import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


snake_case__				= get_logger(__name__)



class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  'dummy_data'
     A_										=  'datasets'
     A_										=  False



     def __init__(					self				,				A_				,				A_				,				A_				,				A_ = None				,				A_ = False				,				A_ = True				,				A_ = None				,				) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				0
           _lowerCamelCase			    =				dataset_name
           _lowerCamelCase			    =				cache_dir
           _lowerCamelCase			    =				use_local_dummy_data
           _lowerCamelCase			    =				config
           # download_callbacks take a single url as input
           _lowerCamelCase			    =				download_callbacks or []
           # if False, it doesn't load existing files and it returns the paths of the dummy files relative
           # to the dummy_data zip file root
           _lowerCamelCase			    =				load_existing_dummy_data

           # TODO(PVP, QL) might need to make this more general
           _lowerCamelCase			    =				str(A_						)
           # to be downloaded
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				None



     @property
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""

           if self._dummy_file is None:
                 _lowerCamelCase			    =				self.download_dummy_data()
           return self._dummy_file



     @property
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""

           if self.config is not None:
                 # structure is dummy / config_name / version_name
                 return os.path.join('''dummy'''				,				self.config.name				,				self.version_name						)
           # structure is dummy / version_name
           return os.path.join('''dummy'''				,				self.version_name						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return os.path.join(self.dummy_data_folder				,				'''dummy_data.zip'''						)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				(
               self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
           )

           _lowerCamelCase			    =				cached_path(
               A_				,				cache_dir=self.cache_dir				,				extract_compressed_file=A_				,				force_extract=A_						)

           return os.path.join(A_				,				self.dummy_file_name						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return os.path.join(self.datasets_scripts_dir				,				self.dataset_name				,				self.dummy_zip_file						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""

           if self._bucket_url is None:
                 _lowerCamelCase			    =				hf_github_url(self.dataset_name				,				self.dummy_zip_file.replace(os.sep				,				'''/'''						)						)
           return self._bucket_url



     @property
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""

           if os.path.isdir(self.dummy_file						):
                 return self.dummy_file
           # else cut off path to file -> example `xsum`.
           return "/".join(self.dummy_file.replace(os.sep				,				'''/'''						).split('''/'''						)[:-1]						)



     def 		UpperCamelCase_						(					self				,				A_				,				*A_						) ->				Tuple:






           """simple docstring"""

           if self.load_existing_dummy_data:
                 # dummy data is downloaded and tested
                 _lowerCamelCase			    =				self.dummy_file
           else:
                 # dummy data cannot be downloaded and only the path to dummy file is returned
                 _lowerCamelCase			    =				self.dummy_file_name

           # special case when data_url is a dict
           if isinstance(A_				,				A_						):
                 return self.create_dummy_data_dict(A_				,				A_						)
           elif isinstance(A_				,				(list, tuple)						):
                 return self.create_dummy_data_list(A_				,				A_						)
           else:
                 return self.create_dummy_data_single(A_				,				A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				*A_						) ->				List[Any]:






           """simple docstring"""
           return self.download_and_extract(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Any:






           """simple docstring"""
           return self.download_and_extract(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				*A_				,				**A_						) ->				Dict:






           """simple docstring"""
           return path



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           return {}



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				{}
           for key, single_urls in data_url.items():
                 for download_callback in self.download_callbacks:
                       if isinstance(A_				,				A_						):
                             for single_url in single_urls:
                                   download_callback(A_						)
                       else:
                             _lowerCamelCase			    =				single_urls
                             download_callback(A_						)
                       # we force the name of each key to be the last file / folder name of the url path
                       # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 if isinstance(A_				,				A_						):
                       _lowerCamelCase			    =				[os.path.join(A_				,				urllib.parse.quote_plus(Path(A_						).name						)						) for x in single_urls]
                 else:
                       _lowerCamelCase			    =				single_urls
                       _lowerCamelCase			    =				os.path.join(A_				,				urllib.parse.quote_plus(Path(A_						).name						)						)
                 _lowerCamelCase			    =				value

