





"""simple docstring"""




import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class      UpperCamelCase_						:




					def __init__(						self			,  lowerCAmelCase_ = "cpu"			,  lowerCAmelCase_ = "openai/clip-vit-large-patch14"					)						->							None:
										_snake_case													=			device
										_snake_case													=			CLIPTokenizerFast.from_pretrained(lowerCAmelCase__					)
										_snake_case													=			[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]
										_snake_case													=			[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]
										_snake_case													=			torchvision.transforms.Normalize(self.image_mean			,  self.image_std					)
										_snake_case													=			torchvision.transforms.Resize(224					)
										_snake_case													=			torchvision.transforms.CenterCrop(224					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Tuple:
										_snake_case													=			self.resize(lowerCAmelCase__					)
										_snake_case													=			self.center_crop(lowerCAmelCase__					)
										_snake_case													=			self.normalize(lowerCAmelCase__					)
										return images






					def __call__(						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  **lowerCAmelCase_					)						->							Any:
										_snake_case													=			self.tokenizer(text=lowerCAmelCase__			,  **lowerCAmelCase__					)
										_snake_case													=			self.preprocess_img(lowerCAmelCase__					)
										_snake_case													=			{key: value.to(self.device					) for (key, value) in encoding.items()}
										return encoding





class      UpperCamelCase_						(					nn.Module   ):




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

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




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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None					)						->							Dict:
										if not (path or img):
															raise ValueError('Input either path or tensor'					)
										if img is not None:
															raise NotImplementedError
										_snake_case													=			preprocess(Image.open(lowerCAmelCase__					)			,  target_image_size=256					).to(self.device					)
										_snake_case													=			preprocess_vqgan(lowerCAmelCase__					)
										_snake_case													=			self.vqgan.encode(lowerCAmelCase__					)
										return z




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Dict:
										_snake_case													=			self.latent.detach().requires_grad_()
										_snake_case													=			base_latent + transform_vector
										if self.quantize:
															_snake_case													=			self.vqgan.quantize(lowerCAmelCase__					)
										else:
															_snake_case													=			trans_latent
										return self.vqgan.decode(lowerCAmelCase__					)




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=None					)						->							Tuple:
										_snake_case													=			self.clip_preprocessor(text=lowerCAmelCase__			,  images=lowerCAmelCase__			,  return_tensors='pt'			,  padding=lowerCAmelCase__					)
										_snake_case													=			self.clip(**lowerCAmelCase__					)
										_snake_case													=			clip_outputs.logits_per_image
										if weights is not None:
															_snake_case													=			similarity_logits * weights
										return similarity_logits.sum()




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										_snake_case													=			self._get_clip_similarity(pos_prompts['prompts']			,  lowerCAmelCase__			,  weights=(1 / pos_prompts['weights'])					)
										if neg_prompts:
															_snake_case													=			self._get_clip_similarity(neg_prompts['prompts']			,  lowerCAmelCase__			,  weights=neg_prompts['weights']					)
										else:
															_snake_case													=			torch.tensor([1]			,  device=self.device					)
										_snake_case													=			-torch.log(lowerCAmelCase__					) + torch.log(lowerCAmelCase__					)
										return loss




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Any:
										_snake_case													=			torch.randn_like(self.latent			,  requires_grad=lowerCAmelCase__			,  device=self.device					)
										_snake_case													=			torch.optim.Adam([vector]			,  lr=self.lr					)

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




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							List[str]:
										wandb.init(reinit=lowerCAmelCase__			,  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:
															_snake_case													=			Image.open(lowerCAmelCase__					)
															_snake_case													=			image.resize((256, 256)					)
															wandb.log('Original Image'			,  wandb.Image(lowerCAmelCase__					)					)




