






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					:






						def __init__(	self     ,						_UpperCamelCase = "cpu"     ,						_UpperCamelCase = "openai/clip-vit-large-patch14"   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						device
												lowerCAmelCase__    						=						CLIPTokenizerFast.from_pretrained(_UpperCamelCase   )
												lowerCAmelCase__    						=						[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
												lowerCAmelCase__    						=						[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
												lowerCAmelCase__    						=						torchvision.transforms.Normalize(self.image_mean     ,						self.image_std   )
												lowerCAmelCase__    						=						torchvision.transforms.Resize(2_24   )
												lowerCAmelCase__    						=						torchvision.transforms.CenterCrop(2_24   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.resize(_UpperCamelCase   )
												lowerCAmelCase__    						=						self.center_crop(_UpperCamelCase   )
												lowerCAmelCase__    						=						self.normalize(_UpperCamelCase   )
												return images







						def __call__(	self     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.tokenizer(text=_UpperCamelCase     ,						**_UpperCamelCase   )
												lowerCAmelCase__    						=						self.preprocess_img(_UpperCamelCase   )
												lowerCAmelCase__    						=						{key: value.to(self.device   ) for (key, value) in encoding.items()}
												return encoding




class     __SCREAMING_SNAKE_CASE					(   nn.Module):






						def __init__(	self     ,						_UpperCamelCase=10     ,						_UpperCamelCase=0.01     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						_UpperCamelCase="image"     ,						_UpperCamelCase=True     ,						_UpperCamelCase=False     ,						_UpperCamelCase=False     ,						_UpperCamelCase=False     ,						):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						device if device else get_device()
												if vqgan:
																		lowerCAmelCase__    						=						vqgan
												else:
																		lowerCAmelCase__    						=						load_vqgan(self.device     ,						conf_path=_UpperCamelCase     ,						ckpt_path=_UpperCamelCase   )
												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     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=5     ,						_UpperCamelCase=True   ):






												"""simple docstring"""
												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(_UpperCamelCase   ):
																		raise ValueError(
																		    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
																		    ' function?)'   )
												if len(_UpperCamelCase   ) == 1:
																		print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'   )
												lowerCAmelCase__    						=						total_duration / len(_UpperCamelCase   )
												lowerCAmelCase__    						=						[frame_duration] * len(_UpperCamelCase   )
												if extend_frames:
																		lowerCAmelCase__    						=						1.5
																		lowerCAmelCase__    						=						3
												for file_name in paths:
																		if file_name.endswith('.png'   ):
																								images.append(imageio.imread(_UpperCamelCase   )   )
												imageio.mimsave(_UpperCamelCase     ,						_UpperCamelCase     ,						duration=_UpperCamelCase   )
												print(F"gif saved to {output_path}"   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if not (path or img):
																		raise ValueError('Input either path or tensor'   )
												if img is not None:
																		raise NotImplementedError
												lowerCAmelCase__    						=						preprocess(Image.open(_UpperCamelCase   )     ,						target_image_size=2_56   ).to(self.device   )
												lowerCAmelCase__    						=						preprocess_vqgan(_UpperCamelCase   )
												lowerCAmelCase__						,       *lowerCAmelCase__    						=						self.vqgan.encode(_UpperCamelCase   )
												return z






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.latent.detach().requires_grad_()
												lowerCAmelCase__    						=						base_latent + transform_vector
												if self.quantize:
																		lowerCAmelCase__						,       *lowerCAmelCase__    						=						self.vqgan.quantize(_UpperCamelCase   )
												else:
																		lowerCAmelCase__    						=						trans_latent
												return self.vqgan.decode(_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self.clip_preprocessor(text=_UpperCamelCase     ,						images=_UpperCamelCase     ,						return_tensors='pt'     ,						padding=_UpperCamelCase   )
												lowerCAmelCase__    						=						self.clip(**_UpperCamelCase   )
												lowerCAmelCase__    						=						clip_outputs.logits_per_image
												if weights is not None:
																		lowerCAmelCase__    						=						similarity_logits * weights
												return similarity_logits.sum()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._get_clip_similarity(pos_prompts['prompts']     ,						_UpperCamelCase     ,						weights=(1 / pos_prompts['weights'])   )
												if neg_prompts:
																		lowerCAmelCase__    						=						self._get_clip_similarity(neg_prompts['prompts']     ,						_UpperCamelCase     ,						weights=neg_prompts['weights']   )
												else:
																		lowerCAmelCase__    						=						torch.tensor([1]     ,						device=self.device   )
												lowerCAmelCase__    						=						-torch.log(_UpperCamelCase   ) + torch.log(_UpperCamelCase   )
												return loss






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						torch.randn_like(self.latent     ,						requires_grad=_UpperCamelCase     ,						device=self.device   )
												lowerCAmelCase__    						=						torch.optim.Adam([vector]     ,						lr=self.lr   )

