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  (					)							->					Union[str, Any]:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       Tuple ,     __UpperCamelCase    :       List[Any]	)							->					List[str]:







  """simple docstring"""





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

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

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Union[str, Any]="robotic cat with wings" ,     __UpperCamelCase    :       Tuple=7.5 ,     __UpperCamelCase    :       Tuple=50 ,     __UpperCamelCase    :       List[Any]=1 ,     __UpperCamelCase    :       int=42 ,     )							->					str:







  """simple docstring"""





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


__lowerCamelCase			: Tuple							   =		parse_args()
# Load models and create wrapper for stable diffusion
__lowerCamelCase			: Dict							   =		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='''tokenizer''')
__lowerCamelCase			: Tuple							   =		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='''text_encoder''')
__lowerCamelCase			: Dict							   =		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			: List[str]							   =		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__lowerCamelCase			: List[str]							   =		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			: Union[str, Any]							   =		unet.to(torch.device('''cuda''', args.cuda_id))
__lowerCamelCase			: Optional[Any]							   =		pipeline.to(unet.device)
__lowerCamelCase   ,						__lowerCamelCase			: 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()))))
__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)))

# 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 typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							"Salesforce/blip-image-captioning-base"
   lowerCAmelCase_														=							(
       "This is a tool that generates a description of an image. It takes an input named `image` which should be the "
       "image to caption, and returns a text that contains the description in English."
   )
   lowerCAmelCase_														=							"image_captioner"
   lowerCAmelCase_														=							AutoModelForVisionaSeq

   lowerCAmelCase_														=							["image"]
   lowerCAmelCase_														=							["text"]




   def __init__(    self			:				List[Any]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     requires_backends(self       ,	["""vision"""]      )
     super().__init__(*_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				"Image"      ):



     """simple docstring"""






     return self.pre_processor(images=_lowercase       ,	return_tensors="""pt"""      )




   def 	__a						(    self			:				Union[str, Any]       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     return self.model.generate(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Any      ):



     """simple docstring"""






     return self.pre_processor.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )[0].strip()
