


"""simple docstring"""






import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
							import jieba

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


UpperCAmelCase       :	Union[str, Any]  		=						logging.get_logger(__name__)

UpperCAmelCase       :	List[Any]  		=						{'''vocab_file''': '''vocab.txt'''}

UpperCAmelCase       :	int  		=						{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

UpperCAmelCase       :	int  		=						{
    '''openbmb/cpm-ant-10b''': 1024,
}
def 	lowerCamelCase			(	_UpperCamelCase						: List[Any]				)						->			Any:

							'''simple docstring'''



							__UpperCAmelCase  :		Optional[Any] 				=   collections.OrderedDict()
							with open(UpperCAmelCase_			,     """r"""			,     encoding="""utf-8"""				) as reader:
														__UpperCAmelCase  :		Dict 				=   reader.readlines()
							for index, token in enumerate(UpperCAmelCase_				):
														__UpperCAmelCase  :		Optional[Any] 				=   token.rstrip("""\n"""				)
														__UpperCAmelCase  :		str 				=   index
							return vocab





class 	lowerCamelCase__  (	A							):







					"""simple docstring"""


					def __init__(			self       :					List[Any]					,			UpperCamelCase       :					Dict					,			UpperCamelCase       :					Union[str, Any]="<unk>"					,			UpperCamelCase       :					List[str]=200    ):
												'''simple docstring'''






												__UpperCAmelCase  :		List[Any] 				=   vocab
												__UpperCAmelCase  :		Any 				=   unk_token
												__UpperCAmelCase  :		Optional[int] 				=   max_input_chars_per_word






					def lowerCamelCase__				(			self       :					int					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






												__UpperCAmelCase  :		Optional[Any] 				=   list(UpperCamelCase_    )
												if len(UpperCamelCase_    ) > self.max_input_chars_per_word:
																			return [self.unk_token]

												__UpperCAmelCase  :		Union[str, Any] 				=   0
												__UpperCAmelCase  :		Union[str, Any] 				=   []
												while start < len(UpperCamelCase_    ):
																			__UpperCAmelCase  :		str 				=   len(UpperCamelCase_    )
																			__UpperCAmelCase  :		Union[str, Any] 				=   None
																			while start < end:
																										__UpperCAmelCase  :		str 				=   """""".join(chars[start:end]    )
																										if substr in self.vocab:
																																	__UpperCAmelCase  :		List[str] 				=   substr
																																	break
																										end -= 1
																			if cur_substr is None:
																										sub_tokens.append(self.unk_token    )
																										start += 1
																			else:
																										sub_tokens.append(UpperCamelCase_    )
																										__UpperCAmelCase  :		int 				=   end

												return sub_tokens






class 	lowerCamelCase__  (	A							):







					"""simple docstring"""


					__a      			=				VOCAB_FILES_NAMES
					__a      			=				PRETRAINED_VOCAB_FILES_MAP
					__a      			=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a      			=				["""input_ids""", """attention_mask"""]
					__a      			=				False






					def __init__(			self       :					List[Any]					,			UpperCamelCase       :					Optional[Any]					,			UpperCamelCase       :					Dict="<d>"					,			UpperCamelCase       :					List[str]="</d>"					,			UpperCamelCase       :					Any="<s>"					,			UpperCamelCase       :					List[Any]="</s>"					,			UpperCamelCase       :					int="<pad>"					,			UpperCamelCase       :					Optional[int]="<unk>"					,			UpperCamelCase       :					int="</n>"					,			UpperCamelCase       :					List[Any]="</_>"					,			UpperCamelCase       :					int="left"					,			**UpperCamelCase       :					Any					,			):
												'''simple docstring'''






												requires_backends(self					,			["""jieba"""]    )
												super().__init__(
												    bod_token=UpperCamelCase_					,			eod_token=UpperCamelCase_					,			bos_token=UpperCamelCase_					,			eos_token=UpperCamelCase_					,			pad_token=UpperCamelCase_					,			unk_token=UpperCamelCase_					,			line_token=UpperCamelCase_					,			space_token=UpperCamelCase_					,			padding_side=UpperCamelCase_					,			**UpperCamelCase_					,			)
												__UpperCAmelCase  :		Tuple 				=   bod_token
												__UpperCAmelCase  :		List[Any] 				=   eod_token
												__UpperCAmelCase  :		str 				=   load_vocab(UpperCamelCase_    )
												__UpperCAmelCase  :		Any 				=   self.encoder[space_token]
												__UpperCAmelCase  :		int 				=   self.encoder[line_token]

												del self.encoder[space_token]
												del self.encoder[line_token]

