
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a  			=					logging.get_logger(__name__)

__a  			=					{
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

__a  			=					{
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

__a  			=					{
    'vinai/phobert-base': 2_5_6,
    'vinai/phobert-large': 2_5_6,
}




def 				a						(							snake_case__:					List[str]					):


				'''simple docstring'''
				lowercase_       =					set()
				lowercase_       =					word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)					)
								lowercase_       =					char

				lowercase_       =					set(snake_case__					)
				return pairs


class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	VOCAB_FILES_NAMES
	a			:List[str]         =	PRETRAINED_VOCAB_FILES_MAP
	a			:Tuple         =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	def __init__(       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="<s>"       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="</s>"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="</s>"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]="<s>"       ,							SCREAMING_SNAKE_CASE_  :			List[str]="<unk>"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="<pad>"       ,							SCREAMING_SNAKE_CASE_  :			List[Any]="<mask>"       ,							**SCREAMING_SNAKE_CASE_  :			Optional[Any]       ,							)       ->     Union[str, Any]:
					super().__init__(
					    bos_token=SCREAMING_SNAKE_CASE_       ,							eos_token=SCREAMING_SNAKE_CASE_       ,							unk_token=SCREAMING_SNAKE_CASE_       ,							sep_token=SCREAMING_SNAKE_CASE_       ,							cls_token=SCREAMING_SNAKE_CASE_       ,							pad_token=SCREAMING_SNAKE_CASE_       ,							mask_token=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)

					lowercase_       =					vocab_file
					lowercase_       =					merges_file

					lowercase_       =					{}
					lowercase_       =					0
					lowercase_       =					1
					lowercase_       =					2
					lowercase_       =					3

					self.add_from_file(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					{v: k for k, v in self.encoder.items()}

					with open(SCREAMING_SNAKE_CASE_       ,							encoding='''utf-8'''       ) as merges_handle:
									lowercase_       =					merges_handle.read().split('''\n'''       )[:-1]
					lowercase_       =					[tuple(merge.split()[:-1]       ) for merge in merges]
					lowercase_       =					dict(zip(SCREAMING_SNAKE_CASE_       ,							range(len(SCREAMING_SNAKE_CASE_       )       )       )       )
					lowercase_       =					{}
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[int]] = None       )       ->     List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_       =					[self.cls_token_id]
					lowercase_       =					[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 						_lowercase  (       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			List[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[int]] = None       ,							SCREAMING_SNAKE_CASE_  :			bool = False       )       ->     List[int]:

					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=SCREAMING_SNAKE_CASE_       ,							token_ids_a=SCREAMING_SNAKE_CASE_       ,							already_has_special_tokens=SCREAMING_SNAKE_CASE_       )

					if token_ids_a is None:
									return [1] + ([0] * len(SCREAMING_SNAKE_CASE_       )) + [1]
					return [1] + ([0] * len(SCREAMING_SNAKE_CASE_       )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_       )) + [1]
	def 						_lowercase  (       self  :			Union[str, Any]       ,							SCREAMING_SNAKE_CASE_  :			List[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[List[int]] = None       )       ->     List[int]:
					lowercase_       =					[self.sep_token_id]
					lowercase_       =					[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 + sep + token_ids_a + sep       ) * [0]
	@property
	def 						_lowercase  (       self  :			Any       )       ->     Any:
					return len(self.encoder       )
	def 						_lowercase  (       self  :			Any       )       ->     Dict:
					return dict(self.encoder       ,							**self.added_tokens_encoder       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Any:
					if token in self.cache:
									return self.cache[token]
					lowercase_       =					tuple(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					tuple(list(word[:-1]       ) + [word[-1] + '''</w>''']       )
					lowercase_       =					get_pairs(SCREAMING_SNAKE_CASE_       )

