ,		)
						if isinstance(SCREAMING_SNAKE_CASE__  ,		torch.Tensor       ):
												return image
						elif isinstance(SCREAMING_SNAKE_CASE__  ,		PIL.Image.Image       ):
												snake_case_   =	[image]

						if isinstance(image[0]  ,		PIL.Image.Image       ):
												snake_case_,    snake_case_   =	image[0].size
												snake_case_,    snake_case_   =	(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

												snake_case_   =	[np.array(i.resize((w, h)  ,		resample=PIL_INTERPOLATION['''lanczos''']       )       )[None, :] for i in image]
												snake_case_   =	np.concatenate(SCREAMING_SNAKE_CASE__  ,		axis=0       )
												snake_case_   =	np.array(SCREAMING_SNAKE_CASE__       ).astype(np.floataa       ) / 255.0
												snake_case_   =	image.transpose(0  ,		3  ,		1  ,		2       )
												snake_case_   =	2.0 * image - 1.0
												snake_case_   =	torch.from_numpy(SCREAMING_SNAKE_CASE__       )
						elif isinstance(image[0]  ,		torch.Tensor       ):
												snake_case_   =	torch.cat(SCREAMING_SNAKE_CASE__  ,		dim=0       )
						return image


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						if isinstance(SCREAMING_SNAKE_CASE__  ,		torch.Tensor       ):
												return mask
						elif isinstance(SCREAMING_SNAKE_CASE__  ,		PIL.Image.Image       ):
												snake_case_   =	[mask]

						if isinstance(mask[0]  ,		PIL.Image.Image       ):
												snake_case_,    snake_case_   =	mask[0].size
												snake_case_,    snake_case_   =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
												snake_case_   =	[np.array(m.convert('''L'''       ).resize((w, h)  ,		resample=PIL_INTERPOLATION['''nearest''']       )       )[None, :] for m in mask]
												snake_case_   =	np.concatenate(SCREAMING_SNAKE_CASE__  ,		axis=0       )
												snake_case_   =	mask.astype(np.floataa       ) / 255.0
												snake_case_   =	0
												snake_case_   =	1
												snake_case_   =	torch.from_numpy(SCREAMING_SNAKE_CASE__       )
						elif isinstance(mask[0]  ,		torch.Tensor       ):
												snake_case_   =	torch.cat(SCREAMING_SNAKE_CASE__  ,		dim=0       )
						return mask





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   UNetaDModel
			SCREAMING_SNAKE_CASE     :   RePaintScheduler







			def __init__(     self :				List[str]							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Optional[int]							)      ->List[str]:
									super().__init__()
									self.register_modules(unet=_UpperCamelCase							,   scheduler=_UpperCamelCase							)







			@torch.no_grad()
			def __call__(     self :				List[Any]							,   _UpperCamelCase :				Union[torch.Tensor, PIL.Image.Image]							,   _UpperCamelCase :				Union[torch.Tensor, PIL.Image.Image]							,   _UpperCamelCase :				int = 2_5_0							,   _UpperCamelCase :				float = 0.0							,   _UpperCamelCase :				int = 1_0							,   _UpperCamelCase :				int = 1_0							,   _UpperCamelCase :				Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   _UpperCamelCase :				Optional[str] = "pil"							,   _UpperCamelCase :				bool = True							,   )      ->Union[ImagePipelineOutput, Tuple]:
									snake_case_   =	image

									snake_case_   =	_preprocess_image(_UpperCamelCase							)
									snake_case_   =	original_image.to(device=self.device							,   dtype=self.unet.dtype							)
									snake_case_   =	_preprocess_mask(_UpperCamelCase							)
									snake_case_   =	mask_image.to(device=self.device							,   dtype=self.unet.dtype							)

									snake_case_   =	original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(_UpperCamelCase							,   _UpperCamelCase							) and len(_UpperCamelCase							) != batch_size:
															raise ValueError(
															    f'''You have passed a list of generators of length {len(_UpperCamelCase							)}, but requested an effective batch'''
															    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

									snake_case_   =	original_image.shape
									snake_case_   =	randn_tensor(_UpperCamelCase							,   generator=_UpperCamelCase							,   device=self.device							,   dtype=self.unet.dtype							)

