

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    _lowercase			(	)							->					Optional[int]:





	"""simple docstring"""


	_UpperCamelCase			=   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."							,  )
	_UpperCamelCase			=   parser.parse_args()
	return args



def    _lowercase			(	a__						:			List[Any]							,  a__						:			int							,  a__						:			Any					)							->					Dict:





	"""simple docstring"""


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

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

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



def    _lowercase			(	a__						:			int							,  a__						:			Optional[int]="robotic cat with wings"							,  a__						:			List[Any]=7.5							,  a__						:			Optional[int]=50							,  a__						:			List[Any]=1							,  a__						:			Dict=42							,  )							->					Tuple:





	"""simple docstring"""


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


__lowerCAmelCase			     =       parse_args()
# Load models and create wrapper for stable diffusion
__lowerCAmelCase			     =       CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__lowerCAmelCase			     =       CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__lowerCAmelCase			     =       AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__lowerCAmelCase			     =       UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__lowerCAmelCase			     =       StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__lowerCAmelCase			     =       lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
	__lowerCAmelCase			     =       load(args.pretrained_model_name_or_path, model=unet)
	unet.eval()
	setattr(pipeline, """unet""", unet)
else:
	__lowerCAmelCase			     =       unet.to(torch.device("""cuda""", args.cuda_id))
__lowerCAmelCase			     =       pipeline.to(unet.device)
__lowerCAmelCase     , __lowerCAmelCase			     =       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			     =       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)))









import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler



@dataclass
class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					0
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					3.0







class      _UpperCAmelCase      (							unittest.TestCase  ):




     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()			,    {})
         self.assertDictEqual(MockClass(a=2).to_kwargs()			,    {'''a''': 2})
         self.assertDictEqual(MockClass(a=2			,    b=a__).to_kwargs()			,    {'''a''': 2, '''b''': True})
         self.assertDictEqual(MockClass(a=2			,    c=2.2_5).to_kwargs()			,    {'''a''': 2, '''c''': 2.2_5})




     @require_cuda
     def 		snake_case_		(					self):
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         A__					=       GradScalerKwargs(init_scale=1_0_2_4			,    growth_factor=2)
         AcceleratorState._reset_state()
         A__					=       Accelerator(mixed_precision='''fp16'''			,    kwargs_handlers=[scaler_handler])
         print(accelerator.use_fpaa)
         A__					=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale			,    1_0_2_4.0)
         self.assertEqual(scaler._growth_factor			,    2.0)

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor			,    0.5)
         self.assertEqual(scaler._growth_interval			,    2_0_0_0)
         self.assertEqual(scaler._enabled			,    a__)




     @require_multi_gpu
     def 		snake_case_		(					self):
         A__					=       ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__)]
         execute_subprocess_async(a__			,    env=os.environ.copy())


if __name__ == "__main__":
   _lowercase    	=		DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _lowercase    	=		Accelerator(kwargs_handlers=[ddp_scaler])
   _lowercase    	=		torch.nn.Linear(100, 200)
   _lowercase    	=		accelerator.prepare(model)

   # Check the values changed in kwargs
   _lowercase    	=		""
   _lowercase    	=		model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
   if model.find_unused_parameters is not True:
      error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

   # Check the values of the defaults
   if model.dim != 0:
      error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
   if model.broadcast_buffers is not True:
      error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
   if model.gradient_as_bucket_view is not False:
      error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

   # Raise error at the end to make sure we don't stop at the first failure.
   if len(error_msg) > 0:
      raise ValueError(error_msg)
