





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowercase	: Optional[Any]       =os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowercase	: Union[str, Any]       ="""    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""

class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_           =tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir		,	"models/bert/"							)							)
         UpperCAmelCase_           =self.transformer_dir
         shutil.copy(
             os.path.join(_lowerCAmelCase		,	"src/transformers/models/bert/modeling_bert.py"							)		,	os.path.join(self.transformer_dir		,	"models/bert/modeling_bert.py"							)		,	)



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           ="src/transformers"
         shutil.rmtree(self.transformer_dir							)



      def 						lowerCAmelCase__     (						self:    List[Any]		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    Union[str, Any]		,	_lowerCAmelCase:    Any=None							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =comment + F'\nclass {class_name}(nn.Module):\n' + class_code
         if overwrite_result is not None:
            UpperCAmelCase_           =comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
         UpperCAmelCase_           =black.Mode(target_versions={black.TargetVersion.PYaa}		,	line_length=119							)
         UpperCAmelCase_           =black.format_str(_lowerCAmelCase		,	mode=_lowerCAmelCase							)
         UpperCAmelCase_           =os.path.join(self.transformer_dir		,	"new_code.py"							)
         with open(_lowerCAmelCase		,	"w"		,	newline="\n"							) as f:
            f.write(_lowerCAmelCase							)
         if overwrite_result is None:
            self.assertTrue(len(check_copies.is_copy_consistent(_lowerCAmelCase							)							) == 0							)
         else:
            check_copies.is_copy_consistent(f.name		,	overwrite=_lowerCAmelCase							)
            with open(_lowerCAmelCase		,	"r"							) as f:
               self.assertTrue(f.read()		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    List[str]							)    ->     str:
         '''simple docstring'''

         UpperCAmelCase_           =check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"							)
         self.assertEqual(_lowerCAmelCase		,	_lowerCAmelCase							)



      def 						lowerCAmelCase__     (						self:    str							)    ->     List[str]:
         '''simple docstring'''

         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	REFERENCE_CODE + "\n"		,	)

         # With no empty line at the end
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"		,	"BertLMPredictionHead"		,	_lowerCAmelCase		,	)

         # Copy consistency with rename
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	re.sub("Bert"		,	"TestModel"		,	_lowerCAmelCase							)		,	)

         # Copy consistency with a really long name
         UpperCAmelCase_           ="TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
         self.check_copy_consistency(
             F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'		,	F'{long_class_name}LMPredictionHead'		,	re.sub("Bert"		,	_lowerCAmelCase		,	_lowerCAmelCase							)		,	)

         # Copy consistency with overwrite
         self.check_copy_consistency(
             "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"		,	"TestModelLMPredictionHead"		,	_lowerCAmelCase		,	overwrite_result=re.sub("Bert"		,	"TestModel"		,	_lowerCAmelCase							)		,	)






      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     List[Any]:
         '''simple docstring'''

         UpperCAmelCase_           =check_copies.LOCALIZED_READMES["README_zh-hans.md"]

         UpperCAmelCase_           =(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
             " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
             " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
             " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
             " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
             " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
             " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
             " method has been applied to compress GPT2 into"
             " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
             " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
             " Multilingual BERT into"
             " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
             " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
             " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
             " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
             " Luong, Quoc V. Le, Christopher D. Manning."
         )
         UpperCAmelCase_           =(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )
         UpperCAmelCase_           =(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
             " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
             " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
             " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
             " method has been applied to compress GPT2 into"
             " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
             " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
             " Multilingual BERT into"
             " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
             " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
             " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
             " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
             " Christopher D. Manning 发布。\n"
         )

         UpperCAmelCase_    ,							UpperCAmelCase_           =check_copies.convert_to_localized_md(
             _lowerCAmelCase		,	_lowerCAmelCase		,	localized_readme["format_model_list"]							)

         self.assertFalse(_lowerCAmelCase							)
         self.assertEqual(_lowerCAmelCase		,	_lowerCAmelCase							)

         UpperCAmelCase_    ,							UpperCAmelCase_           =check_copies.convert_to_localized_md(
             _lowerCAmelCase		,	_lowerCAmelCase		,	localized_readme["format_model_list"]							)

         # Check whether the number of models is equal to README.md after conversion.
         self.assertTrue(_lowerCAmelCase							)

