



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





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

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

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





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


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

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case						:Tuple						=					{
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __snake_case						:Optional[int]						=					[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 __snake_case						:Dict						=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





