




"""simple docstring"""


import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      GPTSanJapaneseTokenizer
 lowercase__				      =      False
 lowercase__				      =      {"do_clean_text": False, "add_prefix_space": False}


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   super().setUp()

   # fmt: off
   _snake_case							:	Optional[int]			=						["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
   # fmt: on
   _snake_case							:	Tuple			=						{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
   _snake_case							:	Tuple			=						{"""unk_token""": """<unk>"""}

   _snake_case							:	Optional[Any]			=						os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""vocab_file"""]	)
   _snake_case							:	Optional[int]			=						os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""emoji_file"""]	)
   with open(self.vocab_file,   """w""",   encoding="""utf-8"""	) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]	)	)
   with open(self.emoji_file,   """w"""	) as emoji_writer:
     emoji_writer.write(json.dumps(a_	)	)


 def 				UpperCamelCase_     (       self:	Dict,   **a_:	List[Any]	):




   '''simple docstring'''



   kwargs.update(self.special_tokens_map	)
   return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname,   **a_	)


 def 				UpperCamelCase_     (       self:	int,   a_:	Any	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
   _snake_case							:	Optional[int]			=						"""こんにちは、世界。 \nこんばんは、世界。😀"""
   return input_text, output_text


 def 				UpperCamelCase_     (       self:	Any,   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	Any			=						self.get_input_output_texts(a_	)
   _snake_case							:	Tuple			=						tokenizer.encode(a_,   add_special_tokens=a_	)
   _snake_case							:	str			=						tokenizer.decode(a_,   clean_up_tokenization_spaces=a_	)
   return text, ids


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   pass  # TODO add if relevant


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass  # TODO add if relevant


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   pass  # TODO add if relevant


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.get_tokenizer()

   # Testing tokenization
   _snake_case							:	Tuple			=						"""こんにちは、世界。　こんばんは、㔺界。"""
   _snake_case							:	Union[str, Any]			=						["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
   _snake_case							:	List[Any]			=						tokenizer.tokenize(a_	)
   self.assertListEqual(a_,   a_	)

   # Testing conversion to ids without special tokens
   _snake_case							:	Optional[int]			=						[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
   _snake_case							:	Union[str, Any]			=						tokenizer.convert_tokens_to_ids(a_	)
   self.assertListEqual(a_,   a_	)

   # Testing conversion to ids with special tokens
   _snake_case							:	Any			=						tokens + [tokenizer.unk_token]
   _snake_case							:	List[str]			=						[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
   _snake_case							:	List[Any]			=						tokenizer.convert_tokens_to_ids(a_	)
   self.assertListEqual(a_,   a_	)


 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.get_tokenizer()

   # Testing tokenization
   _snake_case							:	Optional[int]			=						"""こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
   _snake_case							:	List[str]			=						"""こんにちは、、、、世界。こんばんは、、、、世界。"""
   _snake_case							:	Optional[Any]			=						tokenizer.encode(a_	)
   _snake_case							:	Tuple			=						tokenizer.decode(a_	)
   self.assertEqual(a_,   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""	)

   # Testing tokenization
   _snake_case							:	Optional[Any]			=						"""こんにちは、世界。"""
   _snake_case							:	List[Any]			=						"""こんばんは、㔺界。😀"""
   _snake_case							:	Tuple			=						"""こんにちは、世界。こんばんは、世界。😀"""
   _snake_case							:	str			=						tokenizer.encode(prefix_text + input_text	)
   _snake_case							:	Optional[Any]			=						tokenizer.encode("""""",   prefix_text=prefix_text + input_text	)
   _snake_case							:	Optional[int]			=						tokenizer.encode(a_,   prefix_text=a_	)
   _snake_case							:	List[Any]			=						tokenizer.decode(a_	)
   _snake_case							:	str			=						tokenizer.decode(a_	)
   _snake_case							:	Union[str, Any]			=						tokenizer.decode(a_	)
   self.assertEqual(a_,   a_	)
   self.assertEqual(a_,   a_	)
   self.assertEqual(a_,   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	str			=						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""	)

   # Testing tokenization
   _snake_case							:	Union[str, Any]			=						"""こんにちは、世界。"""
   _snake_case							:	Optional[Any]			=						"""こんばんは、㔺界。😀"""

