


'''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_  :






       def __init__( self			,     _UpperCamelCase = "cpu"			,     _UpperCamelCase = "openai/clip-vit-large-patch14"   )->      None:
            _A				=							device
            _A				=							CLIPTokenizerFast.from_pretrained(_UpperCamelCase   )
            _A				=							[0.4814_5466, 0.457_8275, 0.4082_1073]
            _A				=							[0.2686_2954, 0.2613_0258, 0.2757_7711]
            _A				=							torchvision.transforms.Normalize(self.image_mean			,     self.image_std   )
            _A				=							torchvision.transforms.Resize(224   )
            _A				=							torchvision.transforms.CenterCrop(224   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      int:
            _A				=							self.resize(_UpperCamelCase   )
            _A				=							self.center_crop(_UpperCamelCase   )
            _A				=							self.normalize(_UpperCamelCase   )
            return images






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






class        lowerCAmelCase_  (     nn.Module	):






       def __init__( self			,     _UpperCamelCase=10			,     _UpperCamelCase=0.01			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=False			,     _UpperCamelCase=True			,     _UpperCamelCase="image"			,     _UpperCamelCase=True			,     _UpperCamelCase=False			,     _UpperCamelCase=False			,     _UpperCamelCase=False			,     )->      None:
            super().__init__()
            _A				=							None
            _A				=							device if device else get_device()
            if vqgan:
                 _A				=							vqgan
            else:
                 _A				=							load_vqgan(self.device			,     conf_path=_UpperCamelCase			,     ckpt_path=_UpperCamelCase   )
            self.vqgan.eval()
            if clip:
                 _A				=							clip
            else:
                 _A				=							CLIPModel.from_pretrained('openai/clip-vit-base-patch32'   )
            self.clip.to(self.device   )
            _A				=							ProcessorGradientFlow(device=self.device   )

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






       def   UpperCamelCase						( self			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=5			,     _UpperCamelCase=True   )->      Tuple:
            _A				=							[]
            if output_path is None:
                 _A				=							'./animation.gif'
            if input_path is None:
                 _A				=							self.save_path
            _A				=							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?)'   )
            _A				=							total_duration / len(_UpperCamelCase   )
            _A				=							[frame_duration] * len(_UpperCamelCase   )
            if extend_frames:
                 _A				=							1.5
                 _A				=							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   UpperCamelCase						( self			,     _UpperCamelCase=None			,     _UpperCamelCase=None   )->      Union[str, Any]:
            if not (path or img):
                 raise ValueError('Input either path or tensor'   )
            if img is not None:
                 raise NotImplementedError
            _A				=							preprocess(Image.open(_UpperCamelCase   )			,     target_image_size=256   ).to(self.device   )
            _A				=							preprocess_vqgan(_UpperCamelCase   )
            _A		,				*_A				=							self.vqgan.encode(_UpperCamelCase   )
            return z






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      int:
            _A				=							self.latent.detach().requires_grad_()
            _A				=							base_latent + transform_vector
            if self.quantize:
                 _A		,				*_A				=							self.vqgan.quantize(_UpperCamelCase   )
            else:
                 _A				=							trans_latent
            return self.vqgan.decode(_UpperCamelCase   )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=None   )->      Optional[Any]:
            _A				=							self.clip_preprocessor(text=_UpperCamelCase			,     images=_UpperCamelCase			,     return_tensors='pt'			,     padding=_UpperCamelCase   )
            _A				=							self.clip(**_UpperCamelCase   )
            _A				=							clip_outputs.logits_per_image
            if weights is not None:
                 _A				=							similarity_logits * weights
            return similarity_logits.sum()






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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      List[Any]:
            _A				=							torch.randn_like(self.latent			,     requires_grad=_UpperCamelCase			,     device=self.device   )
            _A				=							torch.optim.Adam([vector]			,     lr=self.lr   )

            for i in range(self.iterations   ):
                 optim.zero_grad()
                 _A				=							self._add_vector(_UpperCamelCase   )
                 _A				=							loop_post_process(_UpperCamelCase   )
                 _A				=							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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      str:
            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:
                 _A				=							Image.open(_UpperCamelCase   )
                 _A				=							image.resize((256, 256)   )
                 wandb.log('Original Image'			,     wandb.Image(_UpperCamelCase   )   )






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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=True			,     _UpperCamelCase=False			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=None			,     )->      List[Any]:
            if image_path:
                 _A				=							self._get_latent(_UpperCamelCase   )
            else:
                 _A				=							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."
            _A				=							self.process_prompts(_UpperCamelCase   )
            _A				=							self.process_prompts(_UpperCamelCase   )
            if save_final and save_path is None:
                 _A				=							os.path.join('./outputs/'			,     '_'.join(pos_prompts['prompts']   )   )
            if not os.path.exists(_UpperCamelCase   ):
                 os.makedirs(_UpperCamelCase   )
            else:
                 _A				=							save_path + '_' + get_timestamp()
                 os.makedirs(_UpperCamelCase   )
            _A				=							save_path

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

            _A				=							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 flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      int:
            _A				=							[]
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=_UpperCamelCase			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

