


'''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 			_a			(		):





     '''simple docstring'''






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




def 			_a			(		_lowercase      :			Union[str, Any]    ,      _lowercase      :			int    ,      _lowercase      :			Union[str, Any]						):





     '''simple docstring'''






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

     __UpperCAmelCase			:				Union[str, Any]													=    imgs[0].size
     __UpperCAmelCase			:				Optional[int]													=    Image.new('''RGB'''    ,      size=(cols * w, rows * h)						)
     __UpperCAmelCase			:				Tuple													=    grid.size

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




def 			_a			(		_lowercase      :			str    ,      _lowercase      :			Optional[Any]="robotic cat with wings"    ,      _lowercase      :			Tuple=7.5    ,      _lowercase      :			Dict=50    ,      _lowercase      :			Optional[Any]=1    ,      _lowercase      :			Dict=42    ,      ):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    torch.Generator(pipeline.device						).manual_seed(lowercase_						)
     __UpperCAmelCase			:				int													=    pipeline(
         lowercase_    ,      guidance_scale=lowercase_    ,      num_inference_steps=lowercase_    ,      generator=lowercase_    ,      num_images_per_prompt=lowercase_    ,      ).images
     __UpperCAmelCase			:				Dict													=    int(math.sqrt(lowercase_						)						)
     __UpperCAmelCase			:				Optional[int]													=    image_grid(lowercase_    ,      rows=_rows    ,      cols=num_images_per_prompt // _rows						)
     return grid, images


__UpperCAmelCase    :List[str]				       =     parse_args()
# Load models and create wrapper for stable diffusion
__UpperCAmelCase    :List[str]				       =     CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
__UpperCAmelCase    :List[str]				       =     CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
__UpperCAmelCase    :Dict				       =     AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
__UpperCAmelCase    :int				       =     UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

__UpperCAmelCase    :Dict				       =     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCAmelCase    :str				       =     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
      __UpperCAmelCase    :int				       =     load(args.pretrained_model_name_or_path, model=unet)
      unet.eval()
      setattr(pipeline, "unet", unet)
else:
      __UpperCAmelCase    :Union[str, Any]				       =     unet.to(torch.device("cuda", args.cuda_id))
__UpperCAmelCase    :int				       =     pipeline.to(unet.device)
__UpperCAmelCase    :List[str]				       =     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()))))
__UpperCAmelCase    :Optional[Any]				       =     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 collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

__UpperCAmelCase    :Optional[int]				       =     TypeVar("KEY")
__UpperCAmelCase    :Tuple				       =     TypeVar("VAL")



@dataclass(frozen=_a			,						slots=_a )
class 			a       (	Generic[KEY, VAL] ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				KEY
     SCREAMING_SNAKE_CASE :				VAL



class 			a       (	_Item ):


     """simple docstring"""






     def __init__(       self  :	Optional[int]    )		->							None:
          super().__init__(snake_case							,				snake_case    )






     def __bool__(       self  :	Dict    )		->							bool:
          return False



__UpperCAmelCase    :Optional[Any]				       =     _DeletedItem()



class 			a       (	MutableMapping[KEY, VAL] ):


     """simple docstring"""






     def __init__(       self  :	str							,				snake_case  :	int = 8							,				snake_case  :	float = 0.75    )		->							None:
          __UpperCAmelCase			:				Dict													=    initial_block_size
          __UpperCAmelCase			:				list[_Item | None]													=    [None] * initial_block_size
          assert 0.0 < capacity_factor < 1.0
          __UpperCAmelCase			:				str													=    capacity_factor
          __UpperCAmelCase			:				Optional[int]													=    0

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	KEY    )		->							int:
          return hash(snake_case    ) % len(self._buckets    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							int:
          return (ind + 1) % len(self._buckets    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	int							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							bool:
          __UpperCAmelCase			:				int													=    self._buckets[ind]
          if not stored:
               __UpperCAmelCase			:				List[str]													=    _Item(snake_case							,				snake_case    )
               self._len += 1
               return True
          elif stored.key == key:
               __UpperCAmelCase			:				int													=    _Item(snake_case							,				snake_case    )
               return True
          else:
               return False

     def 				lowerCamelCase__				(       self  :	str    )		->							bool:
          __UpperCAmelCase			:				Union[str, Any]													=    len(self._buckets    ) * self._capacity_factor
          return len(self    ) >= int(snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							bool:
          if len(self._buckets    ) <= self._initial_block_size:
               return False
          __UpperCAmelCase			:				List[str]													=    len(self._buckets    ) * self._capacity_factor / 2
          return len(self    ) < limit

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int    )		->							None:
          __UpperCAmelCase			:				Union[str, Any]													=    self._buckets
          __UpperCAmelCase			:				Union[str, Any]													=    [None] * new_size
          __UpperCAmelCase			:				List[str]													=    0
          for item in old_buckets:
               if item:
                    self._add_item(item.key							,				item.val    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							None:
          self._resize(len(self._buckets    ) * 2    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							None:
          self._resize(len(self._buckets    ) // 2    )

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	KEY    )		->							Iterator[int]:
          __UpperCAmelCase			:				Tuple													=    self._get_bucket_index(snake_case    )
          for _ in range(len(self._buckets    )    ):
               yield ind
               __UpperCAmelCase			:				Dict													=    self._get_next_ind(snake_case    )

     def 				lowerCamelCase__				(       self  :	str							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							None:
          for ind in self._iterate_buckets(snake_case    ):
               if self._try_set(snake_case							,				snake_case							,				snake_case    ):
                    break

     def __setitem__(       self  :	List[str]							,				snake_case  :	KEY							,				snake_case  :	VAL    )		->							None:
          if self._is_full():
               self._size_up()

          self._add_item(snake_case							,				snake_case    )

     def __delitem__(       self  :	Any							,				snake_case  :	KEY    )		->							None:
          for ind in self._iterate_buckets(snake_case    ):
               __UpperCAmelCase			:				List[Any]													=    self._buckets[ind]
               if item is None:
                    raise KeyError(snake_case    )
               if item is _deleted:
                    continue
               if item.key == key:
                    __UpperCAmelCase			:				Optional[Any]													=    _deleted
                    self._len -= 1
                    break
          if self._is_sparse():
               self._size_down()

     def __getitem__(       self  :	List[str]							,				snake_case  :	KEY    )		->							VAL:
          for ind in self._iterate_buckets(snake_case    ):
               __UpperCAmelCase			:				Optional[int]													=    self._buckets[ind]
               if item is None:
                    break
               if item is _deleted:
                    continue
               if item.key == key:
                    return item.val
          raise KeyError(snake_case    )

     def __len__(       self  :	List[Any]    )		->							int:
          return self._len

     def __iter__(       self  :	str    )		->							Iterator[KEY]:
          yield from (item.key for item in self._buckets if item)






     def __repr__(       self  :	Optional[Any]    )		->							str:
          __UpperCAmelCase			:				str													=    ''' ,'''.join(
              f'{item.key}: {item.val}' for item in self._buckets if item    )
          return f'HashMap({val_string})'