import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__    		=logging.get_logger(__name__)

__magic_name__    		={
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}



class      _A (     lowercase_				):
  SCREAMING_SNAKE_CASE_						:						Tuple    ="blip_text_model"






  def __init__(self ,					SCREAMING_SNAKE_CASE_=3_0524 ,					SCREAMING_SNAKE_CASE_=768 ,					SCREAMING_SNAKE_CASE_=768 ,					SCREAMING_SNAKE_CASE_=3072 ,					SCREAMING_SNAKE_CASE_=768 ,					SCREAMING_SNAKE_CASE_=12 ,					SCREAMING_SNAKE_CASE_=8 ,					SCREAMING_SNAKE_CASE_=512 ,					SCREAMING_SNAKE_CASE_="gelu" ,					SCREAMING_SNAKE_CASE_=1E-12 ,					SCREAMING_SNAKE_CASE_=0.0 ,					SCREAMING_SNAKE_CASE_=0.0 ,					SCREAMING_SNAKE_CASE_=0.02 ,					SCREAMING_SNAKE_CASE_=3_0522 ,					SCREAMING_SNAKE_CASE_=2 ,					SCREAMING_SNAKE_CASE_=0 ,					SCREAMING_SNAKE_CASE_=102 ,					SCREAMING_SNAKE_CASE_=True ,					SCREAMING_SNAKE_CASE_=True ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Dict:





       '''simple docstring'''





       super().__init__(
           pad_token_id=_lowercase ,					bos_token_id=_lowercase ,					eos_token_id=_lowercase ,					sep_token_id=_lowercase ,					**_lowercase ,					)

       UpperCamelCase__					    =   vocab_size
       UpperCamelCase__					    =   hidden_size
       UpperCamelCase__					    =   encoder_hidden_size
       UpperCamelCase__					    =   intermediate_size
       UpperCamelCase__					    =   projection_dim
       UpperCamelCase__					    =   hidden_dropout_prob
       UpperCamelCase__					    =   num_hidden_layers
       UpperCamelCase__					    =   num_attention_heads
       UpperCamelCase__					    =   max_position_embeddings
       UpperCamelCase__					    =   layer_norm_eps
       UpperCamelCase__					    =   hidden_act
       UpperCamelCase__					    =   initializer_range
       UpperCamelCase__					    =   attention_probs_dropout_prob
       UpperCamelCase__					    =   is_decoder
       UpperCamelCase__					    =   use_cache






  @classmethod
  def 		_a       (cls ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   "PretrainedConfig":





       '''simple docstring'''





       cls._set_token_in_kwargs(_lowercase						)

       UpperCamelCase__    ,							UpperCamelCase__					    =   cls.get_config_dict(_lowercase ,					**_lowercase						)

       # get the text config dict if we are loading from BlipConfig
       if config_dict.get('''model_type'''						) == "blip":
            UpperCamelCase__					    =   config_dict['''text_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      _A (     lowercase_				):
  SCREAMING_SNAKE_CASE_						:						Optional[int]    ="blip_vision_model"






  def __init__(self ,					SCREAMING_SNAKE_CASE_=768 ,					SCREAMING_SNAKE_CASE_=3072 ,					SCREAMING_SNAKE_CASE_=512 ,					SCREAMING_SNAKE_CASE_=12 ,					SCREAMING_SNAKE_CASE_=12 ,					SCREAMING_SNAKE_CASE_=384 ,					SCREAMING_SNAKE_CASE_=16 ,					SCREAMING_SNAKE_CASE_="gelu" ,					SCREAMING_SNAKE_CASE_=1E-5 ,					SCREAMING_SNAKE_CASE_=0.0 ,					SCREAMING_SNAKE_CASE_=1E-10 ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Any:





       '''simple docstring'''





       super().__init__(**_lowercase						)

       UpperCamelCase__					    =   hidden_size
       UpperCamelCase__					    =   intermediate_size
       UpperCamelCase__					    =   projection_dim
       UpperCamelCase__					    =   num_hidden_layers
       UpperCamelCase__					    =   num_attention_heads
       UpperCamelCase__					    =   patch_size
       UpperCamelCase__					    =   image_size
       UpperCamelCase__					    =   initializer_range
       UpperCamelCase__					    =   attention_dropout
       UpperCamelCase__					    =   layer_norm_eps
       UpperCamelCase__					    =   hidden_act






  @classmethod
  def 		_a       (cls ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   "PretrainedConfig":





       '''simple docstring'''





       cls._set_token_in_kwargs(_lowercase						)

       UpperCamelCase__    ,							UpperCamelCase__					    =   cls.get_config_dict(_lowercase ,					**_lowercase						)

       # get the vision config dict if we are loading from BlipConfig
       if config_dict.get('''model_type'''						) == "blip":
            UpperCamelCase__					    =   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      _A (     lowercase_				):
  SCREAMING_SNAKE_CASE_						:						Tuple    ="blip"
  SCREAMING_SNAKE_CASE_						:						List[Any]    =True






  def __init__(self ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=512 ,					SCREAMING_SNAKE_CASE_=2.6592 ,					SCREAMING_SNAKE_CASE_=256 ,					**SCREAMING_SNAKE_CASE_ ,					)		->   Dict:





       '''simple docstring'''





       super().__init__(**_lowercase						)

       if text_config is None:
            UpperCamelCase__					    =   {}
            logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''						)

       if vision_config is None:
            UpperCamelCase__					    =   {}
            logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''						)

       UpperCamelCase__					    =   BlipTextConfig(**_lowercase						)
       UpperCamelCase__					    =   BlipVisionConfig(**_lowercase						)

       UpperCamelCase__					    =   self.vision_config.hidden_size

       UpperCamelCase__					    =   projection_dim
       UpperCamelCase__					    =   logit_scale_init_value
       UpperCamelCase__					    =   1.0
       UpperCamelCase__					    =   0.02
       UpperCamelCase__					    =   image_text_hidden_size






  @classmethod
  def 		_a       (cls ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   Tuple:





       '''simple docstring'''





       return cls(text_config=text_config.to_dict() ,					vision_config=vision_config.to_dict() ,					**_lowercase						)






  def 		_a       (self						)		->   Optional[Any]:





       '''simple docstring'''





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






'''simple docstring'''

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

import torch

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






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											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(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





