






"""simple docstring"""

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							:





		'''simple docstring'''






		def __init__(  self				:      List[Any]		,     a_				:      Optional[Any] = "cpu"		,     a_				:      int = "openai/clip-vit-large-patch14"      ):
				lowerCAmelCase_  :				List[str]   							=     device
				lowerCAmelCase_  :				Any   							=     CLIPTokenizerFast.from_pretrained(UpperCamelCase_      )
				lowerCAmelCase_  :				Union[str, Any]   							=     [0.48145466, 0.4578275, 0.40821073]
				lowerCAmelCase_  :				int   							=     [0.26862954, 0.26130258, 0.27577711]
				lowerCAmelCase_  :				List[Any]   							=     torchvision.transforms.Normalize(self.image_mean		,     self.image_std      )
				lowerCAmelCase_  :				List[str]   							=     torchvision.transforms.Resize(2_24      )
				lowerCAmelCase_  :				Optional[int]   							=     torchvision.transforms.CenterCrop(2_24      )





		def   lowerCamelCase			(  self				:      Optional[Any]		,     a_				:      Tuple      ):
				lowerCAmelCase_  :				str   							=     self.resize(UpperCamelCase_      )
				lowerCAmelCase_  :				Tuple   							=     self.center_crop(UpperCamelCase_      )
				lowerCAmelCase_  :				int   							=     self.normalize(UpperCamelCase_      )
				return images





		def __call__(  self				:      List[Any]		,     a_				:      Dict=None		,     a_				:      Any=None		,     **a_				:      List[str]      ):
				lowerCAmelCase_  :				Any   							=     self.tokenizer(text=UpperCamelCase_		,     **UpperCamelCase_      )
				lowerCAmelCase_  :				int   							=     self.preprocess_img(UpperCamelCase_      )
				lowerCAmelCase_  :				str   							=     {key: value.to(self.device      ) for (key, value) in encoding.items()}
				return encoding







class 						__lowerCamelCase							(					nn.Module     ):





		'''simple docstring'''






		def __init__(  self				:      Optional[int]		,     a_				:      Tuple=10		,     a_				:      Any=0.01		,     a_				:      List[Any]=None		,     a_				:      List[Any]=None		,     a_				:      Union[str, Any]=None		,     a_				:      Optional[Any]=None		,     a_				:      Union[str, Any]=None		,     a_				:      Union[str, Any]=None		,     a_				:      Optional[Any]=False		,     a_				:      str=True		,     a_				:      Optional[int]="image"		,     a_				:      Optional[int]=True		,     a_				:      str=False		,     a_				:      Tuple=False		,     a_				:      List[Any]=False		,     ):
				super().__init__()
				lowerCAmelCase_  :				Any   							=     None
				lowerCAmelCase_  :				Any   							=     device if device else get_device()
				if vqgan:
						lowerCAmelCase_  :				List[Any]   							=     vqgan
				else:
						lowerCAmelCase_  :				Optional[int]   							=     load_vqgan(self.device		,     conf_path=UpperCamelCase_		,     ckpt_path=UpperCamelCase_      )
				self.vqgan.eval()
				if clip:
						lowerCAmelCase_  :				Union[str, Any]   							=     clip
				else:
						lowerCAmelCase_  :				Optional[Any]   							=     CLIPModel.from_pretrained("openai/clip-vit-base-patch32"      )
				self.clip.to(self.device      )
				lowerCAmelCase_  :				Tuple   							=     ProcessorGradientFlow(device=self.device      )

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





		def   lowerCamelCase			(  self				:      Dict		,     a_				:      List[Any]=None		,     a_				:      Union[str, Any]=None		,     a_				:      int=5		,     a_				:      List[Any]=True      ):
				lowerCAmelCase_  :				int   							=     []
				if output_path is None:
						lowerCAmelCase_  :				Optional[int]   							=     './animation.gif'
				if input_path is None:
						lowerCAmelCase_  :				Optional[int]   							=     self.save_path
				lowerCAmelCase_  :				List[str]   							=     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_  :				List[Any]   							=     total_duration / len(UpperCamelCase_      )
				lowerCAmelCase_  :				Tuple   							=     [frame_duration] * len(UpperCamelCase_      )
				if extend_frames:
						lowerCAmelCase_  :				Optional[int]   							=     1.5
						lowerCAmelCase_  :				List[Any]   							=     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   lowerCamelCase			(  self				:      Any		,     a_				:      str=None		,     a_				:      Tuple=None      ):
				if not (path or img):
						raise ValueError("Input either path or tensor"      )
				if img is not None:
						raise NotImplementedError
				lowerCAmelCase_  :				int   							=     preprocess(Image.open(UpperCamelCase_      )		,     target_image_size=2_56      ).to(self.device      )
				lowerCAmelCase_  :				Optional[int]   							=     preprocess_vqgan(UpperCamelCase_      )
				lowerCAmelCase_  :				List[str]   							=     self.vqgan.encode(UpperCamelCase_      )
				return z





		def   lowerCamelCase			(  self				:      int		,     a_				:      int      ):
				lowerCAmelCase_  :				Optional[Any]   							=     self.latent.detach().requires_grad_()
				lowerCAmelCase_  :				Dict   							=     base_latent + transform_vector
				if self.quantize:
						lowerCAmelCase_  :				Union[str, Any]   							=     self.vqgan.quantize(UpperCamelCase_      )
				else:
						lowerCAmelCase_  :				Dict   							=     trans_latent
				return self.vqgan.decode(UpperCamelCase_      )





