





'''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 _lowerCAmelCase     (      )->     Optional[int]:
 __UpperCAmelCase					=   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.'       ,    )
 __UpperCAmelCase					=   parser.parse_args()
 return args







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[Any]:
 if not len(_lowerCAmelCase    ) == rows * cols:
  raise ValueError('The specified number of rows and columns are not correct.'    )

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

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







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase="robotic cat with wings"       ,    _lowerCAmelCase=7.5       ,    _lowerCAmelCase=50       ,    _lowerCAmelCase=1       ,    _lowerCAmelCase=42       ,    )->     Optional[Any]:
 __UpperCAmelCase					=   torch.Generator(pipeline.device    ).manual_seed(_lowerCAmelCase    )
 __UpperCAmelCase					=   pipeline(
     _lowerCAmelCase       ,    guidance_scale=_lowerCAmelCase       ,    num_inference_steps=_lowerCAmelCase       ,    generator=_lowerCAmelCase       ,    num_images_per_prompt=_lowerCAmelCase       ,    ).images
 __UpperCAmelCase					=   int(math.sqrt(_lowerCAmelCase    )    )
 __UpperCAmelCase					=   image_grid(_lowerCAmelCase       ,    rows=_rows       ,    cols=num_images_per_prompt // _rows    )
 return grid, images


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

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

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def _lowerCAmelCase     (      _lowerCAmelCase = 3    )->     qiskit.result.counts.Counts:
 if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  raise TypeError('number of qubits must be a integer.'    )
 if number_of_qubits <= 0:
  raise ValueError('number of qubits must be > 0.'    )
 if math.floor(_lowerCAmelCase    ) != number_of_qubits:
  raise ValueError('number of qubits must be exact integer.'    )
 if number_of_qubits > 10:
  raise ValueError('number of qubits too large to simulate(>10).'    )

 __UpperCAmelCase					=   QuantumRegister(_lowerCAmelCase       ,    'qr'    )
 __UpperCAmelCase					=   ClassicalRegister(_lowerCAmelCase       ,    'cr'    )

 __UpperCAmelCase					=   QuantumCircuit(_lowerCAmelCase       ,    _lowerCAmelCase    )

 __UpperCAmelCase					=   number_of_qubits

 for i in range(_lowerCAmelCase    ):
  quantum_circuit.h(number_of_qubits - i - 1    )
  counter -= 1
  for j in range(_lowerCAmelCase    ):
   quantum_circuit.cp(np.pi / 2 ** (counter - j)       ,    _lowerCAmelCase       ,    _lowerCAmelCase    )

 for k in range(number_of_qubits // 2    ):
  quantum_circuit.swap(_lowerCAmelCase       ,    number_of_qubits - k - 1    )

 # measure all the qubits
 quantum_circuit.measure(_lowerCAmelCase       ,    _lowerCAmelCase    )
 # simulate with 10000 shots
 __UpperCAmelCase					=   Aer.get_backend('qasm_simulator'    )
 __UpperCAmelCase					=   execute(_lowerCAmelCase       ,    _lowerCAmelCase       ,    shots=1_00_00    )

 return job.result().get_counts(_lowerCAmelCase    )


if __name__ == "__main__":
    print(
        F"""Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}"""
    )





