





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase:	List[str] = {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Optional[Any] = ["""LayoutLMv2TokenizerFast"""]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = ["""LayoutLMv2FeatureExtractor"""]
			__UpperCamelCase:	int = ["""LayoutLMv2ImageProcessor"""]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:	Dict = [
			    """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """LayoutLMv2ForQuestionAnswering""",
			    """LayoutLMv2ForSequenceClassification""",
			    """LayoutLMv2ForTokenClassification""",
			    """LayoutLMv2Layer""",
			    """LayoutLMv2Model""",
			    """LayoutLMv2PreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
			from .processing_layoutlmva import LayoutLMvaProcessor
			from .tokenization_layoutlmva import LayoutLMvaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_layoutlmva import (
						    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LayoutLMvaForQuestionAnswering,
						    LayoutLMvaForSequenceClassification,
						    LayoutLMvaForTokenClassification,
						    LayoutLMvaLayer,
						    LayoutLMvaModel,
						    LayoutLMvaPreTrainedModel,
						)
else:
			import sys

			__UpperCamelCase:	Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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)))


