from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)



@flax.struct.dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42





@flax_register_to_config
class  UpperCamelCase				( nn.Module   ,  __lowercase   ,  __lowercase ):

     '''simple docstring'''

     A_										=  32
     A_										=  4
     A_										=  4
     A_										=  (
         'CrossAttnDownBlock2D',
         'CrossAttnDownBlock2D',
         'CrossAttnDownBlock2D',
         'DownBlock2D',
     )
     A_										=  ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D')
     A_										=  False
     A_										=  (320, 640, 1_280, 1_280)
     A_										=  2
     A_										=  8
     A_										=  None
     A_										=  1_280
     A_										=  0.0
     A_										=  False
     A_										=  jnp.floataa
     A_										=  True
     A_										=  0
     A_										=  False



     def 		UpperCamelCase_						(					self				,				A_						) ->				FrozenDict:






           """simple docstring"""
           _lowerCamelCase			    =				(1, self.in_channels, self.sample_size, self.sample_size)
           _lowerCamelCase			    =				jnp.zeros(A_				,				dtype=jnp.floataa						)
           _lowerCamelCase			    =				jnp.ones((1,)				,				dtype=jnp.intaa						)
           _lowerCamelCase			    =				jnp.zeros((1, 1, self.cross_attention_dim)				,				dtype=jnp.floataa						)

           _lowerCamelCase    ,					_lowerCamelCase			    =				jax.random.split(A_						)
           _lowerCamelCase			    =				{'''params''': params_rng, '''dropout''': dropout_rng}

           return self.init(A_				,				A_				,				A_				,				A_						)["params"]



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.block_out_channels
           _lowerCamelCase			    =				block_out_channels[0] * 4

           if self.num_attention_heads is not None:
                 raise ValueError(
                     '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'''						)

           # If `num_attention_heads` is not defined (which is the case for most models)
           # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
           # The reason for this behavior is to correct for incorrectly named variables that were introduced
           # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
           # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
           # which is why we correct for the naming here.
           _lowerCamelCase			    =				self.num_attention_heads or self.attention_head_dim

           # input
           _lowerCamelCase			    =				nn.Conv(
               block_out_channels[0]				,				kernel_size=(3, 3)				,				strides=(1, 1)				,				padding=((1, 1), (1, 1))				,				dtype=self.dtype				,				)

           # time
           _lowerCamelCase			    =				FlaxTimesteps(
               block_out_channels[0]				,				flip_sin_to_cos=self.flip_sin_to_cos				,				freq_shift=self.config.freq_shift						)
           _lowerCamelCase			    =				FlaxTimestepEmbedding(A_				,				dtype=self.dtype						)

           _lowerCamelCase			    =				self.only_cross_attention
           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				(only_cross_attention,) * len(self.down_block_types						)

           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				(num_attention_heads,) * len(self.down_block_types						)

           # down
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				block_out_channels[0]
           for i, down_block_type in enumerate(self.down_block_types						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				block_out_channels[i]
                 _lowerCamelCase			    =				i == len(A_						) - 1

                 if down_block_type == "CrossAttnDownBlock2D":
                       _lowerCamelCase			    =				FlaxCrossAttnDownBlockaD(
                           in_channels=A_				,				out_channels=A_				,				dropout=self.dropout				,				num_layers=self.layers_per_block				,				num_attention_heads=num_attention_heads[i]				,				add_downsample=not is_final_block				,				use_linear_projection=self.use_linear_projection				,				only_cross_attention=only_cross_attention[i]				,				use_memory_efficient_attention=self.use_memory_efficient_attention				,				dtype=self.dtype				,				)
                 else:
                       _lowerCamelCase			    =				FlaxDownBlockaD(
                           in_channels=A_				,				out_channels=A_				,				dropout=self.dropout				,				num_layers=self.layers_per_block				,				add_downsample=not is_final_block				,				dtype=self.dtype				,				)

                 down_blocks.append(A_						)
           _lowerCamelCase			    =				down_blocks

           # mid
           _lowerCamelCase			    =				FlaxUNetMidBlockaDCrossAttn(
               in_channels=block_out_channels[-1]				,				dropout=self.dropout				,				num_attention_heads=num_attention_heads[-1]				,				use_linear_projection=self.use_linear_projection				,				use_memory_efficient_attention=self.use_memory_efficient_attention				,				dtype=self.dtype				,				)