           # make sure that values are unique
           if all(isinstance(A_				,				A_						) for i in dummy_data_dict.values()						) and len(set(dummy_data_dict.values()						)						) < len(
               dummy_data_dict.values()						):
                 # append key to value to make its name unique
                 _lowerCamelCase			    =				{key: value + key for key, value in dummy_data_dict.items()}

           return dummy_data_dict



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
           _lowerCamelCase			    =				all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''				,				A_						)						) for url in data_url						)
           _lowerCamelCase			    =				all(
               url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''						) for url in data_url						)
           if data_url and (is_tf_records or is_pubmed_records):
                 _lowerCamelCase			    =				[data_url[0]] * len(A_						)
           for single_url in data_url:
                 for download_callback in self.download_callbacks:
                       download_callback(A_						)
                 # we force the name of each key to be the last file / folder name of the url path
                 # if the url has arguments, we need to encode them with urllib.parse.quote_plus
                 _lowerCamelCase			    =				os.path.join(A_				,				urllib.parse.quote_plus(single_url.split('''/'''						)[-1]						)						)
                 dummy_data_list.append(A_						)
           return dummy_data_list



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				List[str]:






           """simple docstring"""

           for download_callback in self.download_callbacks:
                 download_callback(A_						)
           # we force the name of each key to be the last file / folder name of the url path
           # if the url has arguments, we need to encode them with urllib.parse.quote_plus
           _lowerCamelCase			    =				os.path.join(A_				,				urllib.parse.quote_plus(data_url.split('''/'''						)[-1]						)						)
           if os.path.exists(A_						) or not self.load_existing_dummy_data:
                 return value
           else:
                 # Backward compatibility, maybe deprecate at one point.
                 # For many datasets with single url calls to dl_manager.download_and_extract,
                 # the dummy_data.zip file is actually the zipped downloaded file
                 # while now we expected the dummy_data.zip file to be a directory containing
                 # the downloaded file.
                 return path_to_dummy_data



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""

           def _iter_archive_members(A_						):
                 # this preserves the order of the members inside the ZIP archive
                 _lowerCamelCase			    =				Path(self.dummy_file						).parent
                 _lowerCamelCase			    =				path.relative_to(A_						)
                 with ZipFile(self.local_path_to_dummy_data						) as zip_file:
                       _lowerCamelCase			    =				zip_file.namelist()
                 for member in members:
                       if member.startswith(relative_path.as_posix()						):
                             yield dummy_parent_path.joinpath(A_						)

           _lowerCamelCase			    =				Path(A_						)
           _lowerCamelCase			    =				_iter_archive_members(A_						) if self.use_local_dummy_data else path.rglob('''*'''						)
           for file_path in file_paths:
                 if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')						):
                       yield file_path.relative_to(A_						).as_posix(), file_path.open('''rb'''						)





     def 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """simple docstring"""

           if not isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				[paths]
           for path in paths:
                 if os.path.isfile(A_						):
                       if os.path.basename(A_						).startswith(('''.''', '''__''')						):
                             return
                       yield path
                 else:
                       for dirpath, dirnames, filenames in os.walk(A_						):
                             if os.path.basename(A_						).startswith(('''.''', '''__''')						):
                                   continue
                             dirnames.sort()
                             for filename in sorted(A_						):
                                   if filename.startswith(('''.''', '''__''')						):
                                         continue
                                   yield os.path.join(A_				,				A_						)
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  UpperCamelCase				:

     '''simple docstring'''

     def __init__(					self				,				A_ = "cpu"				,				A_ = "openai/clip-vit-large-patch14"						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				device
           _lowerCamelCase			    =				CLIPTokenizerFast.from_pretrained(A_						)
           _lowerCamelCase			    =				[0.48145466, 0.4578275, 0.40821073]
           _lowerCamelCase			    =				[0.26862954, 0.26130258, 0.27577711]
           _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				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.resize(A_						)
           _lowerCamelCase			    =				self.center_crop(A_						)
           _lowerCamelCase			    =				self.normalize(A_						)
           return images





     def __call__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _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  UpperCamelCase				( 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:






           """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=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						) ->				Any:






           """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(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						) ->				Union[str, Any]:






           """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(A_						)				,				target_image_size=2_56						).to(self.device						)
           _lowerCamelCase			    =				preprocess_vqgan(A_						)
           _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.encode(A_						)
           return z



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _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:






           """simple docstring"""
           _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_						) ->				Dict:






           """simple docstring"""
           _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:






           """simple docstring"""
           _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_						) ->				Any:






           """simple docstring"""
           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((2_56, 2_56)						)
                 wandb.log('''Original Image'''				,				wandb.Image(A_						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           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				,				) ->				str:






           """simple docstring"""
           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'						)						)