									# set step values
									self.scheduler.set_timesteps(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							,   self.device							)
									snake_case_   =	eta

									snake_case_   =	self.scheduler.timesteps[0] + 1
									snake_case_   =	generator[0] if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
															if t < t_last:
																					# predict the noise residual
																					snake_case_   =	self.unet(_UpperCamelCase							,   _UpperCamelCase							).sample
																					# compute previous image: x_t -> x_t-1
																					snake_case_   =	self.scheduler.step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							).prev_sample

															else:
																					# compute the reverse: x_t-1 -> x_t
																					snake_case_   =	self.scheduler.undo_step(_UpperCamelCase							,   _UpperCamelCase							,   _UpperCamelCase							)
															snake_case_   =	t

									snake_case_   =	(image / 2 + 0.5).clamp(0							,   1							)
									snake_case_   =	image.cpu().permute(0							,   2							,   3							,   1							).numpy()
									if output_type == "pil":
															snake_case_   =	self.numpy_to_pil(_UpperCamelCase							)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_UpperCamelCase							)





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__       :      Optional[int]  =   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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Union[str, Any]	     =      """blip_2_vision_model"""







    def __init__(    self     ,  UpperCamelCase__=1408     ,  UpperCamelCase__=6144     ,  UpperCamelCase__=39     ,  UpperCamelCase__=16     ,  UpperCamelCase__=224     ,  UpperCamelCase__=14     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.00001     ,  UpperCamelCase__=0.0     ,  UpperCamelCase__=1e-10     ,  UpperCamelCase__=True     ,  **UpperCamelCase__     ,  )     ->      Optional[Any]:
           super().__init__(**UpperCamelCase__     )

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      "PretrainedConfig":
           cls._set_token_in_kwargs(UpperCamelCase__     )

           lowerCamelCase ,      lowerCamelCase   :      List[str]			 =					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":
                  lowerCamelCase   :      Optional[int]			 =					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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Dict	     =      """blip_2_qformer"""







    def __init__(    self     ,  UpperCamelCase__=3_0522     ,  UpperCamelCase__=768     ,  UpperCamelCase__=12     ,  UpperCamelCase__=12     ,  UpperCamelCase__=3072     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=512     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=1e-12     ,  UpperCamelCase__=0     ,  UpperCamelCase__="absolute"     ,  UpperCamelCase__=2     ,  UpperCamelCase__=1408     ,  **UpperCamelCase__     ,  )     ->      int:
           super().__init__(pad_token_id=UpperCamelCase__     ,  **UpperCamelCase__     )

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







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  **UpperCamelCase__     )     ->      "PretrainedConfig":
           cls._set_token_in_kwargs(UpperCamelCase__     )

           lowerCamelCase ,      lowerCamelCase   :      str			 =					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":
                  lowerCamelCase   :      int			 =					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 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	List[str]	     =      """blip-2"""
    lowerCamelCase_      :	int	     =      True







    def __init__(    self     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=None     ,  UpperCamelCase__=32     ,  **UpperCamelCase__     )     ->      str:
           super().__init__(**UpperCamelCase__     )

           if vision_config is None:
                  lowerCamelCase   :      List[Any]			 =					{}
                  logger.info("vision_config is None. initializing the Blip2VisionConfig with default values."     )

           if qformer_config is None:
                  lowerCamelCase   :      List[Any]			 =					{}
                  logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values."     )

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

           lowerCamelCase   :      Optional[int]			 =					BlipaVisionConfig(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					BlipaQFormerConfig(**UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					text_config["model_type"] if "model_type" in text_config else "opt"
           lowerCamelCase   :      str			 =					CONFIG_MAPPING[text_model_type](**UpperCamelCase__     )

           lowerCamelCase   :      Optional[Any]			 =					self.text_config.tie_word_embeddings
           lowerCamelCase   :      int			 =					self.text_config.is_encoder_decoder

           lowerCamelCase   :      Optional[Any]			 =					num_query_tokens
           lowerCamelCase   :      int			 =					self.vision_config.hidden_size
           lowerCamelCase   :      Tuple			 =					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           lowerCamelCase   :      Dict			 =					1.0
           lowerCamelCase   :      List[Any]			 =					0.02







    @classmethod
    def     _lowercase     (    cls     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  **UpperCamelCase__     ,  )     ->      str:
           return cls(
               vision_config=vision_config.to_dict()     ,  qformer_config=qformer_config.to_dict()     ,  text_config=text_config.to_dict()     ,  **UpperCamelCase__     ,  )







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Tuple			 =					copy.deepcopy(self.__dict__     )
           lowerCamelCase   :      Tuple			 =					self.vision_config.to_dict()
           lowerCamelCase   :      int			 =					self.qformer_config.to_dict()
           lowerCamelCase   :      Optional[Any]			 =					self.text_config.to_dict()
           lowerCamelCase   :      int			 =					self.__class__.model_type
           return output