   _snake_case							:	Dict			=						len(tokenizer.encode(a_	)	) - 2
   _snake_case							:	List[Any]			=						len(tokenizer.encode(a_	)	) - 2

   _snake_case							:	Tuple			=						[1] + [0] * (len_prefix + len_text + 1)
   _snake_case							:	Dict			=						[1] * (len_prefix + len_text + 1) + [0]
   _snake_case							:	List[Any]			=						[1] + [1] * (len_prefix) + [0] * (len_text + 1)

   _snake_case							:	List[Any]			=						tokenizer(prefix_text + input_text	).token_type_ids
   _snake_case							:	Optional[int]			=						tokenizer("""""",   prefix_text=prefix_text + input_text	).token_type_ids
   _snake_case							:	Optional[Any]			=						tokenizer(a_,   prefix_text=a_	).token_type_ids
   self.assertListEqual(a_,   a_	)
   self.assertListEqual(a_,   a_	)
   self.assertListEqual(a_,   a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""	)

   _snake_case							:	int			=						tokenizer.encode("""あンいワ"""	)
   _snake_case							:	Optional[int]			=						tokenizer.encode("""""",   prefix_text="""あンいワ"""	)
   _snake_case							:	Union[str, Any]			=						tokenizer.encode("""いワ""",   prefix_text="""あン"""	)

   self.assertEqual(tokenizer.decode(a_	),   tokenizer.decode(a_	)	)
   self.assertEqual(tokenizer.decode(a_	),   tokenizer.decode(a_	)	)
   self.assertNotEqual(a_,   a_	)
   self.assertNotEqual(a_,   a_	)
   self.assertEqual(x_token_a[1],   x_token_a[-1]	)  # SEG token
   self.assertEqual(x_token_a[1],   x_token_a[3]	)  # SEG token


 @slow
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""	)

   _snake_case							:	List[str]			=						[["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
   _snake_case							:	Dict			=						tokenizer(a_,   padding=a_	)
   _snake_case							:	List[Any]			=						tokenizer.batch_encode_plus(a_,   padding=a_	)

   # fmt: off
   _snake_case							:	Any			=						[[35_993, 8_640, 25_948, 35_998, 30_647, 35_675, 35_999, 35_999], [35_993, 10_382, 9_868, 35_998, 30_646, 9_459, 30_646, 35_675]]
   _snake_case							:	Union[str, Any]			=						[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
   _snake_case							:	Tuple			=						[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
   # fmt: on
   self.assertListEqual(x_token.input_ids,   a_	)
   self.assertListEqual(x_token.token_type_ids,   a_	)
   self.assertListEqual(x_token.attention_mask,   a_	)
   self.assertListEqual(x_token_a.input_ids,   a_	)
   self.assertListEqual(x_token_a.token_type_ids,   a_	)
   self.assertListEqual(x_token_a.attention_mask,   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass








"""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       lowercase:







 '''simple docstring'''





 def __init__(       self:	List[Any],   a_:	str = "cpu",   a_:	str = "openai/clip-vit-large-patch14"	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						device
   _snake_case							:	str			=						CLIPTokenizerFast.from_pretrained(a_	)
   _snake_case							:	Union[str, Any]			=						[0.48_145_466, 0.4_578_275, 0.40_821_073]
   _snake_case							:	Optional[int]			=						[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							:	Optional[int]			=						torchvision.transforms.Resize(224	)
   _snake_case							:	str			=						torchvision.transforms.CenterCrop(224	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.resize(a_	)
   _snake_case							:	List[Any]			=						self.center_crop(a_	)
   _snake_case							:	Optional[Any]			=						self.normalize(a_	)
   return images


 def __call__(       self:	Any,   a_:	Optional[int]=None,   a_:	str=None,   **a_:	str	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.tokenizer(text=a_,   **a_	)
   _snake_case							:	Any			=						self.preprocess_img(a_	)
   _snake_case							:	Union[str, Any]			=						{key: value.to(self.device	) for (key, value) in encoding.items()}
   return encoding







class       lowercase(  nn.Module				):







 '''simple docstring'''





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




   '''simple docstring'''



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

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


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	str=None,   a_:	Dict=None,   a_:	Dict=5,   a_:	Dict=True	):




