


"""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   UpperCAmelCase__			():




					'''simple docstring'''


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




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_    ,		lowerCAmelCase_  ):




					'''simple docstring'''


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

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

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




def   UpperCAmelCase__			(lowerCAmelCase_    ,		lowerCAmelCase_="robotic cat with wings"    ,		lowerCAmelCase_=7.5    ,		lowerCAmelCase_=50    ,		lowerCAmelCase_=1    ,		lowerCAmelCase_=42    ,		):




					'''simple docstring'''


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


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

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

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece_bpe.model")





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BartphoTokenizer
    _lowerCamelCase					=   False
    _lowerCamelCase					=   True




    def UpperCamelCase__		( self    )    ->      str:
          super().setUp()

          A				  =		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
          A				  =		dict(zip(lowerCamelCase_ ,range(len(lowerCamelCase_    )    )    )    )
          A				  =		{"""unk_token""": """<unk>"""}

          A				  =		os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    )
          with open(self.monolingual_vocab_file ,"""w""" ,encoding="""utf-8"""    ) as fp:
                for token in vocab_tokens:
                      fp.write(f'{token} {vocab_tokens[token]}\n'    )

          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          tokenizer.save_pretrained(self.tmpdirname    )




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      Tuple:
          kwargs.update(self.special_tokens_map    )
          return BartphoTokenizer.from_pretrained(self.tmpdirname ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		"""This is a là test"""
          A				  =		"""This is a<unk><unk> test"""
          return input_text, output_text




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          A				  =		"""This is a là test"""
          A				  =		"""▁This ▁is ▁a ▁l à ▁t est""".split()
          A				  =		tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokens + [tokenizer.unk_token]
          A				  =		[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,lowerCamelCase_    )