           # up
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				list(reversed(A_						)						)
           _lowerCamelCase			    =				reversed_block_out_channels[0]
           for i, up_block_type in enumerate(self.up_block_types						):
                 _lowerCamelCase			    =				output_channel
                 _lowerCamelCase			    =				reversed_block_out_channels[i]
                 _lowerCamelCase			    =				reversed_block_out_channels[min(i + 1				,				len(A_						) - 1						)]

                 _lowerCamelCase			    =				i == len(A_						) - 1

                 if up_block_type == "CrossAttnUpBlock2D":
                       _lowerCamelCase			    =				FlaxCrossAttnUpBlockaD(
                           in_channels=A_				,				out_channels=A_				,				prev_output_channel=A_				,				num_layers=self.layers_per_block + 1				,				num_attention_heads=reversed_num_attention_heads[i]				,				add_upsample=not is_final_block				,				dropout=self.dropout				,				use_linear_projection=self.use_linear_projection				,				only_cross_attention=only_cross_attention[i]				,				use_memory_efficient_attention=self.use_memory_efficient_attention				,				dtype=self.dtype				,				)
                 else:
                       _lowerCamelCase			    =				FlaxUpBlockaD(
                           in_channels=A_				,				out_channels=A_				,				prev_output_channel=A_				,				num_layers=self.layers_per_block + 1				,				add_upsample=not is_final_block				,				dropout=self.dropout				,				dtype=self.dtype				,				)

                 up_blocks.append(A_						)
                 _lowerCamelCase			    =				output_channel
           _lowerCamelCase			    =				up_blocks

           # out
           _lowerCamelCase			    =				nn.GroupNorm(num_groups=32				,				epsilon=1E-5						)
           _lowerCamelCase			    =				nn.Conv(
               self.out_channels				,				kernel_size=(3, 3)				,				strides=(1, 1)				,				padding=((1, 1), (1, 1))				,				dtype=self.dtype				,				)





     def __call__(					self				,				A_				,				A_				,				A_				,				A_=None				,				A_=None				,				A_ = True				,				A_ = False				,				) ->				Union[FlaxUNetaDConditionOutput, Tuple]:






           """simple docstring"""
           # 1. time
           if not isinstance(A_				,				jnp.ndarray						):
                 _lowerCamelCase			    =				jnp.array([timesteps]				,				dtype=jnp.intaa						)
           elif isinstance(A_				,				jnp.ndarray						) and len(timesteps.shape						) == 0:
                 _lowerCamelCase			    =				timesteps.astype(dtype=jnp.floataa						)
                 _lowerCamelCase			    =				jnp.expand_dims(A_				,				0						)

           _lowerCamelCase			    =				self.time_proj(A_						)
           _lowerCamelCase			    =				self.time_embedding(A_						)

           # 2. pre-process
           _lowerCamelCase			    =				jnp.transpose(A_				,				(0, 2, 3, 1)						)
           _lowerCamelCase			    =				self.conv_in(A_						)

           # 3. down
           _lowerCamelCase			    =				(sample,)
           for down_block in self.down_blocks:
                 if isinstance(A_				,				A_						):
                       _lowerCamelCase    ,					_lowerCamelCase			    =				down_block(A_				,				A_				,				A_				,				deterministic=not train						)
                 else:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				down_block(A_				,				A_				,				deterministic=not train						)
                 down_block_res_samples += res_samples

           if down_block_additional_residuals is not None:
                 _lowerCamelCase			    =				()

                 for down_block_res_sample, down_block_additional_residual in zip(
                     A_				,				A_						):
                       down_block_res_sample += down_block_additional_residual
                       new_down_block_res_samples += (down_block_res_sample,)

