





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   				=			logging.get_logger(__name__)

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




class   UpperCamelCase_       (     _A    ):
							lowerCamelCase_           =					"blip_2_vision_model"


							def __init__(							self    :Tuple							,				__A    :Any=1408							,				__A    :Union[str, Any]=6144							,				__A    :Any=39							,				__A    :Union[str, Any]=16							,				__A    :Any=224							,				__A    :Tuple=14							,				__A    :Dict="gelu"							,				__A    :str=0.0_0_0_0_1							,				__A    :Dict=0.0							,				__A    :Any=1E-10							,				__A    :List[Any]=True							,				**__A    :Union[str, Any]							,				)   ->   List[Any]:



									"""simple docstring"""



									super().__init__(**__lowerCamelCase		)

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


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



									"""simple docstring"""



									cls._set_token_in_kwargs(__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__lowerCamelCase							,				**__lowerCamelCase		)

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




class   UpperCamelCase_       (     _A    ):
							lowerCamelCase_           =					"blip_2_qformer"


							def __init__(							self    :Tuple							,				__A    :Union[str, Any]=3_0522							,				__A    :Union[str, Any]=768							,				__A    :Tuple=12							,				__A    :Dict=12							,				__A    :Optional[int]=3072							,				__A    :int="gelu"							,				__A    :Any=0.1							,				__A    :int=0.1							,				__A    :str=512							,				__A    :Any=0.0_2							,				__A    :Any=1E-12							,				__A    :Optional[Any]=0							,				__A    :List[Any]="absolute"							,				__A    :Any=2							,				__A    :str=1408							,				**__A    :int							,				)   ->   Optional[Any]:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      num_hidden_layers
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      hidden_act
									SCREAMING_SNAKE_CASE__			=      intermediate_size
									SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
									SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__			=      max_position_embeddings
									SCREAMING_SNAKE_CASE__			=      initializer_range
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      position_embedding_type
									SCREAMING_SNAKE_CASE__			=      cross_attention_frequency
									SCREAMING_SNAKE_CASE__			=      encoder_hidden_size


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :Union[str, os.PathLike]							,				**__A    :Optional[int]		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__lowerCamelCase		)

									SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__lowerCamelCase							,				**__lowerCamelCase		)

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




class   UpperCamelCase_       (     _A    ):
							lowerCamelCase_           =					"blip-2"
							lowerCamelCase_           =					True


							def __init__(							self    :Union[str, Any]							,				__A    :Any=None							,				__A    :Optional[int]=None							,				__A    :Union[str, Any]=None							,				__A    :Tuple=32							,				**__A    :Any		)   ->   Optional[int]:



									"""simple docstring"""



									super().__init__(**__lowerCamelCase		)

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

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

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

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

									SCREAMING_SNAKE_CASE__			=      self.text_config.tie_word_embeddings
									SCREAMING_SNAKE_CASE__			=      self.text_config.is_encoder_decoder

									SCREAMING_SNAKE_CASE__			=      num_query_tokens
									SCREAMING_SNAKE_CASE__			=      self.vision_config.hidden_size
									SCREAMING_SNAKE_CASE__			=      self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									SCREAMING_SNAKE_CASE__			=      1.0
									SCREAMING_SNAKE_CASE__			=      0.0_2


							@classmethod
							def      _snake_case  (							cls    :Optional[int]							,				__A    :BlipaVisionConfig							,				__A    :BlipaQFormerConfig							,				__A    :PretrainedConfig							,				**__A    :Tuple							,				)   ->   Tuple:



									"""simple docstring"""



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


							def      _snake_case  (							self    :Tuple		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.qformer_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output






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

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

				SCREAMING_SNAKE_CASE__			=      [np.array(i.resize((w, h)					,   resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      np.array(UpperCamelCase__    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      image.transpose(0					,   3					,   1					,   2    )
				SCREAMING_SNAKE_CASE__			=      2.0 * image - 1.0
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(image[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return mask
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [mask]

		if isinstance(mask[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      mask[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=      [np.array(m.convert("""L"""    ).resize((w, h)					,   resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      0
				SCREAMING_SNAKE_CASE__			=      1
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(mask[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return mask




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


							def __init__(							self    :Any							,				__A    :List[Any]							,				__A    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



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


							@torch.no_grad()
							def __call__(							self    :str							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :int = 250							,				__A    :float = 0.0							,				__A    :int = 10							,				__A    :int = 10							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      image

									SCREAMING_SNAKE_CASE__			=      _preprocess_image(__A		)
									SCREAMING_SNAKE_CASE__			=      original_image.to(device=self.device							,				dtype=self.unet.dtype		)
									SCREAMING_SNAKE_CASE__			=      _preprocess_mask(__A		)
									SCREAMING_SNAKE_CASE__			=      mask_image.to(device=self.device							,				dtype=self.unet.dtype		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape[0]

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

									SCREAMING_SNAKE_CASE__			=      original_image.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=self.device							,				dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(__A							,				__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      eta

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

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

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)