





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase_					=					logging.get_logger(__name__)

UpperCAmelCase_					=					{
    """hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				Optional[int]  						=						"yolos"




   def __init__(       self,						__magic_name__=768,						__magic_name__=12,						__magic_name__=12,						__magic_name__=3072,						__magic_name__="gelu",						__magic_name__=0.0,						__magic_name__=0.0,						__magic_name__=0.02,						__magic_name__=1E-12,						__magic_name__=[512, 864],						__magic_name__=16,						__magic_name__=3,						__magic_name__=True,						__magic_name__=100,						__magic_name__=True,						__magic_name__=False,						__magic_name__=1,						__magic_name__=5,						__magic_name__=2,						__magic_name__=5,						__magic_name__=2,						__magic_name__=0.1,						**__magic_name__,						)					-> Union[str, Any]:






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

       UpperCamelCase__				:							Dict			  = hidden_size
       UpperCamelCase__				:							str			  = num_hidden_layers
       UpperCamelCase__				:							Dict			  = num_attention_heads
       UpperCamelCase__				:							Union[str, Any]			  = intermediate_size
       UpperCamelCase__				:							Any			  = hidden_act
       UpperCamelCase__				:							Tuple			  = hidden_dropout_prob
       UpperCamelCase__				:							Dict			  = attention_probs_dropout_prob
       UpperCamelCase__				:							Tuple			  = initializer_range
       UpperCamelCase__				:							List[Any]			  = layer_norm_eps
       UpperCamelCase__				:							str			  = image_size
       UpperCamelCase__				:							Dict			  = patch_size
       UpperCamelCase__				:							Dict			  = num_channels
       UpperCamelCase__				:							Union[str, Any]			  = qkv_bias
       UpperCamelCase__				:							Dict			  = num_detection_tokens
       UpperCamelCase__				:							Optional[Any]			  = use_mid_position_embeddings
       UpperCamelCase__				:							str			  = auxiliary_loss
       # Hungarian matcher
       UpperCamelCase__				:							int			  = class_cost
       UpperCamelCase__				:							Dict			  = bbox_cost
       UpperCamelCase__				:							str			  = giou_cost
       # Loss coefficients
       UpperCamelCase__				:							str			  = bbox_loss_coefficient
       UpperCamelCase__				:							Union[str, Any]			  = giou_loss_coefficient
       UpperCamelCase__				:							Optional[int]			  = eos_coefficient






class        lowercase__			(      _UpperCamelCase							):

   '''simple docstring'''


   a		:				List[Any]  						=						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				)					-> int:






       """simple docstring"""
       return 1E-4




   @property
   def  UpperCamelCase__    (       self				)					-> int:






       """simple docstring"""
       return 12








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)))


