



'''simple docstring'''






import json
import os
from typing import Optional, Tuple

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


UpperCamelCase__:   str								=			logging.get_logger(__name__)

UpperCamelCase__:   str								=			{"vocab_file": "vocab.json"}

UpperCamelCase__:   Optional[int]								=			{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

UpperCamelCase__:   List[str]								=			{"mgp-str": 27}

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







	def __init__(						self			:     List[str]		,			__snake_case			:     List[Any]		,			__snake_case			:     Optional[Any]="[GO]"		,			__snake_case			:     List[Any]="[GO]"		,			__snake_case			:     Union[str, Any]="[s]"		,			__snake_case			:     Optional[Any]="[GO]"		,			**__snake_case			:     List[Any]		)			->		Union[str, Any]:
								super().__init__(
								    unk_token=__snake_case		,			bos_token=__snake_case		,			eos_token=__snake_case		,			pad_token=__snake_case		,			**__snake_case		,			)

								with open(__snake_case		,			encoding='''utf-8'''		) as vocab_handle:
															UpperCAmelCase :       Union[str, Any] 							=		json.load(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.vocab.items()}







	@property
	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								return len(self.vocab		)







	def      A	(						self			:     Dict		)			->		List[Any]:
								return dict(self.vocab		,			**self.added_tokens_encoder		)







	def      A	(						self			:     int		,			__snake_case			:     Union[str, Any]		)			->		Dict:
								UpperCAmelCase :       int 							=		[]
								for s in text:
															char_tokens.extend(__snake_case		)
								return char_tokens







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[str]		)			->		List[Any]:
								return self.vocab.get(__snake_case		,			self.vocab.get(self.unk_token		)		)







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Optional[int]		)			->		int:
								return self.decoder.get(__snake_case		)







	def      A	(						self			:     Union[str, Any]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error('''Vocabulary path ({}) should be a directory'''.format(__snake_case		)		)
															return
								UpperCAmelCase :       List[str] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								with open(__snake_case		,			'''w'''		,			encoding='''utf-8'''		) as f:
															f.write(json.dumps(self.vocab		,			indent=2		,			sort_keys=__snake_case		,			ensure_ascii=__snake_case		) + '''\n'''		)

								return (vocab_file,)










'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