   '''simple docstring'''



   _snake_case							:	Dict			=						[]
   if output_path is None:
     _snake_case							:	Tuple			=						"""./animation.gif"""
   if input_path is None:
     _snake_case							:	Any			=						self.save_path
   _snake_case							:	Optional[int]			=						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							:	List[Any]			=						total_duration / len(a_	)
   _snake_case							:	Optional[Any]			=						[frame_duration] * len(a_	)
   if extend_frames:
     _snake_case							:	Optional[int]			=						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:	str,   a_:	Tuple=None,   a_:	Optional[Any]=None	):




   '''simple docstring'''



   if not (path or img):
     raise ValueError("""Input either path or tensor"""	)
   if img is not None:
     raise NotImplementedError
   _snake_case							:	int			=						preprocess(Image.open(a_	),   target_image_size=256	).to(self.device	)
   _snake_case							:	int			=						preprocess_vqgan(a_	)
   _snake_case	,      *_snake_case							:	List[Any]			=						self.vqgan.encode(a_	)
   return z


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.latent.detach().requires_grad_()
   _snake_case							:	Tuple			=						base_latent + transform_vector
   if self.quantize:
     _snake_case	,      *_snake_case							:	Any			=						self.vqgan.quantize(a_	)
   else:
     _snake_case							:	List[Any]			=						trans_latent
   return self.vqgan.decode(a_	)


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	Any,   a_:	Union[str, Any],   a_:	Dict=None	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.clip_preprocessor(text=a_,   images=a_,   return_tensors="""pt""",   padding=a_	)
   _snake_case							:	Any			=						self.clip(**a_	)
   _snake_case							:	str			=						clip_outputs.logits_per_image
   if weights is not None:
     _snake_case							:	Any			=						similarity_logits * weights
   return similarity_logits.sum()


 def 				UpperCamelCase_     (       self:	Any,   a_:	Any,   a_:	List[str],   a_:	Dict	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Tuple,   a_:	Union[str, Any],   a_:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						torch.randn_like(self.latent,   requires_grad=a_,   device=self.device	)
   _snake_case							:	Dict			=						torch.optim.Adam([vector],   lr=self.lr	)

   for i in range(self.iterations	):
     optim.zero_grad()
     _snake_case							:	str			=						self._add_vector(a_	)
     _snake_case							:	List[Any]			=						loop_post_process(a_	)
     _snake_case							:	List[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:	int,   a_:	Any,   a_:	Union[str, Any],   a_:	Optional[int]	):




   '''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							:	Any			=						Image.open(a_	)
     _snake_case							:	str			=						image.resize((256, 256)	)
     wandb.log("""Original Image""",   wandb.Image(a_	)	)


 def 				UpperCamelCase_     (       self:	str,   a_:	List[Any]	):




   '''simple docstring'''



   if not prompts:
     return []
   _snake_case							:	List[str]			=						[]
   _snake_case							:	Tuple			=						[]
   if isinstance(a_,   a_	):
     _snake_case							:	Union[str, Any]			=						[prompt.strip() for prompt in prompts.split("""|"""	)]
   for prompt in prompts:
     if isinstance(a_,   (tuple, list)	):
       _snake_case							:	List[Any]			=						prompt[0]
       _snake_case							:	Optional[Any]			=						float(prompt[1]	)
     elif ":" in prompt:
       _snake_case	,      _snake_case							:	List[Any]			=						prompt.split(""":"""	)
       _snake_case							:	str			=						float(a_	)
     else:
       _snake_case							:	int			=						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:	Dict,   a_:	List[Any],   a_:	List[Any]=None,   a_:	Optional[Any]=None,   a_:	Optional[Any]=True,   a_:	Dict=False,   a_:	Optional[Any]=True,   a_:	Optional[Any]=True,   a_:	Any=None,   ):




   '''simple docstring'''



   if image_path:
     _snake_case							:	Union[str, Any]			=						self._get_latent(a_	)
   else:
     _snake_case							:	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							:	str			=						self.process_prompts(a_	)
   _snake_case							:	Dict			=						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							:	List[Any]			=						save_path + """_""" + get_timestamp()
     os.makedirs(a_	)
   _snake_case							:	Optional[Any]			=						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							:	List[Any]			=						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"	)	)


