





'''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_      (		):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  if not len(SCREAMING_SNAKE_CASE__       ) == 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(SCREAMING_SNAKE_CASE__       ):
    grid.paste(SCREAMING_SNAKE_CASE__					,	box=(i % cols * w, i // cols * h)       )
  return grid




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__="robotic cat with wings"					,	SCREAMING_SNAKE_CASE__=7.5					,	SCREAMING_SNAKE_CASE__=50					,	SCREAMING_SNAKE_CASE__=1					,	SCREAMING_SNAKE_CASE__=42					,	):







  '''simple docstring'''



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


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

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



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


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''mobilenet_v1'''

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=224						,   lowerCamelCase=1.0						,   lowerCamelCase=8						,   lowerCamelCase="relu6"						,   lowerCamelCase=True						,   lowerCamelCase=0.999						,   lowerCamelCase=0.02						,   lowerCamelCase=0.001						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)

       if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."	)

       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				depth_multiplier
       _snake_case     		=				min_depth
       _snake_case     		=				hidden_act
       _snake_case     		=				tf_padding
       _snake_case     		=				classifier_dropout_prob
       _snake_case     		=				initializer_range
       _snake_case     		=				layer_norm_eps







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict([("pixel_values", {0: "batch"})]	)

     @property
     def 	UpperCamelCase(					self	):
       if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]	)
       else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]	)






     @property
     def 	UpperCamelCase(					self	):
       return 1e-4