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





		def   lowerCamelCase			(  self				:      Optional[int]		,     a_				:      List[str]		,     a_				:      Union[str, Any]		,     a_				:      Dict      ):
				lowerCAmelCase_  :				Optional[int]   							=     self._get_clip_similarity(pos_prompts["prompts"]		,     UpperCamelCase_		,     weights=(1 / pos_prompts["weights"])      )
				if neg_prompts:
						lowerCAmelCase_  :				List[str]   							=     self._get_clip_similarity(neg_prompts["prompts"]		,     UpperCamelCase_		,     weights=neg_prompts["weights"]      )
				else:
						lowerCAmelCase_  :				Any   							=     torch.tensor([1]		,     device=self.device      )
				lowerCAmelCase_  :				Tuple   							=     -torch.log(UpperCamelCase_      ) + torch.log(UpperCamelCase_      )
				return loss





		def   lowerCamelCase			(  self				:      Optional[int]		,     a_				:      Optional[Any]		,     a_				:      Any		,     a_				:      Optional[Any]      ):
				lowerCAmelCase_  :				int   							=     torch.randn_like(self.latent		,     requires_grad=UpperCamelCase_		,     device=self.device      )
				lowerCAmelCase_  :				List[str]   							=     torch.optim.Adam([vector]		,     lr=self.lr      )

				for i in range(self.iterations      ):
						optim.zero_grad()
						lowerCAmelCase_  :				Any   							=     self._add_vector(UpperCamelCase_      )
						lowerCAmelCase_  :				int   							=     loop_post_process(UpperCamelCase_      )
						lowerCAmelCase_  :				List[Any]   							=     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   lowerCamelCase			(  self				:      Optional[int]		,     a_				:      str		,     a_				:      List[str]		,     a_				:      Optional[int]      ):
				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_  :				Tuple   							=     image.resize((2_56, 2_56)      )
						wandb.log("Original Image"		,     wandb.Image(UpperCamelCase_      )      )





		def   lowerCamelCase			(  self				:      Tuple		,     a_				:      Tuple      ):
				if not prompts:
						return []
				lowerCAmelCase_  :				Optional[int]   							=     []
				lowerCAmelCase_  :				Any   							=     []
				if isinstance(UpperCamelCase_		,     UpperCamelCase_      ):
						lowerCAmelCase_  :				Optional[Any]   							=     [prompt.strip() for prompt in prompts.split("|"      )]
				for prompt in prompts:
						if isinstance(UpperCamelCase_		,     (tuple, list)      ):
								lowerCAmelCase_  :				int   							=     prompt[0]
								lowerCAmelCase_  :				Optional[int]   							=     float(prompt[1]      )
						elif ":" in prompt:
								lowerCAmelCase_  :				Tuple   							=     prompt.split(":"      )
								lowerCAmelCase_  :				Dict   							=     float(UpperCamelCase_      )
						else:
								lowerCAmelCase_  :				int   							=     prompt
								lowerCAmelCase_  :				Tuple   							=     1.0
						processed_prompts.append(UpperCamelCase_      )
						weights.append(UpperCamelCase_      )
				return {
				    "prompts": processed_prompts,
				    "weights": torch.tensor(UpperCamelCase_		,     device=self.device      ),
				}





		def   lowerCamelCase			(  self				:      int		,     a_				:      Union[str, Any]		,     a_				:      Optional[Any]=None		,     a_				:      Optional[Any]=None		,     a_				:      List[str]=True		,     a_				:      Optional[Any]=False		,     a_				:      Tuple=True		,     a_				:      Optional[int]=True		,     a_				:      Union[str, Any]=None		,     ):
				if image_path:
						lowerCAmelCase_  :				Tuple   							=     self._get_latent(UpperCamelCase_      )
				else:
						lowerCAmelCase_  :				Any   							=     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_  :				str   							=     self.process_prompts(UpperCamelCase_      )
				lowerCAmelCase_  :				List[Any]   							=     self.process_prompts(UpperCamelCase_      )
				if save_final and save_path is None:
						lowerCAmelCase_  :				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_  :				List[Any]   							=     save_path

				lowerCAmelCase_  :				Optional[int]   							=     self.vqgan.decode(self.latent      )[0]
				if show_intermediate:
						print("Original Image"      )
						show_pil(custom_to_pil(UpperCamelCase_      )      )

				lowerCAmelCase_  :				List[str]   							=     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 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


UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece.model')
UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

UpperCamelCase__          =	'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizer
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizerFast
      UpperCamelCase							:   str           =     True
      UpperCamelCase							:   Tuple           =     True



      def  __snake_case							(   self      ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__	:      int     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'<pad>'
          UpperCAmelCase__	:      Tuple     							=					1

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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      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(UpperCamelCase_      )     ,	1_004      )



      def  __snake_case							(   self      ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,	1_005      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )
          UpperCAmelCase__	:      str     							=					CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

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

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

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

          # <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)
          UpperCAmelCase__	:      List[Any]     							=					tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase__	:      Any     							=					self.get_tokenizer()
          UpperCAmelCase__	:      List[str]     							=					self.get_rust_tokenizer()

          UpperCAmelCase__	:      List[str]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      List[str]     							=					tokenizer.tokenize(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					self.get_rust_tokenizer()
          UpperCAmelCase__	:      Union[str, Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          # fmt: off
          UpperCAmelCase__	:      Any     							=					{'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 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, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 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.
          UpperCAmelCase__	:      Union[str, Any]     							=					[
              '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=UpperCamelCase_     ,	model_name='camembert-base'     ,	revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'     ,	sequences=UpperCamelCase_     ,	)

