'''simple docstring'''

import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A = "cpu"		,						__A = "openai/clip-vit-large-patch14"				) ->     None:
								_lowerCAmelCase										=device
								_lowerCAmelCase										=CLIPTokenizerFast.from_pretrained(__A				)
								_lowerCAmelCase										=[0.48_145_466, 0.4_578_275, 0.40_821_073]
								_lowerCAmelCase										=[0.26_862_954, 0.26_130_258, 0.27_577_711]
								_lowerCAmelCase										=torchvision.transforms.Normalize(self.image_mean		,						self.image_std				)
								_lowerCAmelCase										=torchvision.transforms.Resize(224				)
								_lowerCAmelCase										=torchvision.transforms.CenterCrop(224				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=self.resize(__A				)
								_lowerCAmelCase										=self.center_crop(__A				)
								_lowerCAmelCase										=self.normalize(__A				)
								return images






		def __call__(   self		,						__A=None		,						__A=None		,						**__A				) ->     List[Any]:
								_lowerCAmelCase										=self.tokenizer(text=__A		,						**__A				)
								_lowerCAmelCase										=self.preprocess_img(__A				)
								_lowerCAmelCase										={key: value.to(self.device				) for (key, value) in encoding.items()}
								return encoding

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A=10		,						__A=0.01		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=False		,						__A=True		,						__A="image"		,						__A=True		,						__A=False		,						__A=False		,						__A=False		,						) ->     None:
								super().__init__()
								_lowerCAmelCase										=None
								_lowerCAmelCase										=device if device else get_device()
								if vqgan:
														_lowerCAmelCase										=vqgan
								else:
														_lowerCAmelCase										=load_vqgan(self.device		,						conf_path=__A		,						ckpt_path=__A				)
								self.vqgan.eval()
								if clip:
														_lowerCAmelCase										=clip
								else:
														_lowerCAmelCase										=CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
								self.clip.to(self.device				)
								_lowerCAmelCase										=ProcessorGradientFlow(device=self.device				)

								_lowerCAmelCase										=iterations
								_lowerCAmelCase										=lr
								_lowerCAmelCase										=log
								_lowerCAmelCase										=make_grid
								_lowerCAmelCase										=return_val
								_lowerCAmelCase										=quantize
								_lowerCAmelCase										=self.vqgan.decoder.z_shape






		def 		UpperCamelCase__       (   self		,						__A=None		,						__A=None		,						__A=5		,						__A=True				) ->     Dict:
								_lowerCAmelCase										=[]
								if output_path is None:
														_lowerCAmelCase										='./animation.gif'
								if input_path is None:
														_lowerCAmelCase										=self.save_path
								_lowerCAmelCase										=sorted(glob(input_path + '/*'				)				)
								if not len(__A				):
														raise ValueError(
														    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
														    ' function?)'				)
								if len(__A				) == 1:
														print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
								_lowerCAmelCase										=total_duration / len(__A				)
								_lowerCAmelCase										=[frame_duration] * len(__A				)
								if extend_frames:
														_lowerCAmelCase										=1.5
														_lowerCAmelCase										=3
								for file_name in paths:
														if file_name.endswith('.png'				):
																				images.append(imageio.imread(__A				)				)
								imageio.mimsave(__A		,						__A		,						duration=__A				)
								print(F'''gif saved to {output_path}'''				)






		def 		UpperCamelCase__       (   self		,						__A=None		,						__A=None				) ->     List[Any]:
								if not (path or img):
														raise ValueError('Input either path or tensor'				)
								if img is not None:
														raise NotImplementedError
								_lowerCAmelCase										=preprocess(Image.open(__A				)		,						target_image_size=256				).to(self.device				)
								_lowerCAmelCase										=preprocess_vqgan(__A				)
								_lowerCAmelCase						,				*_lowerCAmelCase										=self.vqgan.encode(__A				)
								return z






		def 		UpperCamelCase__       (   self		,						__A				) ->     Any:
								_lowerCAmelCase										=self.latent.detach().requires_grad_()
								_lowerCAmelCase										=base_latent + transform_vector
								if self.quantize:
														_lowerCAmelCase						,				*_lowerCAmelCase										=self.vqgan.quantize(__A				)
								else:
														_lowerCAmelCase										=trans_latent
								return self.vqgan.decode(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A=None				) ->     Any:
								_lowerCAmelCase										=self.clip_preprocessor(text=__A		,						images=__A		,						return_tensors='pt'		,						padding=__A				)
								_lowerCAmelCase										=self.clip(**__A				)
								_lowerCAmelCase										=clip_outputs.logits_per_image
								if weights is not None:
														_lowerCAmelCase										=similarity_logits * weights
								return similarity_logits.sum()






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=self._get_clip_similarity(pos_prompts['prompts']		,						__A		,						weights=(1 / pos_prompts['weights'])				)
								if neg_prompts:
														_lowerCAmelCase										=self._get_clip_similarity(neg_prompts['prompts']		,						__A		,						weights=neg_prompts['weights']				)
								else:
														_lowerCAmelCase										=torch.tensor([1]		,						device=self.device				)
								_lowerCAmelCase										=-torch.log(__A				) + torch.log(__A				)
								return loss






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     str:
								_lowerCAmelCase										=torch.randn_like(self.latent		,						requires_grad=__A		,						device=self.device				)
								_lowerCAmelCase										=torch.optim.Adam([vector]		,						lr=self.lr				)

								for i in range(self.iterations				):
														optim.zero_grad()
														_lowerCAmelCase										=self._add_vector(__A				)
														_lowerCAmelCase										=loop_post_process(__A				)
														_lowerCAmelCase										=self._get_CLIP_loss(__A		,						__A		,						__A				)
														print('CLIP loss'		,						__A				)
														if self.log:
																				wandb.log({'CLIP Loss': clip_loss}				)
														clip_loss.backward(retain_graph=__A				)
														optim.step()
														if self.return_val == "image":
																				yield custom_to_pil(transformed_img[0]				)
														else:
																				yield vector






