





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 					__lowerCAmelCase       :






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


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  device
							snake_case_						:    Optional[int]				=  CLIPTokenizerFast.from_pretrained(__magic_name__    )
							snake_case_						:    str				=  [0.48_145_466, 0.4_578_275, 0.40_821_073]
							snake_case_						:    Dict				=  [0.26_862_954, 0.26_130_258, 0.27_577_711]
							snake_case_						:    str				=  torchvision.transforms.Normalize(self.image_mean					,							self.image_std    )
							snake_case_						:    Union[str, Any]				=  torchvision.transforms.Resize(224    )
							snake_case_						:    str				=  torchvision.transforms.CenterCrop(224    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Tuple				=  self.resize(__magic_name__    )
							snake_case_						:    Optional[int]				=  self.center_crop(__magic_name__    )
							snake_case_						:    Tuple				=  self.normalize(__magic_name__    )
							return images






			def __call__(self					,							__magic_name__=None					,							__magic_name__=None					,							**__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Any				=  self.tokenizer(text=__magic_name__					,							**__magic_name__    )
							snake_case_						:    Union[str, Any]				=  self.preprocess_img(__magic_name__    )
							snake_case_						:    Dict				=  {key: value.to(self.device    ) for (key, value) in encoding.items()}
							return encoding




class 					__lowerCAmelCase       (			nn.Module     ):






			def __init__(self					,							__magic_name__=10					,							__magic_name__=0.01					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=False					,							__magic_name__=True					,							__magic_name__="image"					,							__magic_name__=True					,							__magic_name__=False					,							__magic_name__=False					,							__magic_name__=False					,							)     ->   None:


							'''simple docstring'''



							super().__init__()
							snake_case_						:    Optional[Any]				=  None
							snake_case_						:    List[Any]				=  device if device else get_device()
							if vqgan:
											snake_case_						:    List[str]				=  vqgan
							else:
											snake_case_						:    Optional[int]				=  load_vqgan(self.device					,							conf_path=__magic_name__					,							ckpt_path=__magic_name__    )
							self.vqgan.eval()
							if clip:
											snake_case_						:    Optional[Any]				=  clip
							else:
											snake_case_						:    int				=  CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''    )
							self.clip.to(self.device    )
							snake_case_						:    Tuple				=  ProcessorGradientFlow(device=self.device    )

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






			def 			lowerCamelCase  (self					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=5					,							__magic_name__=True    )     ->   List[Any]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__=None					,							__magic_name__=None    )     ->   Optional[Any]:


							'''simple docstring'''



							if not (path or img):
											raise ValueError('''Input either path or tensor'''    )
							if img is not None:
											raise NotImplementedError
							snake_case_						:    Any				=  preprocess(Image.open(__magic_name__    )					,							target_image_size=256    ).to(self.device    )
							snake_case_						:    Union[str, Any]				=  preprocess_vqgan(__magic_name__    )
							snake_case_			,							*snake_case_						:    Union[str, Any]				=  self.vqgan.encode(__magic_name__    )
							return z






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   int:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__=None    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    str				=  self.clip_preprocessor(text=__magic_name__					,							images=__magic_name__					,							return_tensors='''pt'''					,							padding=__magic_name__    )
							snake_case_						:    Optional[Any]				=  self.clip(**__magic_name__    )
							snake_case_						:    Optional[Any]				=  clip_outputs.logits_per_image
							if weights is not None:
											snake_case_						:    Any				=  similarity_logits * weights
							return similarity_logits.sum()






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  torch.randn_like(self.latent					,							requires_grad=__magic_name__					,							device=self.device    )
							snake_case_						:    Any				=  torch.optim.Adam([vector]					,							lr=self.lr    )

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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



							wandb.init(reinit=__magic_name__					,							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:
											snake_case_						:    List[Any]				=  Image.open(__magic_name__    )
											snake_case_						:    str				=  image.resize((256, 256)    )
											wandb.log('''Original Image'''					,							wandb.Image(__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Optional[int]:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__=None					,							__magic_name__=None					,							__magic_name__=True					,							__magic_name__=False					,							__magic_name__=True					,							__magic_name__=True					,							__magic_name__=None					,							)     ->   Dict:


							'''simple docstring'''



							if image_path:
											snake_case_						:    Any				=  self._get_latent(__magic_name__    )
							else:
											snake_case_						:    List[str]				=  torch.randn(self.latent_dim					,							device=self.device    )
							if self.log:
											self._init_logging(__magic_name__					,							__magic_name__					,							__magic_name__    )

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

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

							snake_case_						:    Optional[int]				=  loop_post_process(__magic_name__    )
							for iter, transformed_img in enumerate(self._optimize_CLIP(__magic_name__					,							__magic_name__					,							__magic_name__    )    ):
											if show_intermediate:
															show_pil(__magic_name__    )
											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(__magic_name__    )}    )
							if show_final:
											show_pil(__magic_name__    )
							if save_final:
											transformed_img.save(os.path.join(self.save_path					,							F'''iter_{iter:03d}_final.png'''    )    )







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Any       			=      BioGptTokenizer
			lowerCamelCase_     :     Optional[Any]       			=      False






			def 			lowerCamelCase  (self    )     ->   List[Any]:


							'''simple docstring'''



							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							snake_case_						:    Optional[Any]				=  [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''w</w>''',
							    '''r</w>''',
							    '''t</w>''',
							    '''lo''',
							    '''low''',
							    '''er</w>''',
							    '''low</w>''',
							    '''lowest</w>''',
							    '''newer</w>''',
							    '''wider</w>''',
							    '''<unk>''',
							]
							snake_case_						:    Union[str, Any]				=  dict(zip(__magic_name__					,							range(len(__magic_name__    )    )    )    )
							snake_case_						:    Union[str, Any]				=  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

							snake_case_						:    Any				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''vocab_file''']    )
							snake_case_						:    Dict				=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['''merges_file''']    )
							with open(self.vocab_file					,							'''w'''    ) as fp:
											fp.write(json.dumps(__magic_name__    )    )
							with open(self.merges_file					,							'''w'''    ) as fp:
											fp.write('''\n'''.join(__magic_name__    )    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    str				=  '''lower newer'''
							snake_case_						:    Dict				=  '''lower newer'''
							return input_text, output_text






			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer(self.vocab_file					,							self.merges_file    )

							snake_case_						:    Union[str, Any]				=  '''lower'''
							snake_case_						:    Optional[int]				=  ['''low''', '''er</w>''']
							snake_case_						:    Any				=  tokenizer.tokenize(__magic_name__    )
							self.assertListEqual(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  tokens + ['''<unk>''']
							snake_case_						:    List[str]				=  [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__    )					,							__magic_name__    )






			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  BioGptTokenizer.from_pretrained('''microsoft/biogpt'''    )

							snake_case_						:    List[Any]				=  tokenizer.encode('''sequence builders'''					,							add_special_tokens=__magic_name__    )
							snake_case_						:    str				=  tokenizer.encode('''multi-sequence build'''					,							add_special_tokens=__magic_name__    )

							snake_case_						:    str				=  tokenizer.build_inputs_with_special_tokens(__magic_name__    )
							snake_case_						:    List[str]				=  tokenizer.build_inputs_with_special_tokens(__magic_name__					,							__magic_name__    )

							self.assertTrue(encoded_sentence == [2] + text    )
							self.assertTrue(encoded_pair == [2] + text + [2] + text_a    )
