"""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__			(	)    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	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=4_2       ,	help='Seed for random process.'       ,	)
						parser.add_argument(
						    '-ci'       ,	'--cuda_id'       ,	type=_lowerCAmelCase       ,	default=0       ,	help='cuda_id.'       ,	)
						snake_case	    =	parser.parse_args()
						return args





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: Optional[int]    )    -> Union[str, Any]:





						"""simple docstring"""






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

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

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





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: int="robotic cat with wings"       ,	_UpperCamelCase				: str=7.5       ,	_UpperCamelCase				: Any=5_0       ,	_UpperCamelCase				: Tuple=1       ,	_UpperCamelCase				: Union[str, Any]=4_2       ,	)    -> Dict:





						"""simple docstring"""






						snake_case	    =	torch.Generator(pipeline.device    ).manual_seed(_lowerCAmelCase    )
						snake_case	    =	pipeline(
						    _lowerCAmelCase       ,	guidance_scale=_lowerCAmelCase       ,	num_inference_steps=_lowerCAmelCase       ,	generator=_lowerCAmelCase       ,	num_images_per_prompt=_lowerCAmelCase       ,	).images
						snake_case	    =	int(math.sqrt(_lowerCAmelCase    )    )
						snake_case	    =	image_grid(_lowerCAmelCase       ,	rows=_rows       ,	cols=num_images_per_prompt // _rows    )
						return grid, images


SCREAMING_SNAKE_CASE__					  =     parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__					  =     CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="tokenizer")
SCREAMING_SNAKE_CASE__					  =     CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="text_encoder")
SCREAMING_SNAKE_CASE__					  =     AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae")
SCREAMING_SNAKE_CASE__					  =     UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="unet")

SCREAMING_SNAKE_CASE__					  =     StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__					  =     lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "best_model.pt")):
					SCREAMING_SNAKE_CASE__					  =     load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, "unet", unet)
else:
					SCREAMING_SNAKE_CASE__					  =     unet.to(torch.device("cuda", args.cuda_id))
SCREAMING_SNAKE_CASE__					  =     pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__					  =     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()))))
SCREAMING_SNAKE_CASE__					  =     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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase       :     Any   =     {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Dict   =     ["""ElectraTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Tuple   =     [
        """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ElectraForCausalLM""",
        """ElectraForMaskedLM""",
        """ElectraForMultipleChoice""",
        """ElectraForPreTraining""",
        """ElectraForQuestionAnswering""",
        """ElectraForSequenceClassification""",
        """ElectraForTokenClassification""",
        """ElectraModel""",
        """ElectraPreTrainedModel""",
        """load_tf_weights_in_electra""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     [
        """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFElectraForMaskedLM""",
        """TFElectraForMultipleChoice""",
        """TFElectraForPreTraining""",
        """TFElectraForQuestionAnswering""",
        """TFElectraForSequenceClassification""",
        """TFElectraForTokenClassification""",
        """TFElectraModel""",
        """TFElectraPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[str]   =     [
        """FlaxElectraForCausalLM""",
        """FlaxElectraForMaskedLM""",
        """FlaxElectraForMultipleChoice""",
        """FlaxElectraForPreTraining""",
        """FlaxElectraForQuestionAnswering""",
        """FlaxElectraForSequenceClassification""",
        """FlaxElectraForTokenClassification""",
        """FlaxElectraModel""",
        """FlaxElectraPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase       :     Any   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
