






'''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 	__UpperCAmelCase       ( )->    Optional[Any]:



			"""simple docstring"""



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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



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

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

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__="robotic cat with wings"     ,__magic_name__=7.5     ,__magic_name__=50     ,__magic_name__=1     ,__magic_name__=42     ,)->    Optional[Any]:



			"""simple docstring"""



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


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

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

import datasets


__lowerCamelCase   :					str   							=					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

__lowerCamelCase   :					int   							=					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

__lowerCamelCase   :					List[str]   							=					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION							)
class    A_	(datasets.Metric							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										return datasets.MetricInfo(
										    description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
										        {
										            "predictions": datasets.Value("float"				),
										            "references": datasets.Value("float"				),
										        }				)       ,							reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"]       ,							)






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       spearmanr(lowerCAmelCase__       ,							lowerCAmelCase__				)
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}




