import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'linear'
 UpperCAmelCase_     			= 'cosine'
 UpperCAmelCase_     			= 'cosine_with_restarts'
 UpperCAmelCase_     			= 'polynomial'
 UpperCAmelCase_     			= 'constant'
 UpperCAmelCase_     			= 'constant_with_warmup'
 UpperCAmelCase_     			= 'piecewise_constant'






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = -1     ):
       return LambdaLR(lowerCAmelCase__     ,  lambda lowerCAmelCase__     : 1     ,  last_epoch=lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = -1     ):

       def lr_lambda(lowerCAmelCase__     ):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase__     ) / float(max(1.0     ,  lowerCAmelCase__     )     )
              return 1.0

       return LambdaLR(lowerCAmelCase__     ,  lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = -1     ):
       lowerCAmelCase__					     = {}
       lowerCAmelCase__					     = step_rules.split(','     )
       for rule_str in rule_list[:-1]:
              lowerCAmelCase__     , lowerCAmelCase__					     = rule_str.split(':'     )
              lowerCAmelCase__					     = int(lowerCAmelCase__     )
              lowerCAmelCase__					     = float(lowerCAmelCase__     )
              lowerCAmelCase__					     = value
       lowerCAmelCase__					     = float(rule_list[-1]     )

       def create_rules_function(lowerCAmelCase__     ,  lowerCAmelCase__     ):
              def rule_func(lowerCAmelCase__     ) -> float:
                     lowerCAmelCase__					     = sorted(rules_dict.keys()     )
                     for i, sorted_step in enumerate(lowerCAmelCase__     ):
                            if steps < sorted_step:
                                   return rules_dict[sorted_steps[i]]
                     return last_lr_multiple

              return rule_func

       lowerCAmelCase__					     = create_rules_function(lowerCAmelCase__     ,  lowerCAmelCase__     )

       return LambdaLR(lowerCAmelCase__     ,  lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=-1     ):

       def lr_lambda(lowerCAmelCase__     ):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase__     ) / float(max(1     ,  lowerCAmelCase__     )     )
              return max(
                  0.0     ,  float(num_training_steps - current_step     ) / float(max(1     ,  num_training_steps - num_warmup_steps     )     )     )

       return LambdaLR(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = 0.5     ,  lowerCAmelCase__ = -1     ):

       def lr_lambda(lowerCAmelCase__     ):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase__     ) / float(max(1     ,  lowerCAmelCase__     )     )
              lowerCAmelCase__					     = float(current_step - num_warmup_steps     ) / float(max(1     ,  num_training_steps - num_warmup_steps     )     )
              return max(0.0     ,  0.5 * (1.0 + math.cos(math.pi * float(lowerCAmelCase__     ) * 2.0 * progress     ))     )

       return LambdaLR(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = 1     ,  lowerCAmelCase__ = -1     ):

       def lr_lambda(lowerCAmelCase__     ):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase__     ) / float(max(1     ,  lowerCAmelCase__     )     )
              lowerCAmelCase__					     = float(current_step - num_warmup_steps     ) / float(max(1     ,  num_training_steps - num_warmup_steps     )     )
              if progress >= 1.0:
                     return 0.0
              return max(0.0     ,  0.5 * (1.0 + math.cos(math.pi * ((float(lowerCAmelCase__     ) * progress) % 1.0)     ))     )

       return LambdaLR(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=1e-7     ,  lowerCAmelCase__=1.0     ,  lowerCAmelCase__=-1     ):
       lowerCAmelCase__					     = optimizer.defaults['lr']
       if not (lr_init > lr_end):
              raise ValueError(F"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""     )

       def lr_lambda(lowerCAmelCase__     ):
              if current_step < num_warmup_steps:
                     return float(lowerCAmelCase__     ) / float(max(1     ,  lowerCAmelCase__     )     )
              elif current_step > num_training_steps:
                     return lr_end / lr_init  # as LambdaLR multiplies by lr_init
              else:
                     lowerCAmelCase__					     = lr_init - lr_end
                     lowerCAmelCase__					     = num_training_steps - num_warmup_steps
                     lowerCAmelCase__					     = 1 - (current_step - num_warmup_steps) / decay_steps
                     lowerCAmelCase__					     = lr_range * pct_remaining**power + lr_end
                     return decay / lr_init  # as LambdaLR multiplies by lr_init