                 _lowerCamelCase			    =				new_down_block_res_samples

           # 4. mid
           _lowerCamelCase			    =				self.mid_block(A_				,				A_				,				A_				,				deterministic=not train						)

           if mid_block_additional_residual is not None:
                 sample += mid_block_additional_residual

           # 5. up
           for up_block in self.up_blocks:
                 _lowerCamelCase			    =				down_block_res_samples[-(self.layers_per_block + 1) :]
                 _lowerCamelCase			    =				down_block_res_samples[: -(self.layers_per_block + 1)]
                 if isinstance(A_				,				A_						):
                       _lowerCamelCase			    =				up_block(
                           A_				,				temb=A_				,				encoder_hidden_states=A_				,				res_hidden_states_tuple=A_				,				deterministic=not train				,				)
                 else:
                       _lowerCamelCase			    =				up_block(A_				,				temb=A_				,				res_hidden_states_tuple=A_				,				deterministic=not train						)

        # 6. post-process
           _lowerCamelCase			    =				self.conv_norm_out(A_						)
           _lowerCamelCase			    =				nn.silu(A_						)
           _lowerCamelCase			    =				self.conv_out(A_						)
           _lowerCamelCase			    =				jnp.transpose(A_				,				(0, 3, 1, 2)						)

           if not return_dict:
                 return (sample,)

           return FlaxUNetaDConditionOutput(sample=A_						)
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				,				A_ = "cpu"				,				A_ = "openai/clip-vit-large-patch14"						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				device
           _lowerCamelCase			    =				CLIPTokenizerFast.from_pretrained(A_						)
           _lowerCamelCase			    =				[0.48145466, 0.4578275, 0.40821073]
           _lowerCamelCase			    =				[0.26862954, 0.26130258, 0.27577711]
           _lowerCamelCase			    =				torchvision.transforms.Normalize(self.image_mean				,				self.image_std						)
           _lowerCamelCase			    =				torchvision.transforms.Resize(2_24						)
           _lowerCamelCase			    =				torchvision.transforms.CenterCrop(2_24						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.resize(A_						)
           _lowerCamelCase			    =				self.center_crop(A_						)
           _lowerCamelCase			    =				self.normalize(A_						)
           return images





     def __call__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.tokenizer(text=A_				,				**A_						)
           _lowerCamelCase			    =				self.preprocess_img(A_						)
           _lowerCamelCase			    =				{key: value.to(self.device						) for (key, value) in encoding.items()}
           return encoding





class  UpperCamelCase				( nn.Module ):

     '''simple docstring'''

     def __init__(					self				,				A_=10				,				A_=0.01				,				A_=None				,				A_=None				,				A_=None				,				A_=None				,				A_=None				,				A_=None				,				A_=False				,				A_=True				,				A_="image"				,				A_=True				,				A_=False				,				A_=False				,				A_=False				,				) ->				None:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				device if device else get_device()
           if vqgan:
                 _lowerCamelCase			    =				vqgan
           else:
                 _lowerCamelCase			    =				load_vqgan(self.device				,				conf_path=A_				,				ckpt_path=A_						)
           self.vqgan.eval()
           if clip:
                 _lowerCamelCase			    =				clip
           else:
                 _lowerCamelCase			    =				CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''						)
           self.clip.to(self.device						)
           _lowerCamelCase			    =				ProcessorGradientFlow(device=self.device						)

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



     def 		UpperCamelCase_						(					self				,				A_=None				,				A_=None				,				A_=5				,				A_=True						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           if output_path is None:
                 _lowerCamelCase			    =				'''./animation.gif'''
           if input_path is None:
                 _lowerCamelCase			    =				self.save_path
           _lowerCamelCase			    =				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?)'''						)
           _lowerCamelCase			    =				total_duration / len(A_						)
           _lowerCamelCase			    =				[frame_duration] * len(A_						)
           if extend_frames:
                 _lowerCamelCase			    =				1.5
                 _lowerCamelCase			    =				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				,				A_=None				,				A_=None						) ->				Union[str, Any]:






