





from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=					logging.get_logger(__name__)

UpperCAmelCase_					=					{
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				List[Any]  						=						"efficientnet"




   def __init__(       self,						__magic_name__ = 3,						__magic_name__ = 600,						__magic_name__ = 2.0,						__magic_name__ = 3.1,						__magic_name__ = 8,						__magic_name__ = [3, 3, 5, 3, 5, 5, 3],						__magic_name__ = [32, 16, 24, 40, 80, 112, 192],						__magic_name__ = [16, 24, 40, 80, 112, 192, 320],						__magic_name__ = [],						__magic_name__ = [1, 2, 2, 2, 1, 2, 1],						__magic_name__ = [1, 2, 2, 3, 3, 4, 1],						__magic_name__ = [1, 6, 6, 6, 6, 6, 6],						__magic_name__ = 0.25,						__magic_name__ = "swish",						__magic_name__ = 2560,						__magic_name__ = "mean",						__magic_name__ = 0.02,						__magic_name__ = 0.001,						__magic_name__ = 0.99,						__magic_name__ = 0.5,						__magic_name__ = 0.2,						**__magic_name__,						)					-> str:






       """simple docstring"""
       super().__init__(**lowerCamelCase_				)

       UpperCamelCase__				:							Any			  = num_channels
       UpperCamelCase__				:							int			  = image_size
       UpperCamelCase__				:							Dict			  = width_coefficient
       UpperCamelCase__				:							str			  = depth_coefficient
       UpperCamelCase__				:							str			  = depth_divisor
       UpperCamelCase__				:							Dict			  = kernel_sizes
       UpperCamelCase__				:							int			  = in_channels
       UpperCamelCase__				:							Dict			  = out_channels
       UpperCamelCase__				:							Tuple			  = depthwise_padding
       UpperCamelCase__				:							Union[str, Any]			  = strides
       UpperCamelCase__				:							Union[str, Any]			  = num_block_repeats
       UpperCamelCase__				:							Any			  = expand_ratios
       UpperCamelCase__				:							Dict			  = squeeze_expansion_ratio
       UpperCamelCase__				:							Optional[int]			  = hidden_act
       UpperCamelCase__				:							Dict			  = hidden_dim
       UpperCamelCase__				:							Optional[int]			  = pooling_type
       UpperCamelCase__				:							Union[str, Any]			  = initializer_range
       UpperCamelCase__				:							Optional[int]			  = batch_norm_eps
       UpperCamelCase__				:							Any			  = batch_norm_momentum
       UpperCamelCase__				:							Tuple			  = dropout_rate
       UpperCamelCase__				:							List[str]			  = drop_connect_rate
       UpperCamelCase__				:							int			  = sum(lowerCamelCase_				) * 4






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Dict  						=						version.parse("1.11"							)




   @property
   def  UpperCamelCase__    (       self				)					-> str:






       """simple docstring"""
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
           ]				)




   @property
   def  UpperCamelCase__    (       self				)					-> Tuple:






       """simple docstring"""
       return 1E-5








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)))