                 _A				=							FlaxTransformeraDModel(
                     in_channels=self.out_channels			,     n_heads=self.num_attention_heads			,     d_head=self.out_channels // self.num_attention_heads			,     depth=1			,     use_linear_projection=self.use_linear_projection			,     only_cross_attention=self.only_cross_attention			,     use_memory_efficient_attention=self.use_memory_efficient_attention			,     dtype=self.dtype			,     )
                 attentions.append(_UpperCamelCase   )

            _A				=							resnets
            _A				=							attentions

            if self.add_downsample:
                 _A				=							FlaxDownsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            _A				=							()

            for resnet, attn in zip(self.resnets			,     self.attentions   ):
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 output_states += (hidden_states,)

            if self.add_downsample:
                 _A				=							self.downsamplers_a(_UpperCamelCase   )
                 output_states += (hidden_states,)

            return hidden_states, output_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=_UpperCamelCase			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )
            _A				=							resnets

            if self.add_downsample:
                 _A				=							FlaxDownsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Union[str, Any]:
            _A				=							()

            for resnet in self.resnets:
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 output_states += (hidden_states,)

            if self.add_downsample:
                 _A				=							self.downsamplers_a(_UpperCamelCase   )
                 output_states += (hidden_states,)

            return hidden_states, output_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      str:
            _A				=							[]
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
                 _A				=							self.prev_output_channel if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=resnet_in_channels + res_skip_channels			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

                 _A				=							FlaxTransformeraDModel(
                     in_channels=self.out_channels			,     n_heads=self.num_attention_heads			,     d_head=self.out_channels // self.num_attention_heads			,     depth=1			,     use_linear_projection=self.use_linear_projection			,     only_cross_attention=self.only_cross_attention			,     use_memory_efficient_attention=self.use_memory_efficient_attention			,     dtype=self.dtype			,     )
                 attentions.append(_UpperCamelCase   )

            _A				=							resnets
            _A				=							attentions

            if self.add_upsample:
                 _A				=							FlaxUpsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            for resnet, attn in zip(self.resnets			,     self.attentions   ):
                 # pop res hidden states
                 _A				=							res_hidden_states_tuple[-1]
                 _A				=							res_hidden_states_tuple[:-1]
                 _A				=							jnp.concatenate((hidden_states, res_hidden_states)			,     axis=-1   )

                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            if self.add_upsample:
                 _A				=							self.upsamplers_a(_UpperCamelCase   )

            return hidden_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=True
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							[]

            for i in range(self.num_layers   ):
                 _A				=							self.in_channels if (i == self.num_layers - 1) else self.out_channels
                 _A				=							self.prev_output_channel if i == 0 else self.out_channels

                 _A				=							FlaxResnetBlockaD(
                     in_channels=resnet_in_channels + res_skip_channels			,     out_channels=self.out_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

            _A				=							resnets

            if self.add_upsample:
                 _A				=							FlaxUpsampleaD(self.out_channels			,     dtype=self.dtype   )






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      Dict:
            for resnet in self.resnets:
                 # pop res hidden states
                 _A				=							res_hidden_states_tuple[-1]
                 _A				=							res_hidden_states_tuple[:-1]
                 _A				=							jnp.concatenate((hidden_states, res_hidden_states)			,     axis=-1   )

                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            if self.add_upsample:
                 _A				=							self.upsamplers_a(_UpperCamelCase   )

            return hidden_states






class        lowerCAmelCase_  (     nn.Module	):
       __UpperCAmelCase					=42
       __UpperCAmelCase					=0.0
       __UpperCAmelCase					=1
       __UpperCAmelCase					=1
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=jnp.floataa






       def   UpperCamelCase						( self   )->      Dict:
            # there is always at least one resnet
            _A				=							[
                FlaxResnetBlockaD(
                    in_channels=self.in_channels			,     out_channels=self.in_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
            ]

            _A				=							[]

            for _ in range(self.num_layers   ):
                 _A				=							FlaxTransformeraDModel(
                     in_channels=self.in_channels			,     n_heads=self.num_attention_heads			,     d_head=self.in_channels // self.num_attention_heads			,     depth=1			,     use_linear_projection=self.use_linear_projection			,     use_memory_efficient_attention=self.use_memory_efficient_attention			,     dtype=self.dtype			,     )
                 attentions.append(_UpperCamelCase   )

                 _A				=							FlaxResnetBlockaD(
                     in_channels=self.in_channels			,     out_channels=self.in_channels			,     dropout_prob=self.dropout			,     dtype=self.dtype			,     )
                 resnets.append(_UpperCamelCase   )

            _A				=							resnets
            _A				=							attentions






       def __call__( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase=True   )->      List[Any]:
            _A				=							self.resnets[0](_UpperCamelCase			,     _UpperCamelCase   )
            for attn, resnet in zip(self.attentions			,     self.resnets[1:]   ):
                 _A				=							attn(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )
                 _A				=							resnet(_UpperCamelCase			,     _UpperCamelCase			,     deterministic=_UpperCamelCase   )

            return hidden_states





