





import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Union[str, Any]  						=						""
   a		:				str  						=						(
       None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
   )
   a		:				int  						=						None  # compression type in fsspec. ex: "gzip"
   a		:				Optional[Any]  						=						None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




   def __init__(       self,						__magic_name__ = "",						__magic_name__ = None,						__magic_name__ = None,						**__magic_name__				)					-> Tuple:






       """simple docstring"""
       super().__init__(self,						**lowerCamelCase_				)
       # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
       UpperCamelCase__				:							Dict			  = fsspec.open(
           lowerCamelCase_,						mode='''rb''',						protocol=lowerCamelCase_,						compression=self.compression,						client_kwargs={
               '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
               '''trust_env''': True,  # Enable reading proxy env variables.
               **(target_options or {}).pop('''client_kwargs''',						{}				),  # To avoid issues if it was already passed.
           },						**(target_options or {}),						)
       UpperCamelCase__				:							List[Any]			  = os.path.basename(self.file.path.split('''::'''				)[0]				)
       UpperCamelCase__				:							Optional[int]			  = (
           self.compressed_name[: self.compressed_name.rindex('''.'''				)]
           if '.' in self.compressed_name
           else self.compressed_name
       )
       UpperCamelCase__				:							str			  = None




   @classmethod
   def  UpperCamelCase__    (       cls,						__magic_name__				)					-> str:






       """simple docstring"""
       # compressed file paths are always relative to the archive root
       return super()._strip_protocol(lowerCamelCase_				).lstrip('''/'''				)




   def  UpperCamelCase__    (       self				)					-> Any:






       """simple docstring"""
       if self.dir_cache is None:
           UpperCamelCase__				:							Optional[Any]			  = {**self.file.fs.info(self.file.path				), 'name': self.uncompressed_name}
           UpperCamelCase__				:							Tuple			  = {f['name']: f}




   def  UpperCamelCase__    (       self,						__magic_name__				)					-> Any:






       """simple docstring"""
       return self.file.open().read()




   def  UpperCamelCase__    (       self,						__magic_name__,						__magic_name__ = "rb",						__magic_name__=None,						__magic_name__=True,						__magic_name__=None,						**__magic_name__,						)					-> str:






       """simple docstring"""
       UpperCamelCase__				:							List[str]			  = self._strip_protocol(lowerCamelCase_				)
       if mode != "rb":
           raise ValueError(f"Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"				)
       return self.file.open()






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Union[str, Any]  						=						"bz2"
   a		:				Optional[Any]  						=						"bz2"
   a		:				Optional[int]  						=						".bz2"






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Union[str, Any]  						=						"gzip"
   a		:				Union[str, Any]  						=						"gzip"
   a		:				Dict  						=						".gz"






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Optional[Any]  						=						"lz4"
   a		:				List[Any]  						=						"lz4"
   a		:				str  						=						".lz4"






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Optional[Any]  						=						"xz"
   a		:				List[Any]  						=						"xz"
   a		:				Optional[int]  						=						".xz"






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Dict  						=						"zstd"
   a		:				List[str]  						=						"zstd"
   a		:				Optional[int]  						=						".zst"




   def __init__(       self,						__magic_name__,						__magic_name__ = "rb",						__magic_name__ = None,						__magic_name__ = None,						__magic_name__ = DEFAULT_BLOCK_SIZE,						**__magic_name__,						)					-> List[Any]:






       """simple docstring"""
       super().__init__(
           fo=lowerCamelCase_,						mode=lowerCamelCase_,						target_protocol=lowerCamelCase_,						target_options=lowerCamelCase_,						block_size=lowerCamelCase_,						**lowerCamelCase_,						)
       # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
       #
       # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
       #     out.close = close
       # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
       #
       # see https://github.com/intake/filesystem_spec/issues/725
       UpperCamelCase__				:							Tuple			  = self.file.__enter__






       class        lowercase__			:

          '''simple docstring'''


          def __init__(       self,						__magic_name__				)					-> str:






              """simple docstring"""
              UpperCamelCase__				:							Tuple			  = file_




          def __enter__(       self				)					-> Tuple:






              """simple docstring"""
              self._file.__enter__()
              return self




          def __exit__(       self,						*__magic_name__,						**__magic_name__				)					-> Any:






              """simple docstring"""
              self._file.__exit__(*lowerCamelCase_,						**lowerCamelCase_				)




          def __iter__(       self				)					-> int:






              """simple docstring"""
              return iter(self._file				)




          def  UpperCamelCase__    (       self				)					-> str:






              """simple docstring"""
              return next(self._file				)




          def __getattr__(       self,						__magic_name__				)					-> str:






              """simple docstring"""
              return getattr(self._file,						lowerCamelCase_				)

       def fixed_enter(*__magic_name__,						**__magic_name__				):
           return WrappedFile(_enter(*lowerCamelCase_,						**lowerCamelCase_				)				)

       UpperCamelCase__				:							List[str]			  = fixed_enter








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       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      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.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




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

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

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


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




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


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = 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:	int = 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:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = 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:	List[str] = 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)))


