





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							:  List[Any]			,					UpperCAmelCase_							:  str = "cpu"			,					UpperCAmelCase_							:  str = "openai/clip-vit-large-patch14")			->None:



   '''simple docstring'''



   lowerCamelCase__: Tuple         =device
   lowerCamelCase__: Optional[int]         =CLIPTokenizerFast.from_pretrained(UpperCAmelCase_)
   lowerCamelCase__: Any         =[0.4814_5466, 0.457_8275, 0.4082_1073]
   lowerCamelCase__: Union[str, Any]         =[0.2686_2954, 0.2613_0258, 0.2757_7711]
   lowerCamelCase__: Union[str, Any]         =torchvision.transforms.Normalize(self.image_mean			,					self.image_std)
   lowerCamelCase__: List[Any]         =torchvision.transforms.Resize(224)
   lowerCamelCase__: Dict         =torchvision.transforms.CenterCrop(224)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  str)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.resize(UpperCAmelCase_)
   lowerCamelCase__: Optional[int]         =self.center_crop(UpperCAmelCase_)
   lowerCamelCase__: Dict         =self.normalize(UpperCAmelCase_)
   return images







 def __call__(self							:  Any			,					UpperCAmelCase_							:  List[str]=None			,					UpperCAmelCase_							:  str=None			,					**UpperCAmelCase_							:  Tuple)			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =self.tokenizer(text=UpperCAmelCase_			,					**UpperCAmelCase_)
   lowerCamelCase__: str         =self.preprocess_img(UpperCAmelCase_)
   lowerCamelCase__: str         ={key: value.to(self.device) for (key, value) in encoding.items()}
   return encoding



class   _SCREAMING_SNAKE_CASE		(      nn.Module       ):







 '''simple docstring'''

 def __init__(self							:  Any			,					UpperCAmelCase_							:  List[Any]=10			,					UpperCAmelCase_							:  int=0.01			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  str=None			,					UpperCAmelCase_							:  Optional[Any]=None			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					UpperCAmelCase_							:  Any=False			,					UpperCAmelCase_							:  Dict=True			,					UpperCAmelCase_							:  str="image"			,					UpperCAmelCase_							:  Any=True			,					UpperCAmelCase_							:  str=False			,					UpperCAmelCase_							:  Dict=False			,					UpperCAmelCase_							:  Optional[Any]=False			,					)			->None:



   '''simple docstring'''



   super().__init__()
   lowerCamelCase__: Optional[int]         =None
   lowerCamelCase__: str         =device if device else get_device()
   if vqgan:
     lowerCamelCase__: Dict         =vqgan
   else:
     lowerCamelCase__: Optional[Any]         =load_vqgan(self.device			,					conf_path=UpperCAmelCase_			,					ckpt_path=UpperCAmelCase_)
   self.vqgan.eval()
   if clip:
     lowerCamelCase__: Union[str, Any]         =clip
   else:
     lowerCamelCase__: str         =CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
   self.clip.to(self.device)
   lowerCamelCase__: str         =ProcessorGradientFlow(device=self.device)

   lowerCamelCase__: Union[str, Any]         =iterations
   lowerCamelCase__: str         =lr
   lowerCamelCase__: str         =log
   lowerCamelCase__: str         =make_grid
   lowerCamelCase__: int         =return_val
   lowerCamelCase__: int         =quantize
   lowerCamelCase__: Dict         =self.vqgan.decoder.z_shape

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[Any]=None			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  List[Any]=5			,					UpperCAmelCase_							:  Union[str, Any]=True)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Dict         =[]
   if output_path is None:
     lowerCamelCase__: Union[str, Any]         ="./animation.gif"
   if input_path is None:
     lowerCamelCase__: List[Any]         =self.save_path
   lowerCamelCase__: Any         =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__: Tuple         =total_duration / len(UpperCAmelCase_)
   lowerCamelCase__: Optional[Any]         =[frame_duration] * len(UpperCAmelCase_)
   if extend_frames:
     lowerCamelCase__: int         =1.5
     lowerCamelCase__: Optional[int]         =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 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  Any=None)			->Tuple:



   '''simple docstring'''



   if not (path or img):
     raise ValueError("Input either path or tensor")
   if img is not None:
     raise NotImplementedError
   lowerCamelCase__: str         =preprocess(Image.open(UpperCAmelCase_)			,					target_image_size=256).to(self.device)
   lowerCamelCase__: Any         =preprocess_vqgan(UpperCAmelCase_)
   lowerCamelCase__    ,   *lowerCamelCase__: str         =self.vqgan.encode(UpperCAmelCase_)
   return z