					if not pairs:
									return token

					while True:
									lowercase_       =					min(SCREAMING_SNAKE_CASE_       ,							key=lambda SCREAMING_SNAKE_CASE_       : self.bpe_ranks.get(SCREAMING_SNAKE_CASE_       ,							float('''inf'''       )       )       )
									if bigram not in self.bpe_ranks:
													break
									lowercase_  ,	lowercase_       =					bigram
									lowercase_       =					[]
									lowercase_       =					0
									while i < len(SCREAMING_SNAKE_CASE_       ):
													try:
																	lowercase_       =					word.index(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
													except ValueError:
																	new_word.extend(word[i:]       )
																	break
													else:
																	new_word.extend(word[i:j]       )
																	lowercase_       =					j

													if word[i] == first and i < len(SCREAMING_SNAKE_CASE_       ) - 1 and word[i + 1] == second:
																	new_word.append(first + second       )
																	i += 2
													else:
																	new_word.append(word[i]       )
																	i += 1
									lowercase_       =					tuple(SCREAMING_SNAKE_CASE_       )
									lowercase_       =					new_word
									if len(SCREAMING_SNAKE_CASE_       ) == 1:
													break
									else:
													lowercase_       =					get_pairs(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					'''@@ '''.join(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					word[:-4]
					lowercase_       =					word
					return word
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict       )       ->     int:
					lowercase_       =					[]

					lowercase_       =					re.findall(R'''\S+\n?'''       ,							SCREAMING_SNAKE_CASE_       )

					for token in words:
									split_tokens.extend(list(self.bpe(SCREAMING_SNAKE_CASE_       ).split(''' '''       )       )       )
					return split_tokens
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Union[str, Any]:
					return self.encoder.get(SCREAMING_SNAKE_CASE_       ,							self.encoder.get(self.unk_token       )       )
	def 						_lowercase  (       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]       )       ->     List[Any]:
					return self.decoder.get(SCREAMING_SNAKE_CASE_       ,							self.unk_token       )
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Tuple:
					lowercase_       =					''' '''.join(SCREAMING_SNAKE_CASE_       ).replace('''@@ '''       ,							''''''       ).strip()
					return out_string
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[str] = None       )       ->     Tuple[str]:
					if not os.path.isdir(SCREAMING_SNAKE_CASE_       ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
									return
					lowercase_       =					os.path.join(
					    SCREAMING_SNAKE_CASE_       ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
					lowercase_       =					os.path.join(
					    SCREAMING_SNAKE_CASE_       ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

					if os.path.abspath(self.merges_file       ) != os.path.abspath(SCREAMING_SNAKE_CASE_       ):
									copyfile(self.merges_file       ,							SCREAMING_SNAKE_CASE_       )

					return out_vocab_file, out_merge_file




	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			List[str]       )       ->     Dict:
					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									try:
													with open(SCREAMING_SNAKE_CASE_       ,							'''r'''       ,							encoding='''utf-8'''       ) as fd:
																	self.add_from_file(SCREAMING_SNAKE_CASE_       )
									except FileNotFoundError as fnfe:
													raise fnfe
									except UnicodeError:
													raise Exception(f'''Incorrect encoding detected in {f}, please rebuild the dataset'''       )
									return

					lowercase_       =					f.readlines()
					for lineTmp in lines:
									lowercase_       =					lineTmp.strip()
									lowercase_       =					line.rfind(''' '''       )
									if idx == -1:
													raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt>\''''       )
									lowercase_       =					line[:idx]
									lowercase_       =					len(self.encoder       )






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						(							):


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




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Tuple						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				if not len(snake_case__					) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.'''					)

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

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




def 				a						(							snake_case__:					Tuple						,   snake_case__:					Union[str, Any]="robotic cat with wings"						,   snake_case__:					Union[str, Any]=7.5						,   snake_case__:					List[str]=50						,   snake_case__:					List[Any]=1						,   snake_case__:					Optional[int]=42						,   ):


				'''simple docstring'''
				lowercase_       =					torch.Generator(pipeline.device					).manual_seed(snake_case__					)
				lowercase_       =					pipeline(
				    snake_case__						,   guidance_scale=snake_case__						,   num_inference_steps=snake_case__						,   generator=snake_case__						,   num_images_per_prompt=snake_case__						,   ).images
				lowercase_       =					int(math.sqrt(snake_case__					)					)
				lowercase_       =					image_grid(snake_case__						,   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)))




