
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)))








import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


__lowerCAmelCase	: Optional[Any]  				='\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


__lowerCAmelCase	: Any  				='\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


__lowerCAmelCase	: Optional[Any]  				=r'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION       )
class        _lowercase      (			datasets.Metric       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Optional[Any]	)		->			List[str]:
										return datasets.MetricInfo(
										    description=_DESCRIPTION			,			citation=_CITATION			,			inputs_description=_KWARGS_DESCRIPTION			,			features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''string'''	),
										            '''references''': datasets.Value('''string'''	),
										        }	)			,			homepage='''https://github.com/hendrycks/math'''			,			codebase_urls=['''https://github.com/hendrycks/math''']			,			)



				def 		__magic_name__(       self			:Any			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Any	)		->			Optional[int]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		0.0
										for i, j in zip(lowerCAmelCase__			,			lowerCAmelCase__	):
																n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase__			,			lowerCAmelCase__	) else 0.0
										__SCREAMING_SNAKE_CASE						:     str					=		n_correct / len(lowerCAmelCase__	)
										return {
										    "accuracy": accuracy,
										}
