
"""simple docstring"""




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



def        _lowerCamelCase ( __a,							__a,							__a    ):
		if not len(__a    ) == rows * cols:
				raise ValueError('''The specified number of rows and columns are not correct.'''    )

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

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



def        _lowerCamelCase ( __a,							__a="robotic cat with wings",							__a=7.5,							__a=50,							__a=1,							__a=42,							):
		SCREAMING_SNAKE_CASE_						 = torch.Generator(pipeline.device    ).manual_seed(__a    )
		SCREAMING_SNAKE_CASE_						 = pipeline(
		    __a,							guidance_scale=__a,							num_inference_steps=__a,							generator=__a,							num_images_per_prompt=__a,							).images
		SCREAMING_SNAKE_CASE_						 = int(math.sqrt(__a    )    )
		SCREAMING_SNAKE_CASE_						 = image_grid(__a,							rows=_rows,							cols=num_images_per_prompt // _rows    )
		return grid, images


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

lowerCAmelCase__							=				StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__							=				lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
						lowerCAmelCase__							=				load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, 'unet', unet)
else:
						lowerCAmelCase__							=				unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__							=				pipeline.to(unet.device)
lowerCAmelCase__,  lowerCAmelCase__							=				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()))))
lowerCAmelCase__							=				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)))

"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_="cls"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,       bos_token_id=SCREAMING_SNAKE_CASE_       ,       eos_token_id=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = project_dim
									SCREAMING_SNAKE_CASE_						 = pooler_fn
									SCREAMING_SNAKE_CASE_						 = learn_encoder
									SCREAMING_SNAKE_CASE_						 = use_attention_mask







class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      [R'''pooler''', R'''logit_scale''']
							UpperCAmelCase__						     =      [R'''position_ids''', R'''predictions.decoder.bias''']
							UpperCAmelCase__						     =      '''roberta'''
							UpperCAmelCase__						     =      RobertaSeriesConfig

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = XLMRobertaModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
									SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       '''has_pre_transformation'''       ,       SCREAMING_SNAKE_CASE_    )
									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
											SCREAMING_SNAKE_CASE_						 = nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps    )
									self.post_init()







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_						 = self.base_model(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       position_ids=SCREAMING_SNAKE_CASE_       ,       head_mask=SCREAMING_SNAKE_CASE_       ,       inputs_embeds=SCREAMING_SNAKE_CASE_       ,       encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,       encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,       output_attentions=SCREAMING_SNAKE_CASE_       ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )

									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = outputs['''hidden_states'''][-2]
											SCREAMING_SNAKE_CASE_						 = self.pre_LN(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self.transformation_pre(SCREAMING_SNAKE_CASE_    )

											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = self.transformation(outputs.last_hidden_state    )
											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )