

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__       (  )  ->     Optional[Any]:
	__lowerCamelCase				:				Tuple					       = argparse.ArgumentParser()
	parser.add_argument(
	    '-m'	, '--pretrained_model_name_or_path'	, type=lowerCamelCase__	, default=lowerCamelCase__	, required=lowerCamelCase__	, help='Path to pretrained model or model identifier from huggingface.co/models.'	, )
	parser.add_argument(
	    '-c'	, '--caption'	, type=lowerCamelCase__	, default='robotic cat with wings'	, help='Text used to generate images.'	, )
	parser.add_argument(
	    '-n'	, '--images_num'	, type=lowerCamelCase__	, default=4	, help='How much images to generate.'	, )
	parser.add_argument(
	    '-s'	, '--seed'	, type=lowerCamelCase__	, default=4_2	, help='Seed for random process.'	, )
	parser.add_argument(
	    '-ci'	, '--cuda_id'	, type=lowerCamelCase__	, default=0	, help='cuda_id.'	, )
	__lowerCamelCase				:				Any					       = parser.parse_args()
	return args






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	if not len(lowerCamelCase__			) == rows * cols:
		raise ValueError('The specified number of rows and columns are not correct.'			)

	__lowerCamelCase       , __lowerCamelCase				:				Optional[int]					       = imgs[0].size
	__lowerCamelCase				:				List[Any]					       = Image.new('RGB'	, size=(cols * w, rows * h)			)
	__lowerCamelCase       , __lowerCamelCase				:				List[str]					       = grid.size

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






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__="robotic cat with wings"	, lowerCamelCase__=7.5	, lowerCamelCase__=5_0	, lowerCamelCase__=1	, lowerCamelCase__=4_2	, )  ->     int:
	__lowerCamelCase				:				Any					       = torch.Generator(pipeline.device			).manual_seed(lowerCamelCase__			)
	__lowerCamelCase				:				int					       = pipeline(
	    lowerCamelCase__	, guidance_scale=lowerCamelCase__	, num_inference_steps=lowerCamelCase__	, generator=lowerCamelCase__	, num_images_per_prompt=lowerCamelCase__	, ).images
	__lowerCamelCase				:				Union[str, Any]					       = int(math.sqrt(lowerCamelCase__			)			)
	__lowerCamelCase				:				str					       = image_grid(lowerCamelCase__	, rows=_rows	, cols=num_images_per_prompt // _rows			)
	return grid, images


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

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






import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('-f'       )
				UpperCAmelCase__             =							parser.parse_args()
				return args.f







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



