






import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")




def 				A		(					lowercase__  :  np.ndarray  )		-> np.ndarray:
	return input_array.reshape((input_array.size, 1)  )




def 				A		(					lowercase__  :  np.ndarray ,		lowercase__  :  np.ndarray ,		lowercase__  :  int  )		-> np.ndarray:
	UpperCamelCase__ :Union[str, Any]						=      np.nan
	for i in range(lowercase__  ):
		UpperCamelCase__ :Optional[Any]						=      features[:, labels == i]
		UpperCamelCase__ :Any						=      data.mean(1  )
		# Centralize the data of class i
		UpperCamelCase__ :Tuple						=      data - column_reshape(lowercase__  )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += np.dot(lowercase__ ,		centered_data.T  )
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCamelCase__ :str						=      np.dot(lowercase__ ,		centered_data.T  )

	return covariance_sum / features.shape[1]




def 				A		(					lowercase__  :  np.ndarray ,		lowercase__  :  np.ndarray ,		lowercase__  :  int  )		-> np.ndarray:
	UpperCamelCase__ :List[Any]						=      features.mean(1  )
	UpperCamelCase__ :List[Any]						=      np.nan
	for i in range(lowercase__  ):
		UpperCamelCase__ :int						=      features[:, labels == i]
		UpperCamelCase__ :Optional[int]						=      data.shape[1]
		UpperCamelCase__ :Union[str, Any]						=      data.mean(1  )
		if i > 0:
			# If covariance_sum is not None
			covariance_sum += device_data * np.dot(
			    column_reshape(lowercase__  ) - column_reshape(lowercase__  ) ,		(column_reshape(lowercase__  ) - column_reshape(lowercase__  )).T ,		)
		else:
			# If covariance_sum is np.nan (i.e. first loop)
			UpperCamelCase__ :Optional[int]						=      device_data * np.dot(
			    column_reshape(lowercase__  ) - column_reshape(lowercase__  ) ,		(column_reshape(lowercase__  ) - column_reshape(lowercase__  )).T ,		)

	return covariance_sum / features.shape[1]




def 				A		(					lowercase__  :  np.ndarray ,		lowercase__  :  int  )		-> np.ndarray:

	# Check if the features have been loaded
	if features.any():
		UpperCamelCase__ :List[str]						=      features.mean(1  )
		# Center the dataset
		UpperCamelCase__ :List[Any]						=      features - np.reshape(lowercase__ ,		(data_mean.size, 1)  )
		UpperCamelCase__ :List[str]						=      np.dot(lowercase__ ,		centered_data.T  ) / features.shape[1]
		UpperCamelCase__				,		UpperCamelCase__ :Any						=      np.linalg.eigh(lowercase__  )
		# Take all the columns in the reverse order (-1), and then takes only the first
		UpperCamelCase__ :int						=      eigenvectors[:, ::-1][:, 0:dimensions]
		# Project the database on the new space
		UpperCamelCase__ :Union[str, Any]						=      np.dot(filtered_eigenvectors.T ,		lowercase__  )
		logging.info("""Principal Component Analysis computed"""  )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR ,		format="""%(message)s""" ,		force=lowercase__  )
		logging.error("""Dataset empty"""  )
		raise AssertionError




def 				A		(					lowercase__  :  np.ndarray ,		lowercase__  :  np.ndarray ,		lowercase__  :  int ,		lowercase__  :  int  )		-> np.ndarray:
	assert classes > dimensions

	# Check if features have been already loaded
	if features.any:
		UpperCamelCase__				,		UpperCamelCase__ :int						=      eigh(
		    covariance_between_classes(lowercase__ ,		lowercase__ ,		lowercase__  ) ,		covariance_within_classes(lowercase__ ,		lowercase__ ,		lowercase__  ) ,		)
		UpperCamelCase__ :Optional[Any]						=      eigenvectors[:, ::-1][:, :dimensions]
		UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Tuple						=      np.linalg.svd(lowercase__  )
		UpperCamelCase__ :str						=      svd_matrix[:, 0:dimensions]
		UpperCamelCase__ :Optional[Any]						=      np.dot(filtered_svd_matrix.T ,		lowercase__  )
		logging.info("""Linear Discriminant Analysis computed"""  )

		return projected_data
	else:
		logging.basicConfig(level=logging.ERROR ,		format="""%(message)s""" ,		force=lowercase__  )
		logging.error("""Dataset empty"""  )
		raise AssertionError




def 				A		(					)		-> None:
	# Create dummy dataset with 2 classes and 3 features
	UpperCamelCase__ :List[str]						=      np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]  )
	UpperCamelCase__ :int						=      np.array([0, 0, 0, 1, 1]  )
	UpperCamelCase__ :List[str]						=      2
	UpperCamelCase__ :Optional[int]						=      2

	# Assert that the function raises an AssertionError if dimensions > classes
	with pytest.raises(lowercase__  ) as error_info:
		UpperCamelCase__ :str						=      linear_discriminant_analysis(
		    lowercase__ ,		lowercase__ ,		lowercase__ ,		lowercase__  )
		if isinstance(lowercase__ ,		np.ndarray  ):
			raise AssertionError(
			    """Did not raise AssertionError for dimensions > classes"""  )
		assert error_info.type is AssertionError




def 				A		(					)		-> None:
	UpperCamelCase__ :Union[str, Any]						=      np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]  )
	UpperCamelCase__ :List[str]						=      2
	UpperCamelCase__ :Union[str, Any]						=      np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]]  )

	with pytest.raises(lowercase__  ) as error_info:
		UpperCamelCase__ :Optional[int]						=      principal_component_analysis(lowercase__ ,		lowercase__  )
		if not np.allclose(lowercase__ ,		lowercase__  ):
			raise AssertionError
		assert error_info.type is AssertionError


if __name__ == "__main__":
							import doctest

							doctest.testmod()






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)))


