





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      :      str		,   _A      :      str = "cpu"		,   _A      :      str = "openai/clip-vit-large-patch14"	)							->    None:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	device
							snake_case_		:			Any											=	CLIPTokenizerFast.from_pretrained(_A	)
							snake_case_		:			List[str]											=	[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
							snake_case_		:			Dict											=	[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
							snake_case_		:			Optional[int]											=	torchvision.transforms.Normalize(self.image_mean		,   self.image_std	)
							snake_case_		:			int											=	torchvision.transforms.Resize(224	)
							snake_case_		:			Any											=	torchvision.transforms.CenterCrop(224	)






						def UpperCAmelCase_				(      self      :      List[Any]		,   _A      :      Tuple	)							->    int:







							"""simple docstring"""


							snake_case_		:			List[str]											=	self.resize(_A	)
							snake_case_		:			Dict											=	self.center_crop(_A	)
							snake_case_		:			Optional[Any]											=	self.normalize(_A	)
							return images






						def __call__(      self      :      List[Any]		,   _A      :      Dict=None		,   _A      :      Optional[Any]=None		,   **_A      :      Union[str, Any]	)							->    Tuple:







							"""simple docstring"""


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






						def __init__(      self      :      Union[str, Any]		,   _A      :      str=10		,   _A      :      Optional[int]=0.0_1		,   _A      :      Optional[int]=None		,   _A      :      Tuple=None		,   _A      :      List[Any]=None		,   _A      :      Tuple=None		,   _A      :      int=None		,   _A      :      Any=None		,   _A      :      Optional[int]=False		,   _A      :      Optional[Any]=True		,   _A      :      Tuple="image"		,   _A      :      Tuple=True		,   _A      :      Any=False		,   _A      :      Optional[int]=False		,   _A      :      str=False		,   )							->    None:







							"""simple docstring"""


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

							snake_case_		:			Tuple											=	iterations
							snake_case_		:			Tuple											=	lr
							snake_case_		:			int											=	log
							snake_case_		:			int											=	make_grid
							snake_case_		:			Dict											=	return_val
							snake_case_		:			Optional[int]											=	quantize
							snake_case_		:			Tuple											=	self.vqgan.decoder.z_shape






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







							"""simple docstring"""


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







							"""simple docstring"""


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






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







							"""simple docstring"""


							snake_case_		:			Any											=	self.latent.detach().requires_grad_()
							snake_case_		:			int											=	base_latent + transform_vector
							if self.quantize:
								snake_case_						,*snake_case_		:			Dict											=	self.vqgan.quantize(_A	)
							else:
								snake_case_		:			int											=	trans_latent
							return self.vqgan.decode(_A	)






						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[int]		,   _A      :      Optional[int]		,   _A      :      List[Any]=None	)							->    Any:







							"""simple docstring"""


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






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







							"""simple docstring"""


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






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







							"""simple docstring"""


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

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







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






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







							"""simple docstring"""


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






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







							"""simple docstring"""


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

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

							snake_case_		:			int											=	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"""	)	)








import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece.model""")
_SCREAMING_SNAKE_CASE      	=		get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_SCREAMING_SNAKE_CASE      	=		"""pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			str       =       CamembertTokenizer
						__magic_name__:			Optional[Any]       =       CamembertTokenizerFast
						__magic_name__:			Optional[int]       =       True
						__magic_name__:			str       =       True






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







							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_		:			Union[str, Any]											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	'<pad>'
							snake_case_		:			int											=	1

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






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[Any]											=	list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]		,   '<s>NOTUSED'	)
							self.assertEqual(vocab_keys[1]		,   '<pad>'	)
							self.assertEqual(vocab_keys[-1]		,   '<mask>'	)
							self.assertEqual(len(_A	)		,   1004	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size		,   1005	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			Any											=	CamembertTokenizer(_A	)
							tokenizer.save_pretrained(self.tmpdirname	)
							snake_case_		:			List[Any]											=	CamembertTokenizerFast.from_pretrained(self.tmpdirname	)

							snake_case_		:			str											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Dict											=	tokenizer.encode(_A	)
							snake_case_		:			Tuple											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			List[Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			Any											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							# <unk> tokens are not the same for `rust` than for `slow`.
							# Because spm gives back raw token instead of `unk` in EncodeAsPieces
							# tokens = tokenizer.tokenize(sequence)
							snake_case_		:			Union[str, Any]											=	tokenizer.convert_ids_to_tokens(_A	)
							snake_case_		:			Optional[Any]											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)






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







							"""simple docstring"""


							if not self.test_rust_tokenizer:
								return

							snake_case_		:			Union[str, Any]											=	self.get_tokenizer()
							snake_case_		:			Any											=	self.get_rust_tokenizer()

							snake_case_		:			Union[str, Any]											=	'I was born in 92000, and this is falsé.'

							snake_case_		:			Optional[int]											=	tokenizer.tokenize(_A	)
							snake_case_		:			Dict											=	rust_tokenizer.tokenize(_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Union[str, Any]											=	tokenizer.encode(_A		,   add_special_tokens=_A	)
							snake_case_		:			str											=	rust_tokenizer.encode(_A		,   add_special_tokens=_A	)
							self.assertListEqual(_A		,   _A	)

							snake_case_		:			Optional[int]											=	self.get_rust_tokenizer()
							snake_case_		:			int											=	tokenizer.encode(_A	)
							snake_case_		:			int											=	rust_tokenizer.encode(_A	)
							self.assertListEqual(_A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	{'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 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]]}  # noqa: E501
							# fmt: on

							# camembert is a french model. So we also use french texts.
							snake_case_		:			Dict											=	[
							    '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=_A		,   model_name='camembert-base'		,   revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,   sequences=_A		,   )

