
'''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 				SCREAMING_SNAKE_CASE__	(   )       ->	List[Any]:
	_snake_case   						=		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.'			,				)
	_snake_case   						=		parser.parse_args()
	return args







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Dict:
	if not len(__A	) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'	)

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

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







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


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

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




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				"philschmid/bart-large-cnn-samsum"
			SCREAMING_SNAKE_CASE     :   Tuple									=				(
			    "This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
			    "and returns a summary of the text."
			)
			SCREAMING_SNAKE_CASE     :   str									=				"summarizer"
			SCREAMING_SNAKE_CASE     :   str									=				AutoTokenizer
			SCREAMING_SNAKE_CASE     :   str									=				AutoModelForSeqaSeqLM

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				["text"]







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				int							)      ->Optional[int]:
									return self.pre_processor(_UpperCamelCase							,   return_tensors='''pt'''							,   truncation=_UpperCamelCase							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Optional[int]							)      ->Tuple:
									return self.model.generate(**_UpperCamelCase							)[0]







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[int]							)      ->Any:
									return self.pre_processor.decode(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							,   clean_up_tokenization_spaces=_UpperCamelCase							)