

"""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 	lowercase_						(     ):




					'''simple docstring'''






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



def 	lowercase_						(     _lowercase							:		Tuple				,   _lowercase							:		Dict				,   _lowercase							:		str						):




					'''simple docstring'''






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

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

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



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




					'''simple docstring'''






					UpperCAmelCase      : List[str]   =					torch.Generator(pipeline.device						).manual_seed(UpperCamelCase__						)
					UpperCAmelCase      : List[str]   =					pipeline(
					    UpperCamelCase__				,   guidance_scale=UpperCamelCase__				,   num_inference_steps=UpperCamelCase__				,   generator=UpperCamelCase__				,   num_images_per_prompt=UpperCamelCase__				,   ).images
					UpperCAmelCase      : Union[str, Any]   =					int(math.sqrt(UpperCamelCase__						)						)
					UpperCAmelCase      : Any   =					image_grid(UpperCamelCase__				,   rows=_rows				,   cols=num_images_per_prompt // _rows						)
					return grid, images


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

snake_case_ : int   =							StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
snake_case_ : Dict   =							lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
	snake_case_ : Dict   =							load(args.pretrained_model_name_or_path, model=unet)
	unet.eval()
	setattr(pipeline, """unet""", unet)
else:
	snake_case_ : Optional[int]   =							unet.to(torch.device("""cuda""", args.cuda_id))
snake_case_ : List[Any]   =							pipeline.to(unet.device)
snake_case_ : 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()))))
snake_case_ : Optional[int]   =							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)))









import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCAmelCase							:       Dict					 =	"""\
    Text data.
    Second line of data."""

lowerCAmelCase							:       str					 =	"""file"""






@pytest.fixture(scope='''session'''					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Any           =   tmp_path_factory.mktemp('''data'''					) / (FILE_PATH + '''.zstd''')
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   bytes(UpperCamelCase__       ,			'''utf-8'''					)
						with zstd.open(UpperCamelCase__       ,			'''wb'''					) as f:
												f.write(UpperCamelCase__					)
						return path






@pytest.fixture
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    int:

						"""simple docstring"""





						with open(os.path.join(tmpfs.local_root_dir       ,			UpperCamelCase__					)       ,			'''w'''					) as f:
												f.write(UpperCamelCase__					)
						return FILE_PATH






@pytest.mark.parametrize('''compression_format'''       ,			['''gzip''', '''xz''', '''zstd''']					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   List[Any]					)      ->    Any:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Dict           =   {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
						__SCREAMING_SNAKE_CASE:				List[str]           =   input_paths[compression_format]
						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path / '''cache'''
						__SCREAMING_SNAKE_CASE:				List[str]           =   DownloadConfig(cache_dir=UpperCamelCase__       ,			extract_compressed_file=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   cached_path(UpperCamelCase__       ,			download_config=UpperCamelCase__					)
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   f.read()
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Dict           =   f.read()
						assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''       ,			[True, False]					)
@pytest.mark.parametrize('''default_cache_dir'''       ,			[True, False]					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   str       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Optional[Any]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   '''custom_cache'''
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''custom_extracted_dir'''
						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path / '''custom_extracted_path'''
						if default_extracted:
												__SCREAMING_SNAKE_CASE:				int           =   ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
						else:
												monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''       ,			UpperCamelCase__					)
												monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''       ,			str(UpperCamelCase__					)					)
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

						__SCREAMING_SNAKE_CASE:				Dict           =   xz_file
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   (
						    DownloadConfig(extract_compressed_file=UpperCamelCase__					)
						    if default_cache_dir
						    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir       ,			extract_compressed_file=UpperCamelCase__					)
						)
						__SCREAMING_SNAKE_CASE:				int           =   cached_path(UpperCamelCase__       ,			download_config=UpperCamelCase__					)
						assert Path(UpperCamelCase__					).parent.parts[-2:] == expected






def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   str(Path(UpperCamelCase__					).resolve()					)
						assert cached_path(UpperCamelCase__					) == text_file
						# relative path
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   str(Path(UpperCamelCase__					).resolve().relative_to(Path(os.getcwd()					)					)					)
						assert cached_path(UpperCamelCase__					) == text_file






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   str(tmp_path.resolve() / '''__missing_file__.txt'''					)
						with pytest.raises(UpperCamelCase__					):
												cached_path(UpperCamelCase__					)
						# relative path
						__SCREAMING_SNAKE_CASE:				int           =   '''./__missing_file__.txt'''
						with pytest.raises(UpperCamelCase__					):
												cached_path(UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   get_from_cache(F"""tmp://{tmpfs_file}"""					)
						with open(UpperCamelCase__					) as f:
												__SCREAMING_SNAKE_CASE:				Tuple           =   f.read()
						assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	)      ->    Any:

						"""simple docstring"""





						with pytest.raises(UpperCamelCase__					):
												cached_path('''https://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   int					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												http_get('''https://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												http_head('''https://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Dict					)      ->    List[str]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												ftp_get('''ftp://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												ftp_head('''ftp://huggingface.co'''					)






@patch('''datasets.config.HF_DATASETS_OFFLINE'''       ,			UpperCamelCase__					)
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Tuple:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Tuple           =   tmp_path_factory.mktemp('''data'''					) / '''file.html'''
						with pytest.raises(UpperCamelCase__					):
												fsspec_get('''s3://huggingface.co'''       ,			temp_file=UpperCamelCase__					)
						with pytest.raises(UpperCamelCase__					):
												fsspec_head('''s3://huggingface.co'''					)