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  List[Any])			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.latent.detach().requires_grad_()
   lowerCamelCase__: List[Any]         =base_latent + transform_vector
   if self.quantize:
     lowerCamelCase__    ,   *lowerCamelCase__: int         =self.vqgan.quantize(UpperCAmelCase_)
   else:
     lowerCamelCase__: str         =trans_latent
   return self.vqgan.decode(UpperCAmelCase_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					UpperCAmelCase_							:  Any			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  Union[str, Any]=None)			->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.clip_preprocessor(text=UpperCAmelCase_			,					images=UpperCAmelCase_			,					return_tensors="pt"			,					padding=UpperCAmelCase_)
   lowerCamelCase__: List[str]         =self.clip(**UpperCAmelCase_)
   lowerCamelCase__: int         =clip_outputs.logits_per_image
   if weights is not None:
     lowerCamelCase__: Optional[int]         =similarity_logits * weights
   return similarity_logits.sum()

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  int)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self._get_clip_similarity(pos_prompts["prompts"]			,					UpperCAmelCase_			,					weights=(1 / pos_prompts["weights"]))
   if neg_prompts:
     lowerCamelCase__: Tuple         =self._get_clip_similarity(neg_prompts["prompts"]			,					UpperCAmelCase_			,					weights=neg_prompts["weights"])
   else:
     lowerCamelCase__: List[Any]         =torch.tensor([1]			,					device=self.device)
   lowerCamelCase__: Union[str, Any]         =-torch.log(UpperCAmelCase_) + torch.log(UpperCAmelCase_)
   return loss

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  Tuple			,					UpperCAmelCase_							:  Union[str, Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: Dict         =torch.randn_like(self.latent			,					requires_grad=UpperCAmelCase_			,					device=self.device)
   lowerCamelCase__: Any         =torch.optim.Adam([vector]			,					lr=self.lr)

   for i in range(self.iterations):
     optim.zero_grad()
     lowerCamelCase__: List[Any]         =self._add_vector(UpperCAmelCase_)
     lowerCamelCase__: Optional[int]         =loop_post_process(UpperCAmelCase_)
     lowerCamelCase__: str         =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 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any]			,					UpperCAmelCase_							:  Union[str, Any]			,					UpperCAmelCase_							:  Optional[int]			,					UpperCAmelCase_							:  List[Any])			->str:



   '''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__: Dict         =Image.open(UpperCAmelCase_)
     lowerCamelCase__: str         =image.resize((256, 256))
     wandb.log("Original Image"			,					wandb.Image(UpperCAmelCase_))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  Optional[int])			->int:



   '''simple docstring'''



   if not prompts:
     return []
   lowerCamelCase__: Optional[Any]         =[]
   lowerCamelCase__: Union[str, Any]         =[]
   if isinstance(UpperCAmelCase_			,					UpperCAmelCase_):
     lowerCamelCase__: Tuple         =[prompt.strip() for prompt in prompts.split("|")]
   for prompt in prompts:
     if isinstance(UpperCAmelCase_			,					(tuple, list)):
       lowerCamelCase__: Optional[Any]         =prompt[0]
       lowerCamelCase__: Dict         =float(prompt[1])
     elif ":" in prompt:
       lowerCamelCase__    ,   lowerCamelCase__: Optional[Any]         =prompt.split(":")
       lowerCamelCase__: str         =float(UpperCAmelCase_)
     else:
       lowerCamelCase__: List[str]         =prompt
       lowerCamelCase__: Any         =1.0
     processed_prompts.append(UpperCAmelCase_)
     weights.append(UpperCAmelCase_)
   return {
       "prompts": processed_prompts,
       "weights": torch.tensor(UpperCAmelCase_			,					device=self.device),
   }







 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  List[Any]			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  Any=True			,					UpperCAmelCase_							:  List[Any]=False			,					UpperCAmelCase_							:  Optional[int]=True			,					UpperCAmelCase_							:  Union[str, Any]=True			,					UpperCAmelCase_							:  Tuple=None			,					)			->List[str]:



   '''simple docstring'''



   if image_path:
     lowerCamelCase__: Any         =self._get_latent(UpperCAmelCase_)
   else:
     lowerCamelCase__: int         =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__: Optional[int]         =self.process_prompts(UpperCAmelCase_)
   lowerCamelCase__: Dict         =self.process_prompts(UpperCAmelCase_)
   if save_final and save_path is None:
     lowerCamelCase__: Optional[Any]         =os.path.join("./outputs/"			,					"_".join(pos_prompts["prompts"]))
   if not os.path.exists(UpperCAmelCase_):
     os.makedirs(UpperCAmelCase_)
   else:
     lowerCamelCase__: Dict         =save_path + "_" + get_timestamp()
     os.makedirs(UpperCAmelCase_)
   lowerCamelCase__: int         =save_path

   lowerCamelCase__: Dict         =self.vqgan.decode(self.latent)[0]
   if show_intermediate:
     print("Original Image")
     show_pil(custom_to_pil(UpperCAmelCase_))

   lowerCamelCase__: List[Any]         =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"""))




'''simple docstring'''

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
       import tensorflow as tf

       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

UpperCAmelCase					 =	logging.get_logger(__name__)


class 	lowerCAmelCase  (   enum.Enum ):
       lowerCAmelCase_												=			0
       lowerCAmelCase_												=			1






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"generated"







       def __init__(				self      :			Tuple    ,   *__lowercase      :			Tuple    ,   **__lowercase      :			Optional[int]   ):







            """simple docstring"""







            super().__init__(*__lowercase    ,   **__lowercase   )

            self.check_model_type(
                TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
                if self.framework == 'tf'
                else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING   )







       def       snake_case (				self      :			Dict    ,   __lowercase      :			Any=None    ,   __lowercase      :			int=None    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			int=None    ,   __lowercase      :			str=None    ,   __lowercase      :			List[Any]=None    ,   **__lowercase      :			Union[str, Any]    ,   ):







            """simple docstring"""







            __lowercase      		={}
            if truncation is not None:
                 __lowercase      		=truncation

            __lowercase      		=generate_kwargs

            __lowercase      		={}
            if return_tensors is not None and return_type is None:
                 __lowercase      		=ReturnType.TENSORS if return_tensors else ReturnType.TEXT
            if return_type is not None:
                 __lowercase      		=return_type

            if clean_up_tokenization_spaces is not None:
                 __lowercase      		=clean_up_tokenization_spaces

            if stop_sequence is not None:
                 __lowercase      		=self.tokenizer.encode(__lowercase    ,   add_special_tokens=__lowercase   )
                 if len(__lowercase   ) > 1:
                      warnings.warn(
                          'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                          ' the stop sequence will be used as the stop sequence string in the interim.'   )
                 __lowercase      		=stop_sequence_ids[0]

            return preprocess_params, forward_params, postprocess_params







       def       snake_case (				self      :			List[Any]    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            return True







       def       snake_case (				self      :			Optional[Any]    ,   *__lowercase      :			Optional[int]    ,   __lowercase      :			Union[str, Any]   ):







            """simple docstring"""







            __lowercase      		=self.model.config.prefix if self.model.config.prefix is not None else ''
            if isinstance(args[0]    ,   __lowercase   ):
                 if self.tokenizer.pad_token_id is None:
                      raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'   )
                 __lowercase      		=([prefix + arg for arg in args[0]],)
                 __lowercase      		=True

            elif isinstance(args[0]    ,   __lowercase   ):
                 __lowercase      		=(prefix + args[0],)
                 __lowercase      		=False
            else:
                 raise ValueError(
                     f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''   )
            __lowercase      		=self.tokenizer(*__lowercase    ,   padding=__lowercase    ,   truncation=__lowercase    ,   return_tensors=self.framework   )
            # This is produced by tokenizers but is an invalid generate kwargs
            if "token_type_ids" in inputs:
                 del inputs["token_type_ids"]
            return inputs







       def __call__(				self      :			Any    ,   *__lowercase      :			int    ,   **__lowercase      :			Optional[Any]   ):







            """simple docstring"""







            __lowercase      		=super().__call__(*__lowercase    ,   **__lowercase   )
            if (
                isinstance(args[0]    ,   __lowercase   )
                and all(isinstance(__lowercase    ,   __lowercase   ) for el in args[0]   )
                and all(len(__lowercase   ) == 1 for res in result   )
            ):
                 return [res[0] for res in result]
            return result







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			Tuple    ,   __lowercase      :			List[Any]=TruncationStrategy.DO_NOT_TRUNCATE    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







            __lowercase      		=self._parse_and_tokenize(__lowercase    ,   truncation=__lowercase    ,   **__lowercase   )
            return inputs







