


'''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__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__ = "cpu"   ,						snake_case__ = "openai/clip-vit-large-patch14" ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							device
            _lowerCAmelCase     :				Any       =							CLIPTokenizerFast.from_pretrained(snake_case__ )
            _lowerCAmelCase     :				Optional[Any]       =							[0.4814_5466, 0.457_8275, 0.4082_1073]
            _lowerCAmelCase     :				Dict       =							[0.2686_2954, 0.2613_0258, 0.2757_7711]
            _lowerCAmelCase     :				List[str]       =							torchvision.transforms.Normalize(self.image_mean   ,						self.image_std )
            _lowerCAmelCase     :				List[Any]       =							torchvision.transforms.Resize(224 )
            _lowerCAmelCase     :				Dict       =							torchvision.transforms.CenterCrop(224 )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							self.resize(snake_case__ )
            _lowerCAmelCase     :				str       =							self.center_crop(snake_case__ )
            _lowerCAmelCase     :				Any       =							self.normalize(snake_case__ )
            return images







     def __call__(			self   ,						snake_case__=None   ,						snake_case__=None   ,						**snake_case__ ):

            '''simple docstring'''

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



     """simple docstring"""






     def __init__(			self   ,						snake_case__=10   ,						snake_case__=0.01   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__="image"   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=False   ,						snake_case__=False   ,						):

            '''simple docstring'''

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

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







     def a						(			self   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=5   ,						snake_case__=True ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__=None   ,						snake_case__=None ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__=None ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							torch.randn_like(self.latent   ,						requires_grad=snake_case__   ,						device=self.device )
            _lowerCAmelCase     :				Tuple       =							torch.optim.Adam([vector]   ,						lr=self.lr )

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







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            wandb.init(reinit=snake_case__   ,						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     :				str       =							Image.open(snake_case__ )
                   _lowerCAmelCase     :				Union[str, Any]       =							image.resize((256, 256) )
                   wandb.log('Original Image'   ,						wandb.Image(snake_case__ ) )







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__   ,						snake_case__=None   ,						snake_case__=None   ,						snake_case__=True   ,						snake_case__=False   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=None   ,						):

            '''simple docstring'''

            if image_path:
                   _lowerCAmelCase     :				Tuple       =							self._get_latent(snake_case__ )
            else:
                   _lowerCAmelCase     :				Optional[int]       =							torch.randn(self.latent_dim   ,						device=self.device )
            if self.log:
                   self._init_logging(snake_case__   ,						snake_case__   ,						snake_case__ )

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

            _lowerCAmelCase     :				Optional[Any]       =							self.vqgan.decode(self.latent )[0]
            if show_intermediate:
                   print('Original Image' )
                   show_pil(custom_to_pil(snake_case__ ) )

            _lowerCAmelCase     :				List[Any]       =							loop_post_process(snake_case__ )
            for iter, transformed_img in enumerate(self._optimize_CLIP(snake_case__   ,						snake_case__   ,						snake_case__ ) ):
                   if show_intermediate:
                          show_pil(snake_case__ )
                   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(snake_case__ )} )
            if show_final:
                   show_pil(snake_case__ )
            if save_final:
                   transformed_img.save(os.path.join(self.save_path   ,						F'iter_{iter:03d}_final.png' ) )





'''simple docstring'''







from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand






def       lowercase					(_A       ):







       """simple docstring"""







       return ConvertCommand(
           args.model_type ,    args.tf_checkpoint ,    args.pytorch_dump_output ,    args.config ,    args.finetuning_task_name       )


lowerCAmelCase     :  Optional[int]											=    """
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @staticmethod
     def a						(			snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							parser.add_parser(
                'convert'   ,						help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.'   ,						)
            train_parser.add_argument('--model_type'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Model\'s type.' )
            train_parser.add_argument(
                '--tf_checkpoint'   ,						type=snake_case__   ,						required=snake_case__   ,						help='TensorFlow checkpoint path or folder.' )
            train_parser.add_argument(
                '--pytorch_dump_output'   ,						type=snake_case__   ,						required=snake_case__   ,						help='Path to the PyTorch saved model output.' )
            train_parser.add_argument('--config'   ,						type=snake_case__   ,						default=''   ,						help='Configuration file path or folder.' )
            train_parser.add_argument(
                '--finetuning_task_name'   ,						type=snake_case__   ,						default=snake_case__   ,						help='Optional fine-tuning task name if the TF model was a finetuned model.'   ,						)
            train_parser.set_defaults(func=snake_case__ )







     def __init__(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						*snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							logging.get_logger('transformers-cli/converting' )

            self._logger.info(F'Loading model {model_type}' )
            _lowerCAmelCase     :				Any       =							model_type
            _lowerCAmelCase     :				Any       =							tf_checkpoint
            _lowerCAmelCase     :				List[str]       =							pytorch_dump_output
            _lowerCAmelCase     :				int       =							config
            _lowerCAmelCase     :				str       =							finetuning_task_name







     def a						(			self ):

            '''simple docstring'''

            if self._model_type == "albert":
                   try:
                          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "bert":
                   try:
                          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "funnel":
                   try:
                          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
                              convert_tf_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "t5":
                   try:
                          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "gpt":
                   from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
                       convert_openai_checkpoint_to_pytorch,
                   )

                   convert_openai_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "transfo_xl":
                   try:
                          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
                              convert_transfo_xl_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   if "ckpt" in self._tf_checkpoint.lower():
                          _lowerCAmelCase     :				List[Any]       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Optional[int]       =							''
                   else:
                          _lowerCAmelCase     :				int       =							self._tf_checkpoint
                          _lowerCAmelCase     :				Dict       =							''
                   convert_transfo_xl_checkpoint_to_pytorch(
                       snake_case__   ,						self._config   ,						self._pytorch_dump_output   ,						snake_case__ )
            elif self._model_type == "gpt2":
                   try:
                          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
                              convert_gpta_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            elif self._model_type == "xlnet":
                   try:
                          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
                              convert_xlnet_checkpoint_to_pytorch,
                          )
                   except ImportError:
                          raise ImportError(snake_case__ )

                   convert_xlnet_checkpoint_to_pytorch(
                       self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output   ,						self._finetuning_task_name )
            elif self._model_type == "xlm":
                   from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
                       convert_xlm_checkpoint_to_pytorch,
                   )

                   convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "lxmert":
                   from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                       convert_lxmert_checkpoint_to_pytorch,
                   )

                   convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._pytorch_dump_output )
            elif self._model_type == "rembert":
                   from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
                       convert_rembert_tf_checkpoint_to_pytorch,
                   )

                   convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint   ,						self._config   ,						self._pytorch_dump_output )
            else:
                   raise ValueError(
                       '--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )

