



'''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   (						):
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			argparse.ArgumentParser()
							parser.add_argument(
							    '''-m'''					,		'''--pretrained_model_name_or_path'''					,		type=A					,		default=A					,		required=A					,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,		)
							parser.add_argument(
							    '''-c'''					,		'''--caption'''					,		type=A					,		default='''robotic cat with wings'''					,		help='''Text used to generate images.'''					,		)
							parser.add_argument(
							    '''-n'''					,		'''--images_num'''					,		type=A					,		default=4					,		help='''How much images to generate.'''					,		)
							parser.add_argument(
							    '''-s'''					,		'''--seed'''					,		type=A					,		default=42					,		help='''Seed for random process.'''					,		)
							parser.add_argument(
							    '''-ci'''					,		'''--cuda_id'''					,		type=A					,		default=0					,		help='''cuda_id.'''					,		)
							SCREAMING_SNAKE_CASE					:      Dict     					=			parser.parse_args()
							return args
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Dict					,		A  :				Any				):
							if not len(A				) == rows * cols:
														raise ValueError('''The specified number of rows and columns are not correct.'''				)

							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			imgs[0].size
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.new('''RGB'''					,		size=(cols * w, rows * h)				)
							SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			grid.size

							for i, img in enumerate(A				):
														grid.paste(A					,		box=(i % cols * w, i // cols * h)				)
							return grid
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				Optional[int]="robotic cat with wings"					,		A  :				Tuple=7.5					,		A  :				int=50					,		A  :				Tuple=1					,		A  :				Union[str, Any]=42					,		):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.Generator(pipeline.device				).manual_seed(A				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			pipeline(
							    A					,		guidance_scale=A					,		num_inference_steps=A					,		generator=A					,		num_images_per_prompt=A					,		).images
							SCREAMING_SNAKE_CASE					:      Tuple     					=			int(math.sqrt(A				)				)
							SCREAMING_SNAKE_CASE					:      str     					=			image_grid(A					,		rows=_rows					,		cols=num_images_per_prompt // _rows				)
							return grid, images


lowerCAmelCase_     :       Any								=						parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase_     :       Any								=						CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase_     :       int								=						CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase_     :       Union[str, Any]								=						AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase_     :       List[str]								=						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_     :       Dict								=						lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
							lowerCAmelCase_     :       Dict								=						load(args.pretrained_model_name_or_path, model=unet)
							unet.eval()
							setattr(pipeline, 'unet', unet)
else:
							lowerCAmelCase_     :       List[Any]								=						unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase_     :       Dict								=						pipeline.to(unet.device)
lowerCAmelCase_			,							lowerCAmelCase_     :       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_     :       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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase_     :       Tuple								=						{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       Dict								=						[
							    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TapasForMaskedLM',
							    'TapasForQuestionAnswering',
							    'TapasForSequenceClassification',
							    'TapasModel',
							    'TapasPreTrainedModel',
							    'load_tf_weights_in_tapas',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_     :       str								=						[
							    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFTapasForMaskedLM',
							    'TFTapasForQuestionAnswering',
							    'TFTapasForSequenceClassification',
							    'TFTapasModel',
							    'TFTapasPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_     :       List[Any]								=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




