'''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   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""






		def __init__(   self		,						__A = "cpu"		,						__A = "openai/clip-vit-large-patch14"				) ->     None:
								_lowerCAmelCase										=device
								_lowerCAmelCase										=CLIPTokenizerFast.from_pretrained(__A				)
								_lowerCAmelCase										=[0.48_145_466, 0.4_578_275, 0.40_821_073]
								_lowerCAmelCase										=[0.26_862_954, 0.26_130_258, 0.27_577_711]
								_lowerCAmelCase										=torchvision.transforms.Normalize(self.image_mean		,						self.image_std				)
								_lowerCAmelCase										=torchvision.transforms.Resize(224				)
								_lowerCAmelCase										=torchvision.transforms.CenterCrop(224				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[str]:
								_lowerCAmelCase										=self.resize(__A				)
								_lowerCAmelCase										=self.center_crop(__A				)
								_lowerCAmelCase										=self.normalize(__A				)
								return images






		def __call__(   self		,						__A=None		,						__A=None		,						**__A				) ->     List[Any]:
								_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   SCREAMING_SNAKE_CASE			(						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:
								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				) ->     Dict:
								_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				) ->     List[Any]:
								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=256				).to(self.device				)
								_lowerCAmelCase										=preprocess_vqgan(__A				)
								_lowerCAmelCase						,				*_lowerCAmelCase										=self.vqgan.encode(__A				)
								return z






		def 		UpperCamelCase__       (   self		,						__A				) ->     Any:
								_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:
								_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				) ->     List[Any]:
								_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:
								_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				) ->     Tuple:
								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((256, 256)				)
														wandb.log('Original Image'		,						wandb.Image(__A				)				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								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		,						) ->     Dict:
								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'''				)				)






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=os.path.join(args.tf_model_dir			,      'parameters.json'      )
						_lowerCAmelCase										=json.loads(open(a__      ).read()      )
						if not params:
												raise ValueError(
												    F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''      )
						if not args.output.endswith('.pt'      ):
												_lowerCAmelCase										=args.output + '.pt'
						_lowerCAmelCase										=OrderedDict()
						with tf.device('/CPU:0'      ):
												_lowerCAmelCase										=tf.train.load_checkpoint(args.tf_model_dir      )
												_lowerCAmelCase										=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		_lowerCAmelCase										=reader.get_tensor(a__      ).astype(np.floataa      )
																		if key_name.endswith('/adam_m'      ) or key_name.endswith('/adam_v'      ):
																								continue
																		if key_name.startswith('pasts/'      ):
																								if key_name.startswith('pasts/mlp'      ):
																														_lowerCAmelCase										=int(key_name[9]      )
																								elif key_name.startswith('pasts/out'      ):
																														_lowerCAmelCase										=8
																								_lowerCAmelCase										='model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/moe'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/switch_gating/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/softmlp/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/wo/kernel'      ) or key_name.endswith('/wi/kernel'      ):
																														_lowerCAmelCase										=key_name[-9:-7]
																														for i in range(1_6      ):
																																				_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
																																				_lowerCAmelCase										=(
																																				    vnp[i].transpose([1, 0]      ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/mlp'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/p1/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p1/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wi.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.weight' % player
																														_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/p2/bias'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.mlp.wo.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/ln'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.feed_forward.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/att'      ):
																								_lowerCAmelCase										=int(key_name[9:].split('/'      )[0]      )
																								if key_name.endswith('/qkv/kernel'      ):
																														_lowerCAmelCase										=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														_lowerCAmelCase										=state[:, 0, :, :]
																														_lowerCAmelCase										=state[:, 1, :, :]
																														_lowerCAmelCase										=state[:, 2, :, :]
																														_lowerCAmelCase										=(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
																														    .transpose([1, 0]      )
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/o/kernel'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
																														_lowerCAmelCase										=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/an'      ):
																								_lowerCAmelCase										=int(key_name[8:].split('/'      )[0]      )
																								if key_name.endswith('/b'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.bias' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																								elif key_name.endswith('/g'      ):
																														_lowerCAmelCase										='model.blocks.%d.self_attn.norm.weight' % player
																														_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif (
																		    key_name.startswith('model/wte'      )
																		    or key_name.startswith('model/wpe'      )
																		    or key_name.startswith('model/ete'      )
																		):
																								_lowerCAmelCase										={'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
																								    key_name[-3:]
																								]
																								_lowerCAmelCase										='model.%s.weight' % nlayer
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=torch.tensor(a__      )
																								if key_name.startswith('model/wte'      ):
																														_lowerCAmelCase										='lm_head.weight'
																														_lowerCAmelCase										=vnp.copy()  # same in embedded
																														_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name.startswith('model/wob'      ):
																								_lowerCAmelCase										='final_logits_bias'
																								_lowerCAmelCase										=vnp.copy()  # same in embedded
																								_lowerCAmelCase										=state.reshape((1, -1)      )
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense/kernel":
																								_lowerCAmelCase										='model.last_project.weight'
																								_lowerCAmelCase										=vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								_lowerCAmelCase										=torch.tensor(a__      )
																		elif key_name == "model/dense_1/bias":
																								_lowerCAmelCase										='model.last_project.bias'
																								_lowerCAmelCase										=vnp.copy()  # same because it is one dimensional
																								_lowerCAmelCase										=torch.tensor(a__      )
						torch.save(a__			,      args.output      )


if __name__ == "__main__":
							lowercase_     		= argparse.ArgumentParser(
							    description='''model converter.''', formatter_class=argparse.ArgumentDefaultsHelpFormatter
							)
							parser.add_argument('''--tf_model_dir''', metavar='''PATH''', type=str, required=True, help='''import model''')
							parser.add_argument('''--output''', metavar='''PATH''', type=str, required=True, help='''output model''')
							lowercase_     		= parser.parse_args()
							convert_tf_gptsan_to_pt(args)