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Tuple:
								wandb.init(reinit=__A		,						project='face-editor'				)
								wandb.config.update({'Positive Prompts': positive_prompts}				)
								wandb.config.update({'Negative Prompts': negative_prompts}				)
								wandb.config.update({'lr': self.lr, 'iterations': self.iterations}				)
								if image_path:
														_lowerCAmelCase										=Image.open(__A				)
														_lowerCAmelCase										=image.resize((256, 256)				)
														wandb.log('Original Image'		,						wandb.Image(__A				)				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								if not prompts:
														return []
								_lowerCAmelCase										=[]
								_lowerCAmelCase										=[]
								if isinstance(__A		,						__A				):
														_lowerCAmelCase										=[prompt.strip() for prompt in prompts.split('|'				)]
								for prompt in prompts:
														if isinstance(__A		,						(tuple, list)				):
																				_lowerCAmelCase										=prompt[0]
																				_lowerCAmelCase										=float(prompt[1]				)
														elif ":" in prompt:
																				_lowerCAmelCase						,				_lowerCAmelCase										=prompt.split(':'				)
																				_lowerCAmelCase										=float(__A				)
														else:
																				_lowerCAmelCase										=prompt
																				_lowerCAmelCase										=1.0
														processed_prompts.append(__A				)
														weights.append(__A				)
								return {
								    "prompts": processed_prompts,
								    "weights": torch.tensor(__A		,						device=self.device				),
								}






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						__A=True		,						__A=False		,						__A=True		,						__A=True		,						__A=None		,						) ->     Dict:
								if image_path:
														_lowerCAmelCase										=self._get_latent(__A				)
								else:
														_lowerCAmelCase										=torch.randn(self.latent_dim		,						device=self.device				)
								if self.log:
														self._init_logging(__A		,						__A		,						__A				)

								assert pos_prompts, "You must provide at least one positive prompt."
								_lowerCAmelCase										=self.process_prompts(__A				)
								_lowerCAmelCase										=self.process_prompts(__A				)
								if save_final and save_path is None:
														_lowerCAmelCase										=os.path.join('./outputs/'		,						'_'.join(pos_prompts['prompts']				)				)
								if not os.path.exists(__A				):
														os.makedirs(__A				)
								else:
														_lowerCAmelCase										=save_path + '_' + get_timestamp()
														os.makedirs(__A				)
								_lowerCAmelCase										=save_path

								_lowerCAmelCase										=self.vqgan.decode(self.latent				)[0]
								if show_intermediate:
														print('Original Image'				)
														show_pil(custom_to_pil(__A				)				)

								_lowerCAmelCase										=loop_post_process(__A				)
								for iter, transformed_img in enumerate(self._optimize_CLIP(__A		,						__A		,						__A				)				):
														if show_intermediate:
																				show_pil(__A				)
														if save_intermediate:
																				transformed_img.save(os.path.join(self.save_path		,						F'''iter_{iter:03d}.png'''				)				)
														if self.log:
																				wandb.log({'Image': wandb.Image(__A				)}				)
								if show_final:
														show_pil(__A				)
								if save_final:
														transformed_img.save(os.path.join(self.save_path		,						F'''iter_{iter:03d}_final.png'''				)				)






'''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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Tuple       =							'blip_2_vision_model'






		def __init__(   self		,						__A=1408		,						__A=6144		,						__A=39		,						__A=16		,						__A=224		,						__A=14		,						__A="gelu"		,						__A=0.00_001		,						__A=0.0		,						__A=1E-10		,						__A=True		,						**__A		,						) ->     int:
								super().__init__(**__A				)

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






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

								_lowerCAmelCase						,				_lowerCAmelCase										=cls.get_config_dict(__A		,						**__A				)

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

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							'blip_2_qformer'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=0.02		,						__A=1E-12		,						__A=0		,						__A="absolute"		,						__A=2		,						__A=1408		,						**__A		,						) ->     List[str]:
								super().__init__(pad_token_id=__A		,						**__A				)

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






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

								_lowerCAmelCase						,				_lowerCAmelCase										=cls.get_config_dict(__A		,						**__A				)

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

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      Optional[int]       =							'blip-2'
		lowercase   :      Any       =							True






		def __init__(   self		,						__A=None		,						__A=None		,						__A=None		,						__A=32		,						**__A				) ->     int:
								super().__init__(**__A				)

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

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

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

								_lowerCAmelCase										=BlipaVisionConfig(**__A				)
								_lowerCAmelCase										=BlipaQFormerConfig(**__A				)
								_lowerCAmelCase										=text_config['model_type'] if 'model_type' in text_config else 'opt'
								_lowerCAmelCase										=CONFIG_MAPPING[text_model_type](**__A				)

								_lowerCAmelCase										=self.text_config.tie_word_embeddings
								_lowerCAmelCase										=self.text_config.is_encoder_decoder

								_lowerCAmelCase										=num_query_tokens
								_lowerCAmelCase										=self.vision_config.hidden_size
								_lowerCAmelCase										=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
								_lowerCAmelCase										=1.0
								_lowerCAmelCase										=0.02






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






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=copy.deepcopy(self.__dict__				)
								_lowerCAmelCase										=self.vision_config.to_dict()
								_lowerCAmelCase										=self.qformer_config.to_dict()
								_lowerCAmelCase										=self.text_config.to_dict()
								_lowerCAmelCase										=self.__class__.model_type
								return output