           """simple docstring"""
           if not (path or img):
                 raise ValueError('''Input either path or tensor'''						)
           if img is not None:
                 raise NotImplementedError
           _lowerCamelCase			    =				preprocess(Image.open(A_						)				,				target_image_size=2_56						).to(self.device						)
           _lowerCamelCase			    =				preprocess_vqgan(A_						)
           _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.encode(A_						)
           return z



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.latent.detach().requires_grad_()
           _lowerCamelCase			    =				base_latent + transform_vector
           if self.quantize:
                 _lowerCamelCase    ,					*_lowerCamelCase			    =				self.vqgan.quantize(A_						)
           else:
                 _lowerCamelCase			    =				trans_latent
           return self.vqgan.decode(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_=None						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.clip_preprocessor(text=A_				,				images=A_				,				return_tensors='''pt'''				,				padding=A_						)
           _lowerCamelCase			    =				self.clip(**A_						)
           _lowerCamelCase			    =				clip_outputs.logits_per_image
           if weights is not None:
                 _lowerCamelCase			    =				similarity_logits * weights
           return similarity_logits.sum()



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self._get_clip_similarity(pos_prompts['''prompts''']				,				A_				,				weights=(1 / pos_prompts['''weights'''])						)
           if neg_prompts:
                 _lowerCamelCase			    =				self._get_clip_similarity(neg_prompts['''prompts''']				,				A_				,				weights=neg_prompts['''weights''']						)
           else:
                 _lowerCamelCase			    =				torch.tensor([1]				,				device=self.device						)
           _lowerCamelCase			    =				-torch.log(A_						) + torch.log(A_						)
           return loss



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				torch.randn_like(self.latent				,				requires_grad=A_				,				device=self.device						)
           _lowerCamelCase			    =				torch.optim.Adam([vector]				,				lr=self.lr						)

           for i in range(self.iterations						):
                 optim.zero_grad()
                 _lowerCamelCase			    =				self._add_vector(A_						)
                 _lowerCamelCase			    =				loop_post_process(A_						)
                 _lowerCamelCase			    =				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				,				A_				,				A_				,				A_						) ->				Any:






           """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:
                 _lowerCamelCase			    =				Image.open(A_						)
                 _lowerCamelCase			    =				image.resize((2_56, 2_56)						)
                 wandb.log('''Original Image'''				,				wandb.Image(A_						)						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           if not prompts:
                 return []
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				[]
           if isinstance(A_				,				A_						):
                 _lowerCamelCase			    =				[prompt.strip() for prompt in prompts.split('''|'''						)]
           for prompt in prompts:
                 if isinstance(A_				,				(tuple, list)						):
                       _lowerCamelCase			    =				prompt[0]
                       _lowerCamelCase			    =				float(prompt[1]						)
                 elif ":" in prompt:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				prompt.split(''':'''						)
                       _lowerCamelCase			    =				float(A_						)
                 else:
                       _lowerCamelCase			    =				prompt
                       _lowerCamelCase			    =				1.0
                 processed_prompts.append(A_						)
                 weights.append(A_						)
           return {
               "prompts": processed_prompts,
               "weights": torch.tensor(A_				,				device=self.device						),
           }





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=True				,				A_=False				,				A_=True				,				A_=True				,				A_=None				,				) ->				str:






           """simple docstring"""
           if image_path:
                 _lowerCamelCase			    =				self._get_latent(A_						)
           else:
                 _lowerCamelCase			    =				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."
           _lowerCamelCase			    =				self.process_prompts(A_						)
           _lowerCamelCase			    =				self.process_prompts(A_						)
           if save_final and save_path is None:
                 _lowerCamelCase			    =				os.path.join('''./outputs/'''				,				'''_'''.join(pos_prompts['''prompts''']						)						)
           if not os.path.exists(A_						):
                 os.makedirs(A_						)
           else:
                 _lowerCamelCase			    =				save_path + '''_''' + get_timestamp()
                 os.makedirs(A_						)
           _lowerCamelCase			    =				save_path

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

           _lowerCamelCase			    =				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'						)						)