
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        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						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=42 ,			help='''Seed for random process.''' ,			)
					parser.add_argument(
					    '''-ci''' ,			'''--cuda_id''' ,			type=lowerCAmelCase__ ,			default=0 ,			help='''cuda_id.''' ,			)
					lowercase							=						parser.parse_args()
					return args



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

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

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



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


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

lowercase__		:Union[str, Any]   =    StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowercase__		:List[Any]   =    lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
							lowercase__		:str   =    load(args.pretrained_model_name_or_path, model=unet)
							unet.eval()
							setattr(pipeline, "unet", unet)
else:
							lowercase__		:Optional[int]   =    unet.to(torch.device("cuda", args.cuda_id))
lowercase__		:List[Any]   =    pipeline.to(unet.device)
lowercase__  ,     lowercase__		:Tuple   =    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()))))
lowercase__		: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'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__    					=      namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[int]:
    return (abs(source - target						) / target) < 0.0_1







@pytest.mark.integration
def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		str													=				_TestCommandArgs(dataset=lowerCAmelCase__  ,					all_configs=lowerCAmelCase__  ,					save_infos=lowerCAmelCase__						)
    UpperCAmelCase__			:		Tuple													=				TestCommand(*lowerCAmelCase__						)
    test_command.run()
    UpperCAmelCase__			:		List[Any]													=				os.path.join(lowerCAmelCase__  ,					'''README.md'''						)
    assert os.path.exists(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				DatasetInfosDict.from_directory(lowerCAmelCase__						)
    UpperCAmelCase__			:		Any													=				DatasetInfosDict(
        {
            '''default''': DatasetInfo(
                features=Features(
                    {
                        '''tokens''': Sequence(Value('''string'''						)						),
                        '''ner_tags''': Sequence(
                            ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
                        '''langs''': Sequence(Value('''string'''						)						),
                        '''spans''': Sequence(Value('''string'''						)						),
                    }						)  ,					splits=[
                    {
                        '''name''': '''train''',
                        '''num_bytes''': 2_35_15_63,
                        '''num_examples''': 1_00_00,
                    },
                    {
                        '''name''': '''validation''',
                        '''num_bytes''': 23_84_18,
                        '''num_examples''': 10_00,
                    },
                ]  ,					download_size=3_94_06_80  ,					dataset_size=2_58_99_81  ,					)
        }						)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				getattr(dataset_infos['''default''']  ,					lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']  ,					lowerCAmelCase__						)
        if key == "num_bytes":
            assert is_apercent_close(lowerCAmelCase__  ,					lowerCAmelCase__						)
        elif key == "splits":
            assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
            for split in result:
                assert result[split].name == expected[split].name
                assert result[split].num_examples == expected[split].num_examples
                assert is_apercent_close(result[split].num_bytes  ,					expected[split].num_bytes						)
        else:
            result == expected