       return LambdaLR(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )


lowerCAmelCase__     						=			{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = None     ,  lowerCAmelCase__ = None     ,  lowerCAmelCase__ = None     ,  lowerCAmelCase__ = 1     ,  lowerCAmelCase__ = 1.0     ,  lowerCAmelCase__ = -1     ,  ):
       lowerCAmelCase__					     = SchedulerType(lowerCAmelCase__     )
       lowerCAmelCase__					     = TYPE_TO_SCHEDULER_FUNCTION[name]
       if name == SchedulerType.CONSTANT:
              return schedule_func(lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     )

       if name == SchedulerType.PIECEWISE_CONSTANT:
              return schedule_func(lowerCAmelCase__     ,  step_rules=lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     )

       # All other schedulers require `num_warmup_steps`
       if num_warmup_steps is None:
              raise ValueError(F"""{name} requires `num_warmup_steps`, please provide that argument."""     )

       if name == SchedulerType.CONSTANT_WITH_WARMUP:
              return schedule_func(lowerCAmelCase__     ,  num_warmup_steps=lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     )

       # All other schedulers require `num_training_steps`
       if num_training_steps is None:
              raise ValueError(F"""{name} requires `num_training_steps`, please provide that argument."""     )

       if name == SchedulerType.COSINE_WITH_RESTARTS:
              return schedule_func(
                  lowerCAmelCase__     ,  num_warmup_steps=lowerCAmelCase__     ,  num_training_steps=lowerCAmelCase__     ,  num_cycles=lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     ,  )

       if name == SchedulerType.POLYNOMIAL:
              return schedule_func(
                  lowerCAmelCase__     ,  num_warmup_steps=lowerCAmelCase__     ,  num_training_steps=lowerCAmelCase__     ,  power=lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     ,  )

       return schedule_func(
           lowerCAmelCase__     ,  num_warmup_steps=lowerCAmelCase__     ,  num_training_steps=lowerCAmelCase__     ,  last_epoch=lowerCAmelCase__     )

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		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument(
           '-m'     ,  '--pretrained_model_name_or_path'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  required=lowerCAmelCase__     ,  help='Path to pretrained model or model identifier from huggingface.co/models.'     ,  )
       parser.add_argument(
           '-c'     ,  '--caption'     ,  type=lowerCAmelCase__     ,  default='robotic cat with wings'     ,  help='Text used to generate images.'     ,  )
       parser.add_argument(
           '-n'     ,  '--images_num'     ,  type=lowerCAmelCase__     ,  default=4     ,  help='How much images to generate.'     ,  )
       parser.add_argument(
           '-s'     ,  '--seed'     ,  type=lowerCAmelCase__     ,  default=4_2     ,  help='Seed for random process.'     ,  )
       parser.add_argument(
           '-ci'     ,  '--cuda_id'     ,  type=lowerCAmelCase__     ,  default=0     ,  help='cuda_id.'     ,  )
       lowerCAmelCase__					     = parser.parse_args()
       return args






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

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

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="robotic cat with wings"     ,  lowerCAmelCase__=7.5     ,  lowerCAmelCase__=5_0     ,  lowerCAmelCase__=1     ,  lowerCAmelCase__=4_2     ,  ):
       lowerCAmelCase__					     = torch.Generator(pipeline.device     ).manual_seed(lowerCAmelCase__     )
       lowerCAmelCase__					     = pipeline(
           lowerCAmelCase__     ,  guidance_scale=lowerCAmelCase__     ,  num_inference_steps=lowerCAmelCase__     ,  generator=lowerCAmelCase__     ,  num_images_per_prompt=lowerCAmelCase__     ,  ).images
       lowerCAmelCase__					     = int(math.sqrt(lowerCAmelCase__     )     )
       lowerCAmelCase__					     = image_grid(lowerCAmelCase__     ,  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)))
