


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


def      _snake_case						(						_snake_case				:			str ,   _snake_case				:			List[Any] ,   _snake_case				:			str			):
      if not len(_snake_case			) == rows * cols:
            raise ValueError('''The specified number of rows and columns are not correct.'''			)

      lowerCAmelCase,      lowerCAmelCase					:	Tuple       =   imgs[0].size
      lowerCAmelCase					:	Dict       =   Image.new('''RGB''' ,   size=(cols * w, rows * h)			)
      lowerCAmelCase,      lowerCAmelCase					:	List[str]       =   grid.size

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


def      _snake_case						(						_snake_case				:			Union[str, Any] ,   _snake_case				:			Tuple="robotic cat with wings" ,   _snake_case				:			Union[str, Any]=7.5 ,   _snake_case				:			str=50 ,   _snake_case				:			Dict=1 ,   _snake_case				:			List[str]=42 ,   ):
      lowerCAmelCase					:	Optional[int]       =   torch.Generator(pipeline.device			).manual_seed(_snake_case			)
      lowerCAmelCase					:	Optional[Any]       =   pipeline(
          _snake_case ,   guidance_scale=_snake_case ,   num_inference_steps=_snake_case ,   generator=_snake_case ,   num_images_per_prompt=_snake_case ,   ).images
      lowerCAmelCase					:	Dict       =   int(math.sqrt(_snake_case			)			)
      lowerCAmelCase					:	str       =   image_grid(_snake_case ,   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__		:     Union[str, 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__		:     Optional[Any]    				=				AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='''vae''')
snake_case__		:     Optional[int]    				=				UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''unet''')

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







"""simple docstring"""





# 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__			):
     __UpperCamelCase  =					'''philschmid/bart-large-cnn-samsum'''
     __UpperCamelCase  =					(
         '''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.'''
     )
     __UpperCamelCase  =					'''summarizer'''
     __UpperCamelCase  =					AutoTokenizer
     __UpperCamelCase  =					AutoModelForSeqaSeqLM

     __UpperCamelCase  =					['''text''']
     __UpperCamelCase  =					['''text''']






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					int				):
           return self.pre_processor(UpperCamelCase_					,				return_tensors='''pt'''					,				truncation=UpperCamelCase_				)






     def      lowerCamelCase__     (	self  :					Optional[int]					,				UpperCamelCase_  :					str				):
           return self.model.generate(**UpperCamelCase_				)[0]






     def      lowerCamelCase__     (	self  :					Any					,				UpperCamelCase_  :					Tuple				):
           return self.pre_processor.decode(UpperCamelCase_					,				skip_special_tokens=UpperCamelCase_					,				clean_up_tokenization_spaces=UpperCamelCase_				)


