
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							:							int     ,						_A							:							str = "cpu"     ,						_A							:							str = "openai/clip-vit-large-patch14" ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		device
       __SCREAMING_SNAKE_CASE      :	int					=		CLIPTokenizerFast.from_pretrained(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		torchvision.transforms.Normalize(self.image_mean     ,						self.image_std )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		torchvision.transforms.Resize(224 )
       __SCREAMING_SNAKE_CASE      :	int					=		torchvision.transforms.CenterCrop(224 )



     def  UpperCAmelCase__							(				self							:							Any     ,						_A							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.resize(_A )
       __SCREAMING_SNAKE_CASE      :	Any					=		self.center_crop(_A )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.normalize(_A )
       return images




     def __call__(				self							:							int     ,						_A							:							Dict=None     ,						_A							:							str=None     ,						**_A							:							Dict ):







       """simple docstring"""




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






class     __UpperCamelCase (							nn.Module       ):






     """simple docstring"""







     def __init__(				self							:							Any     ,						_A							:							Any=10     ,						_A							:							Tuple=0.01     ,						_A							:							int=None     ,						_A							:							Any=None     ,						_A							:							str=None     ,						_A							:							str=None     ,						_A							:							Union[str, Any]=None     ,						_A							:							int=None     ,						_A							:							str=False     ,						_A							:							int=True     ,						_A							:							str="image"     ,						_A							:							Union[str, Any]=True     ,						_A							:							Tuple=False     ,						_A							:							str=False     ,						_A							:							Dict=False     ,						):







       """simple docstring"""




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

       __SCREAMING_SNAKE_CASE      :	Any					=		iterations
       __SCREAMING_SNAKE_CASE      :	List[str]					=		lr
       __SCREAMING_SNAKE_CASE      :	List[str]					=		log
       __SCREAMING_SNAKE_CASE      :	List[str]					=		make_grid
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		return_val
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		quantize
       __SCREAMING_SNAKE_CASE      :	Any					=		self.vqgan.decoder.z_shape



     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							int=None     ,						_A							:							str=None     ,						_A							:							str=5     ,						_A							:							str=True ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		[]
       if output_path is None:
         __SCREAMING_SNAKE_CASE      :	Dict					=		'''./animation.gif'''
       if input_path is None:
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.save_path
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		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?)''' )
       __SCREAMING_SNAKE_CASE      :	Tuple					=		total_duration / len(_A )
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[frame_duration] * len(_A )
       if extend_frames:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		1.5
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		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							:							List[Any]     ,						_A							:							Dict=None     ,						_A							:							Tuple=None ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Optional[Any]     ,						_A							:							List[str] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							List[Any]     ,						_A							:							Tuple     ,						_A							:							Dict     ,						_A							:							Optional[Any]=None ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							Tuple     ,						_A							:							List[Any]     ,						_A							:							List[Any]     ,						_A							:							Union[str, Any] ):







       """simple docstring"""




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



     def  UpperCAmelCase__							(				self							:							int     ,						_A							:							str     ,						_A							:							List[Any]     ,						_A							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.randn_like(self.latent     ,						requires_grad=_A     ,						device=self.device )
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		torch.optim.Adam([vector]     ,						lr=self.lr )

       for i in range(self.iterations ):
         optim.zero_grad()
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self._add_vector(_A )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		loop_post_process(_A )
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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							:							str     ,						_A							:							List[str]     ,						_A							:							int     ,						_A							:							List[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:
         __SCREAMING_SNAKE_CASE      :	Dict					=		Image.open(_A )
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		image.resize((256, 256) )
         wandb.log('''Original Image'''     ,						wandb.Image(_A ) )



     def  UpperCAmelCase__							(				self							:							Union[str, Any]     ,						_A							:							Optional[int] ):







       """simple docstring"""




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




     def  UpperCAmelCase__							(				self							:							Optional[int]     ,						_A							:							List[str]     ,						_A							:							List[Any]=None     ,						_A							:							Tuple=None     ,						_A							:							Dict=True     ,						_A							:							int=False     ,						_A							:							List[Any]=True     ,						_A							:							Tuple=True     ,						_A							:							int=None     ,						):







       """simple docstring"""




       if image_path:
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self._get_latent(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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."
       __SCREAMING_SNAKE_CASE      :	Tuple					=		self.process_prompts(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.process_prompts(_A )
       if save_final and save_path is None:
         __SCREAMING_SNAKE_CASE      :	List[str]					=		os.path.join('''./outputs/'''     ,						'''_'''.join(pos_prompts['''prompts'''] ) )
       if not os.path.exists(_A ):
         os.makedirs(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	str					=		save_path + '''_''' + get_timestamp()
         os.makedirs(_A )
       __SCREAMING_SNAKE_CASE      :	Dict					=		save_path

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

       __SCREAMING_SNAKE_CASE      :	str					=		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 unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   int										=      BarthezTokenizer
       __lowercase	:   Any										=      BarthezTokenizerFast
       __lowercase	:   Dict										=      True
       __lowercase	:   Optional[int]										=      True


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            super().setUp()

            __snake_case            =	BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''	)
            tokenizer.save_pretrained(self.tmpdirname	)
            tokenizer.save_pretrained(self.tmpdirname     ,	legacy_format=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	tokenizer


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	'''<pad>'''
            __snake_case            =	1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE	)     ,	__SCREAMING_SNAKE_CASE	)


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



            '''simple docstring'''




            __snake_case            =	list(self.get_tokenizer().get_vocab().keys()	)

            self.assertEqual(vocab_keys[0]     ,	'''<s>'''	)
            self.assertEqual(vocab_keys[1]     ,	'''<pad>'''	)
            self.assertEqual(vocab_keys[-1]     ,	'''<mask>'''	)
            self.assertEqual(len(__SCREAMING_SNAKE_CASE	)     ,	10_1122	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            self.assertEqual(self.get_tokenizer().vocab_size     ,	10_1122	)


       @require_torch
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
            __snake_case            =	[0, 57, 3018, 7_0307, 91, 2]

            __snake_case            =	self.tokenizer(
                __SCREAMING_SNAKE_CASE     ,	max_length=len(__SCREAMING_SNAKE_CASE	)     ,	padding=__SCREAMING_SNAKE_CASE     ,	truncation=__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	)
            self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            self.assertEqual((2, 6)     ,	batch.input_ids.shape	)
            self.assertEqual((2, 6)     ,	batch.attention_mask.shape	)
            __snake_case            =	batch.input_ids.tolist()[0]
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            if not self.test_rust_tokenizer:
                 return

            __snake_case            =	self.get_tokenizer()
            __snake_case            =	self.get_rust_tokenizer()

            __snake_case            =	'''I was born in 92000, and this is falsé.'''

            __snake_case            =	tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE     ,	add_special_tokens=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)

            __snake_case            =	self.get_rust_tokenizer()
            __snake_case            =	tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	rust_tokenizer.encode(__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)




       @slow
       def    lowerCAmelCase   ( self	)   ->	int:



            '''simple docstring'''




            __snake_case            =	{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
            # fmt: on

            # moussaKam/mbarthez is a french model. So we also use french texts.
            __snake_case            =	[
                '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
                '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
                '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
                '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
                '''telles que la traduction et la synthèse de texte.''',
            ]

            self.tokenizer_integration_test_util(
                expected_encoding=__SCREAMING_SNAKE_CASE     ,	model_name='''moussaKam/mbarthez'''     ,	revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''     ,	sequences=__SCREAMING_SNAKE_CASE     ,	)