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






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												wandb.init(reinit=_UpperCamelCase     ,						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(_UpperCamelCase   )
																		lowerCAmelCase__    						=						image.resize((2_56, 2_56)   )
																		wandb.log('Original Image'     ,						wandb.Image(_UpperCamelCase   )   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if not prompts:
																		return []
												lowerCAmelCase__    						=						[]
												lowerCAmelCase__    						=						[]
												if isinstance(_UpperCamelCase     ,						_UpperCamelCase   ):
																		lowerCAmelCase__    						=						[prompt.strip() for prompt in prompts.split('|'   )]
												for prompt in prompts:
																		if isinstance(_UpperCamelCase     ,						(tuple, list)   ):
																								lowerCAmelCase__    						=						prompt[0]
																								lowerCAmelCase__    						=						float(prompt[1]   )
																		elif ":" in prompt:
																								lowerCAmelCase__						,       lowerCAmelCase__    						=						prompt.split(':'   )
																								lowerCAmelCase__    						=						float(_UpperCamelCase   )
																		else:
																								lowerCAmelCase__    						=						prompt
																								lowerCAmelCase__    						=						1.0
																		processed_prompts.append(_UpperCamelCase   )
																		weights.append(_UpperCamelCase   )
												return {
												    "prompts": processed_prompts,
												    "weights": torch.tensor(_UpperCamelCase     ,						device=self.device   ),
												}







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=True     ,						_UpperCamelCase=False     ,						_UpperCamelCase=True     ,						_UpperCamelCase=True     ,						_UpperCamelCase=None     ,						):






												"""simple docstring"""
												if image_path:
																		lowerCAmelCase__    						=						self._get_latent(_UpperCamelCase   )
												else:
																		lowerCAmelCase__    						=						torch.randn(self.latent_dim     ,						device=self.device   )
												if self.log:
																		self._init_logging(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )

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

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

												lowerCAmelCase__    						=						loop_post_process(_UpperCamelCase   )
												for iter, transformed_img in enumerate(self._optimize_CLIP(_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase   )   ):
																		if show_intermediate:
																								show_pil(_UpperCamelCase   )
																		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(_UpperCamelCase   )}   )
												if show_final:
																		show_pil(_UpperCamelCase   )
												if save_final:
																		transformed_img.save(os.path.join(self.save_path     ,						F"iter_{iter:03d}_final.png"   )   )













import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


__snake_case    :    Any         =     logging.get_logger(__name__)

__snake_case    :    Tuple         =     {"""tokenizer_file""": """tokenizer.json"""}

__snake_case    :    str         =     {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}


class     __SCREAMING_SNAKE_CASE					(   __lowercase):
						_SCREAMING_SNAKE_CASE	:       Dict   =					VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					['''input_ids''', '''attention_mask''']
						_SCREAMING_SNAKE_CASE	:       Union[str, Any]   =					None






						def __init__(	self     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase=None     ,						_UpperCamelCase="<unk>"     ,						_UpperCamelCase="<s>"     ,						_UpperCamelCase="</s>"     ,						_UpperCamelCase="<pad>"     ,						_UpperCamelCase=False     ,						_UpperCamelCase=False     ,						**_UpperCamelCase     ,						):






												"""simple docstring"""
												super().__init__(
												    _UpperCamelCase     ,						_UpperCamelCase     ,						tokenizer_file=_UpperCamelCase     ,						unk_token=_UpperCamelCase     ,						bos_token=_UpperCamelCase     ,						eos_token=_UpperCamelCase     ,						pad_token=_UpperCamelCase     ,						add_prefix_space=_UpperCamelCase     ,						clean_up_tokenization_spaces=_UpperCamelCase     ,						**_UpperCamelCase     ,						)
												lowerCAmelCase__    						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
												if pre_tok_state.get('add_prefix_space'     ,						_UpperCamelCase   ) != add_prefix_space:
																		lowerCAmelCase__    						=						getattr(_UpperCamelCase     ,						pre_tok_state.pop('type'   )   )
																		lowerCAmelCase__    						=						add_prefix_space
																		lowerCAmelCase__    						=						pre_tok_class(**_UpperCamelCase   )

												lowerCAmelCase__    						=						add_prefix_space






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.get('is_split_into_words'     ,						_UpperCamelCase   )
												if not (self.add_prefix_space or not is_split_into_words):
																		raise Exception(
																		    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
																		    ' pretokenized inputs.'   )

												return super()._batch_encode_plus(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						kwargs.get('is_split_into_words'     ,						_UpperCamelCase   )

												if not (self.add_prefix_space or not is_split_into_words):
																		raise Exception(
																		    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
																		    ' pretokenized inputs.'   )

												return super()._encode_plus(*_UpperCamelCase     ,						**_UpperCamelCase   )






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase = None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						self._tokenizer.model.save(_UpperCamelCase     ,						name=_UpperCamelCase   )
												return tuple(_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						[]
												for is_user, text in conversation.iter_texts():
																		input_ids.extend(self.encode(_UpperCamelCase     ,						add_special_tokens=_UpperCamelCase   ) + [self.eos_token_id]   )

												if len(_UpperCamelCase   ) > self.model_max_length:
																		lowerCAmelCase__    						=						input_ids[-self.model_max_length :]
												return input_ids