												__UpperCAmelCase  :		Optional[int] 				=   collections.OrderedDict(sorted(self.encoder.items()					,			key=lambda UpperCamelCase    : x[1]    )    )
												__UpperCAmelCase  :		int 				=   {v: k for k, v in self.encoder.items()}

												__UpperCAmelCase  :		List[Any] 				=   WordpieceTokenizer(vocab=self.encoder					,			unk_token=self.unk_token    )






					@property
					def lowerCamelCase__				(			self       :					str    ):
												'''simple docstring'''






												return self.encoder[self.bod_token]






					@property
					def lowerCamelCase__				(			self       :					Optional[int]    ):
												'''simple docstring'''






												return self.encoder[self.eod_token]






					@property
					def lowerCamelCase__				(			self       :					List[str]    ):
												'''simple docstring'''






												return self.encoder["\n"]






					@property
					def lowerCamelCase__				(			self       :					Union[str, Any]    ):
												'''simple docstring'''






												return len(self.encoder    )






					def lowerCamelCase__				(			self       :					str    ):
												'''simple docstring'''






												return dict(self.encoder					,			**self.added_tokens_encoder    )






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Optional[int]    ):
												'''simple docstring'''






												__UpperCAmelCase  :		int 				=   []
												for x in jieba.cut(UpperCamelCase_					,			cut_all=UpperCamelCase_    ):
																			output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase_    )    )
												return output_tokens






					def lowerCamelCase__				(			self       :					Dict					,			UpperCamelCase       :					Any					,			**UpperCamelCase       :					str    ):
												'''simple docstring'''






												__UpperCAmelCase  :		List[Any] 				=   [i for i in token_ids if i >= 0]
												__UpperCAmelCase  :		Any 				=   [
												    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
												]
												return super()._decode(UpperCamelCase_					,			**UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					int					,			UpperCamelCase       :					Union[str, Any]    ):
												'''simple docstring'''






												return token in self.encoder






					def lowerCamelCase__				(			self       :					List[Any]					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






												return "".join(UpperCamelCase_    )






					def lowerCamelCase__				(			self       :					int					,			UpperCamelCase       :					Tuple    ):
												'''simple docstring'''






												return self.encoder.get(UpperCamelCase_					,			self.encoder.get(self.unk_token    )    )






					def lowerCamelCase__				(			self       :					Tuple					,			UpperCamelCase       :					Optional[Any]    ):
												'''simple docstring'''






												return self.decoder.get(UpperCamelCase_					,			self.unk_token    )






					def lowerCamelCase__				(			self       :					str					,			UpperCamelCase       :					Dict					,			UpperCamelCase       :					int = None    ):
												'''simple docstring'''






												if os.path.isdir(UpperCamelCase_    ):
																			__UpperCAmelCase  :		Dict 				=   os.path.join(
																			    UpperCamelCase_					,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
												else:
																			__UpperCAmelCase  :		Union[str, Any] 				=   (filename_prefix + """-""" if filename_prefix else """""") + save_directory
												__UpperCAmelCase  :		str 				=   0
												if " " in self.encoder:
																			__UpperCAmelCase  :		Optional[Any] 				=   self.encoder[""" """]
																			del self.encoder[" "]
												if "\n" in self.encoder:
																			__UpperCAmelCase  :		List[str] 				=   self.encoder["""\n"""]
																			del self.encoder["\n"]
												__UpperCAmelCase  :		str 				=   collections.OrderedDict(sorted(self.encoder.items()					,			key=lambda UpperCamelCase    : x[1]    )    )
												with open(UpperCamelCase_					,			"""w"""					,			encoding="""utf-8"""    ) as writer:
																			for token, token_index in self.encoder.items():
																										if index != token_index:
																																	logger.warning(
																																	    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																																	    """ Please check that the vocabulary is not corrupted!"""    )
																																	__UpperCAmelCase  :		List[Any] 				=   token_index
																										writer.write(token + """\n"""    )
																										index += 1
												return (vocab_file,)






					def lowerCamelCase__				(			self       :					Optional[int]					,			UpperCamelCase       :					List[Any]					,			UpperCamelCase       :					Union[str, Any] = None    ):
												'''simple docstring'''






												if token_ids_a is None:
																			return [self.bos_token_id] + token_ids_a
												return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






					def lowerCamelCase__				(			self       :					Optional[int]					,			UpperCamelCase       :					Dict					,			UpperCamelCase       :					Any = None					,			UpperCamelCase       :					Dict = False    ):
												'''simple docstring'''






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

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







'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



