
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


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




def 				a						(							snake_case__:					str						,   snake_case__:					str					):


				'''simple docstring'''
				if "xprophetnet" in prophetnet_checkpoint_path:
								lowercase_       =					XLMProphetNetForConditionalGenerationOld.from_pretrained(snake_case__					)
								lowercase_  ,	lowercase_       =					XLMProphetNetForConditionalGeneration.from_pretrained(
								    snake_case__						,   output_loading_info=snake_case__					)
				else:
								lowercase_       =					ProphetNetForConditionalGenerationOld.from_pretrained(snake_case__					)
								lowercase_  ,	lowercase_       =					ProphetNetForConditionalGeneration.from_pretrained(
								    snake_case__						,   output_loading_info=snake_case__					)

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

				lowercase_       =					{
				    '''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"]:
								lowercase_       =					key.split('''.'''					)

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

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

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

																elif attribute == "key_proj":
																				lowercase_       =					nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
																				lowercase_       =					nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
																elif attribute == "value_proj":
																				lowercase_       =					nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
																				lowercase_       =					nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
																lowercase_       =					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."
																lowercase_       =					nn.Parameter(old_model.embed_positions.weight[:512, :]					)
																lowercase_       =					True
																break

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

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

								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(snake_case__					)


if __name__ == "__main__":
						__a  			=					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.'
						)
						__a  			=					parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					argparse.ArgumentParser()
				parser.add_argument(
				    '''-m'''						,   '''--pretrained_model_name_or_path'''						,   type=snake_case__						,   default=snake_case__						,   required=snake_case__						,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''						,   )
				parser.add_argument(
				    '''-c'''						,   '''--caption'''						,   type=snake_case__						,   default='''robotic cat with wings'''						,   help='''Text used to generate images.'''						,   )
				parser.add_argument(
				    '''-n'''						,   '''--images_num'''						,   type=snake_case__						,   default=4						,   help='''How much images to generate.'''						,   )
				parser.add_argument(
				    '''-s'''						,   '''--seed'''						,   type=snake_case__						,   default=42						,   help='''Seed for random process.'''						,   )
				parser.add_argument(
				    '''-ci'''						,   '''--cuda_id'''						,   type=snake_case__						,   default=0						,   help='''cuda_id.'''						,   )
				lowercase_       =					parser.parse_args()
				return args




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Tuple						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				if not len(snake_case__					) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.'''					)

				lowercase_  ,	lowercase_       =					imgs[0].size
				lowercase_       =					Image.new('''RGB'''						,   size=(cols * w, rows * h)					)
				lowercase_  ,	lowercase_       =					grid.size

				for i, img in enumerate(snake_case__					):
								grid.paste(snake_case__						,   box=(i % cols * w, i // cols * h)					)
				return grid




def 				a						(							snake_case__:					Tuple						,   snake_case__:					Union[str, Any]="robotic cat with wings"						,   snake_case__:					Union[str, Any]=7.5						,   snake_case__:					List[str]=50						,   snake_case__:					List[Any]=1						,   snake_case__:					Optional[int]=42						,   ):


				'''simple docstring'''
				lowercase_       =					torch.Generator(pipeline.device					).manual_seed(snake_case__					)
				lowercase_       =					pipeline(
				    snake_case__						,   guidance_scale=snake_case__						,   num_inference_steps=snake_case__						,   generator=snake_case__						,   num_images_per_prompt=snake_case__						,   ).images
				lowercase_       =					int(math.sqrt(snake_case__					)					)
				lowercase_       =					image_grid(snake_case__						,   rows=_rows						,   cols=num_images_per_prompt // _rows					)
				return grid, images


__a  			=					parse_args()
# Load models and create wrapper for stable diffusion
__a  			=					CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
__a  			=					CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
__a  			=					AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
__a  			=					UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

__a  			=					StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__a  			=					lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
						__a  			=					load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, 'unet', unet)
else:
						__a  			=					unet.to(torch.device('cuda', args.cuda_id))
__a  			=					pipeline.to(unet.device)
__a			,     __a  			=					generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
__a  			=					os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
						image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))