         UpperCAmelCase_           =(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
             " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
             " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
             " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
         )
         UpperCAmelCase_           =(
             "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
             " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )
         UpperCAmelCase_           =(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )

         UpperCAmelCase_    ,							UpperCAmelCase_           =check_copies.convert_to_localized_md(
             _lowerCAmelCase		,	_lowerCAmelCase		,	localized_readme["format_model_list"]							)

         # Check if the model link is synchronized.
         self.assertEqual(_lowerCAmelCase		,	_lowerCAmelCase							)






"""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   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ = "cpu" ,							lowercase_ = "openai/clip-vit-large-patch14"				)    ->       None:
          UpperCAmelCase          =       device
          UpperCAmelCase          =       CLIPTokenizerFast.from_pretrained(lowercase_				)
          UpperCAmelCase          =       [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]
          UpperCAmelCase          =       [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]
          UpperCAmelCase          =       torchvision.transforms.Normalize(self.image_mean ,							self.image_std				)
          UpperCAmelCase          =       torchvision.transforms.Resize(2_2_4				)
          UpperCAmelCase          =       torchvision.transforms.CenterCrop(2_2_4				)




      def      a_   (				self ,							lowercase_				)    ->       Any:
          UpperCAmelCase          =       self.resize(lowercase_				)
          UpperCAmelCase          =       self.center_crop(lowercase_				)
          UpperCAmelCase          =       self.normalize(lowercase_				)
          return images




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Union[str, Any]:
          UpperCAmelCase          =       self.tokenizer(text=lowercase_ ,							**lowercase_				)
          UpperCAmelCase          =       self.preprocess_img(lowercase_				)
          UpperCAmelCase          =       {key: value.to(self.device				) for (key, value) in encoding.items()}
          return encoding

class   _UpperCAmelCase				(		nn.Module				):




      def __init__(				self ,							lowercase_=1_0 ,							lowercase_=0.0_1 ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							lowercase_=False ,							lowercase_=True ,							lowercase_="image" ,							lowercase_=True ,							lowercase_=False ,							lowercase_=False ,							lowercase_=False ,							)    ->       None:
          super().__init__()
          UpperCAmelCase          =       None
          UpperCAmelCase          =       device if device else get_device()
          if vqgan:
              UpperCAmelCase          =       vqgan
          else:
              UpperCAmelCase          =       load_vqgan(self.device ,							conf_path=lowercase_ ,							ckpt_path=lowercase_				)
          self.vqgan.eval()
          if clip:
              UpperCAmelCase          =       clip
          else:
              UpperCAmelCase          =       CLIPModel.from_pretrained('openai/clip-vit-base-patch32'				)
          self.clip.to(self.device				)
          UpperCAmelCase          =       ProcessorGradientFlow(device=self.device				)

          UpperCAmelCase          =       iterations
          UpperCAmelCase          =       lr
          UpperCAmelCase          =       log
          UpperCAmelCase          =       make_grid
          UpperCAmelCase          =       return_val
          UpperCAmelCase          =       quantize
          UpperCAmelCase          =       self.vqgan.decoder.z_shape




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=5 ,							lowercase_=True				)    ->       Dict:
          UpperCAmelCase          =       []
          if output_path is None:
              UpperCAmelCase          =       './animation.gif'
          if input_path is None:
              UpperCAmelCase          =       self.save_path
          UpperCAmelCase          =       sorted(glob(input_path + '/*'				)				)
          if not len(lowercase_				):
              raise ValueError(
                  'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
                  ' function?)'				)
          if len(lowercase_				) == 1:
              print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'				)
          UpperCAmelCase          =       total_duration / len(lowercase_				)
          UpperCAmelCase          =       [frame_duration] * len(lowercase_				)
          if extend_frames:
              UpperCAmelCase          =       1.5
              UpperCAmelCase          =       3
          for file_name in paths:
              if file_name.endswith('.png'				):
                  images.append(imageio.imread(lowercase_				)				)
          imageio.mimsave(lowercase_ ,							lowercase_ ,							duration=lowercase_				)
          print(F"gif saved to {output_path}"				)




      def      a_   (				self ,							lowercase_=None ,							lowercase_=None				)    ->       List[Any]:
          if not (path or img):
              raise ValueError('Input either path or tensor'				)
          if img is not None:
              raise NotImplementedError
          UpperCAmelCase          =       preprocess(Image.open(lowercase_				) ,							target_image_size=2_5_6				).to(self.device				)
          UpperCAmelCase          =       preprocess_vqgan(lowercase_				)
          UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.encode(lowercase_				)
          return z




      def      a_   (				self ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       self.latent.detach().requires_grad_()
          UpperCAmelCase          =       base_latent + transform_vector
          if self.quantize:
              UpperCAmelCase      ,				*UpperCAmelCase          =       self.vqgan.quantize(lowercase_				)
          else:
              UpperCAmelCase          =       trans_latent
          return self.vqgan.decode(lowercase_				)




