



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 A						(       )						->      Tuple:
			A__       =    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.' ,	)
			A__       =    parser.parse_args()
			return args


def A						(       __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)						->      Optional[Any]:
			if not len(__UpperCamelCase					) == rows * cols:
						raise ValueError('The specified number of rows and columns are not correct.'					)

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

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


def A						(       __UpperCamelCase ,	__UpperCamelCase="robotic cat with wings" ,	__UpperCamelCase=7.5 ,	__UpperCamelCase=50 ,	__UpperCamelCase=1 ,	__UpperCamelCase=42 ,	)						->      Union[str, Any]:
			A__       =    torch.Generator(pipeline.device					).manual_seed(__UpperCamelCase					)
			A__       =    pipeline(
			    __UpperCamelCase ,	guidance_scale=__UpperCamelCase ,	num_inference_steps=__UpperCamelCase ,	generator=__UpperCamelCase ,	num_images_per_prompt=__UpperCamelCase ,	).images
			A__       =    int(math.sqrt(__UpperCamelCase					)					)
			A__       =    image_grid(__UpperCamelCase ,	rows=_rows ,	cols=num_images_per_prompt // _rows					)
			return grid, images


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

SCREAMING_SNAKE_CASE__       =      StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__       =      lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, '''best_model.pt''')):
							SCREAMING_SNAKE_CASE__       =      load(args.pretrained_model_name_or_path, model=unet)
							unet.eval()
							setattr(pipeline, '''unet''', unet)
else:
							SCREAMING_SNAKE_CASE__       =      unet.to(torch.device('''cuda''', args.cuda_id))
SCREAMING_SNAKE_CASE__       =      pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__       =      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()))))
SCREAMING_SNAKE_CASE__       =      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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							SCREAMING_SNAKE_CASE__       =      None

SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__       =      {
    '''google/rembert''': 2_5_6,
}

SCREAMING_SNAKE_CASE__       =      '''▁'''


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Any			       =    VOCAB_FILES_NAMES
					A__				:		str			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		str			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		int			       =    RemBertTokenizer



					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Any=None				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Dict=False				,					_snake_case					:       Dict="[CLS]"				,					_snake_case					:       List[Any]="[SEP]"				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       List[str]="[SEP]"				,					_snake_case					:       List[str]="<pad>"				,					_snake_case					:       str="[CLS]"				,					_snake_case					:       Any="[MASK]"				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								A__       =    AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					) if isinstance(_snake_case				,					_snake_case					) else mask_token

								super().__init__(
								    _snake_case				,					tokenizer_file=_snake_case				,					do_lower_case=_snake_case				,					remove_space=_snake_case				,					keep_accents=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					unk_token=_snake_case				,					sep_token=_snake_case				,					pad_token=_snake_case				,					cls_token=_snake_case				,					mask_token=_snake_case				,					**_snake_case				,					)

								A__       =    do_lower_case
								A__       =    remove_space
								A__       =    keep_accents
								A__       =    vocab_file
								A__       =    False if not self.vocab_file else True



					def     _a							(     self					:       Any				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None				,					_snake_case					:       bool = False					):






								"""simple docstring"""


								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'					)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
											return [1] + ([0] * len(_snake_case					)) + [1] + ([0] * len(_snake_case					)) + [1]
								return [1] + ([0] * len(_snake_case					)) + [1]



					def     _a							(     self					:       Dict				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



					def     _a							(     self					:       Any				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								if not os.path.isdir(_snake_case					):
											logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case					)					)
											return
								A__       =    os.path.join(
								    _snake_case				,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
											copyfile(self.vocab_file				,					_snake_case					)

								return (out_vocab_file,)