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			Tuple    ,   **__lowercase      :			Tuple   ):







            """simple docstring"""







            if self.framework == "pt":
                 __lowercase		,  __lowercase      		=model_inputs['input_ids'].shape
            elif self.framework == "tf":
                 __lowercase		,  __lowercase      		=tf.shape(model_inputs['input_ids']   ).numpy()

            __lowercase      		=generate_kwargs.get('min_length'    ,   self.model.config.min_length   )
            __lowercase      		=generate_kwargs.get('max_length'    ,   self.model.config.max_length   )
            self.check_inputs(__lowercase    ,   generate_kwargs['min_length']    ,   generate_kwargs['max_length']   )
            __lowercase      		=self.model.generate(**__lowercase    ,   **__lowercase   )
            __lowercase      		=output_ids.shape[0]
            if self.framework == "pt":
                 __lowercase      		=output_ids.reshape(__lowercase    ,   out_b // in_b    ,   *output_ids.shape[1:]   )
            elif self.framework == "tf":
                 __lowercase      		=tf.reshape(__lowercase    ,   (in_b, out_b // in_b, *output_ids.shape[1:])   )
            return {"output_ids": output_ids}







       def       snake_case (				self      :			str    ,   __lowercase      :			Optional[Any]    ,   __lowercase      :			Optional[int]=ReturnType.TEXT    ,   __lowercase      :			Union[str, Any]=False   ):







            """simple docstring"""







            __lowercase      		=[]
            for output_ids in model_outputs["output_ids"][0]:
                 if return_type == ReturnType.TENSORS:
                      __lowercase      		={f'''{self.return_name}_token_ids''': output_ids}
                 elif return_type == ReturnType.TEXT:
                      __lowercase      		={
                          f'''{self.return_name}_text''': self.tokenizer.decode(
                              __lowercase    ,   skip_special_tokens=__lowercase    ,   clean_up_tokenization_spaces=__lowercase    ,   )
                      }
                 records.append(__lowercase   )
            return records






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"summary"







       def __call__(				self      :			Dict    ,   *__lowercase      :			Dict    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







            return super().__call__(*__lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			str    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            if max_length < min_length:
                 logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''   )

            if input_length < max_length:
                 logger.warning(
                     f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
                     'a summarization task, where outputs shorter than the input are typically wanted, you might '
                     f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''   )






@add_end_docstrings(A )
class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			"translation"







       def       snake_case (				self      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            if input_length > 0.9 * max_length:
                 logger.warning(
                     f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
                     'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'   )
            return True







       def       snake_case (				self      :			int    ,   *__lowercase      :			str    ,   __lowercase      :			List[str]=TruncationStrategy.DO_NOT_TRUNCATE    ,   __lowercase      :			List[Any]=None    ,   __lowercase      :			Optional[int]=None   ):







            """simple docstring"""







            if getattr(self.tokenizer    ,   '_build_translation_inputs'    ,   __lowercase   ):
                 return self.tokenizer._build_translation_inputs(
                     *__lowercase    ,   return_tensors=self.framework    ,   truncation=__lowercase    ,   src_lang=__lowercase    ,   tgt_lang=__lowercase   )
            else:
                 return super()._parse_and_tokenize(*__lowercase    ,   truncation=__lowercase   )







       def       snake_case (				self      :			Optional[int]    ,   __lowercase      :			List[str]=None    ,   __lowercase      :			Optional[Any]=None    ,   **__lowercase      :			int   ):







            """simple docstring"""







            __lowercase		,  __lowercase		,  __lowercase      		=super()._sanitize_parameters(**__lowercase   )
            if src_lang is not None:
                 __lowercase      		=src_lang
            if tgt_lang is not None:
                 __lowercase      		=tgt_lang
            if src_lang is None and tgt_lang is None:
                 # Backward compatibility, direct arguments use is preferred.
                 __lowercase      		=kwargs.get('task'    ,   self.task   )
                 __lowercase      		=task.split('_'   )
                 if task and len(__lowercase   ) == 4:
                      # translation, XX, to YY
                      __lowercase      		=items[1]
                      __lowercase      		=items[3]
            return preprocess_params, forward_params, postprocess_params







       def __call__(				self      :			Optional[Any]    ,   *__lowercase      :			Any    ,   **__lowercase      :			List[Any]   ):







            """simple docstring"""







            return super().__call__(*__lowercase    ,   **__lowercase   )