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_=None				)    ->       str:
          UpperCAmelCase          =       self.clip_preprocessor(text=lowercase_ ,							images=lowercase_ ,							return_tensors='pt' ,							padding=lowercase_				)
          UpperCAmelCase          =       self.clip(**lowercase_				)
          UpperCAmelCase          =       clip_outputs.logits_per_image
          if weights is not None:
              UpperCAmelCase          =       similarity_logits * weights
          return similarity_logits.sum()




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       List[Any]:
          UpperCAmelCase          =       self._get_clip_similarity(pos_prompts['prompts'] ,							lowercase_ ,							weights=(1 / pos_prompts['weights'])				)
          if neg_prompts:
              UpperCAmelCase          =       self._get_clip_similarity(neg_prompts['prompts'] ,							lowercase_ ,							weights=neg_prompts['weights']				)
          else:
              UpperCAmelCase          =       torch.tensor([1] ,							device=self.device				)
          UpperCAmelCase          =       -torch.log(lowercase_				) + torch.log(lowercase_				)
          return loss




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Tuple:
          UpperCAmelCase          =       torch.randn_like(self.latent ,							requires_grad=lowercase_ ,							device=self.device				)
          UpperCAmelCase          =       torch.optim.Adam([vector] ,							lr=self.lr				)

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




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       Dict:
          wandb.init(reinit=lowercase_ ,							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:
              UpperCAmelCase          =       Image.open(lowercase_				)
              UpperCAmelCase          =       image.resize((2_5_6, 2_5_6)				)
              wandb.log('Original Image' ,							wandb.Image(lowercase_				)				)




      def      a_   (				self ,							lowercase_				)    ->       Tuple:
          if not prompts:
              return []
          UpperCAmelCase          =       []
          UpperCAmelCase          =       []
          if isinstance(lowercase_ ,							lowercase_				):
              UpperCAmelCase          =       [prompt.strip() for prompt in prompts.split('|'				)]
          for prompt in prompts:
              if isinstance(lowercase_ ,							(tuple, list)				):
                  UpperCAmelCase          =       prompt[0]
                  UpperCAmelCase          =       float(prompt[1]				)
              elif ":" in prompt:
                  UpperCAmelCase      ,				UpperCAmelCase          =       prompt.split(':'				)
                  UpperCAmelCase          =       float(lowercase_				)
              else:
                  UpperCAmelCase          =       prompt
                  UpperCAmelCase          =       1.0
              processed_prompts.append(lowercase_				)
              weights.append(lowercase_				)
          return {
              "prompts": processed_prompts,
              "weights": torch.tensor(lowercase_ ,							device=self.device				),
          }




      def      a_   (				self ,							lowercase_ ,							lowercase_=None ,							lowercase_=None ,							lowercase_=True ,							lowercase_=False ,							lowercase_=True ,							lowercase_=True ,							lowercase_=None ,							)    ->       List[str]:
          if image_path:
              UpperCAmelCase          =       self._get_latent(lowercase_				)
          else:
              UpperCAmelCase          =       torch.randn(self.latent_dim ,							device=self.device				)
          if self.log:
              self._init_logging(lowercase_ ,							lowercase_ ,							lowercase_				)

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

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

          UpperCAmelCase          =       loop_post_process(lowercase_				)
          for iter, transformed_img in enumerate(self._optimize_CLIP(lowercase_ ,							lowercase_ ,							lowercase_				)				):
              if show_intermediate:
                  show_pil(lowercase_				)
              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(lowercase_				)}				)
          if show_final:
              show_pil(lowercase_				)
          if save_final:
              transformed_img.save(os.path.join(self.save_path ,							F"iter_{iter:03d}_final.png"				)				)