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






					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=True			,  lowerCAmelCase_=False			,  lowerCAmelCase_=True			,  lowerCAmelCase_=True			,  lowerCAmelCase_=None			,  )						->							Tuple:
										if image_path:
															_snake_case													=			self._get_latent(lowerCAmelCase__					)
										else:
															_snake_case													=			torch.randn(self.latent_dim			,  device=self.device					)
										if self.log:
															self._init_logging(lowerCAmelCase__			,  lowerCAmelCase__			,  lowerCAmelCase__					)

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

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

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










import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase_ 							=       logging.get_logger(__name__)
logging.set_verbosity_info()





def   lowerCamelCase__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    str  )     ->	Union[str, Any]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case													=			XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )
					else:
										_snake_case													=			ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__  )
										_snake_case							,       _snake_case													=			ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__    ,      output_loading_info=UpperCamelCase__  )

					_snake_case													=			['key_proj', 'value_proj', 'query_proj']

					_snake_case													=			{
					    'self_attn': 'ngram_self_attn',
					    'cross_attn': 'encoder_attn',
					    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
					    'feed_forward_layer_norm': 'final_layer_norm',
					    'feed_forward': '',
					    'intermediate': 'fc1',
					    'output': 'fc2',
					    'key_proj': 'k_proj',
					    'query_proj': 'q_proj',
					    'value_proj': 'v_proj',
					    'word_embeddings': 'embed_tokens',
					    'embeddings_layer_norm': 'emb_layer_norm',
					    'relative_pos_embeddings': 'relative_linear',
					    'ngram_embeddings': 'ngram_input_embed',
					    'position_embeddings': 'embed_positions',
					}

					for key in loading_info["missing_keys"]:
										_snake_case													=			key.split('.'  )

										if attributes[0] == "lm_head":
															_snake_case													=			prophet
															_snake_case													=			prophet_old
										else:
															_snake_case													=			prophet.prophetnet
															_snake_case													=			prophet_old.model

										_snake_case													=			False
										for attribute in attributes:
															if attribute in mapping:
																				_snake_case													=			mapping[attribute]
																				if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ) and len(UpperCamelCase__  ) > 0:
																									_snake_case													=			attribute
															elif hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																				_snake_case													=			attribute

															if attribute == "weight":
																				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																				_snake_case													=			old_model.weight
																				logger.info(F'''{attribute} is initialized.'''  )
																				_snake_case													=			True
																				break
															elif attribute == "bias":
																				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																				_snake_case													=			old_model.bias
																				logger.info(F'''{attribute} is initialized'''  )
																				_snake_case													=			True
																				break
															elif attribute in special_keys and hasattr(UpperCamelCase__    ,      'in_proj_weight'  ):
																				_snake_case													=			old_model.in_proj_weight.shape[0] // 3
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )
																				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																				if attribute == "query_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[:embed_dim]  )

																				elif attribute == "key_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]  )
																				elif attribute == "value_proj":
																									_snake_case													=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]  )
																									_snake_case													=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]  )
																				_snake_case													=			True
																				break
															elif attribute == "position_embeddings":
																				assert (
																				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																				), "Hidden size has to match"
																				assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																				_snake_case													=			nn.Parameter(old_model.embed_positions.weight[:512, :]  )
																				_snake_case													=			True
																				break

															if attribute.isdigit():
																				_snake_case													=			model[int(UpperCamelCase__  )]
																				_snake_case													=			old_model[int(UpperCamelCase__  )]
															else:
																				_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

																				if old_attribute == "":
																									_snake_case													=			old_model
																				else:
																									if not hasattr(UpperCamelCase__    ,      UpperCamelCase__  ):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''  )
																									_snake_case													=			getattr(UpperCamelCase__    ,      UpperCamelCase__  )

										if not is_key_init:
															raise ValueError(F'''{key} was not correctly initialized!'''  )

					print(F'''Saving model to {pytorch_dump_folder_path}'''  )
					prophet.save_pretrained(UpperCamelCase__  )


if __name__ == "__main__":
					UpperCAmelCase_ 							=       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					UpperCAmelCase_ 							=       parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



