




'''simple docstring'''





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def      UpperCamelCase_(	snake_case							:       Optional[int]     ):

							'''simple docstring'''






							if (
							    (cp >= 0X4E00 and cp <= 0X9FFF)
							    or (cp >= 0X3400 and cp <= 0X4DBF)  #
							    or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
							    or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
							    or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
							    or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
							    or (cp >= 0XF900 and cp <= 0XFAFF)
							    or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
							):  #
														return True

							return False


def      UpperCamelCase_(	snake_case							:       str     ):

							'''simple docstring'''






							for char in word:
														snake_case_						=      ord(snake_case     )
														if not _is_chinese_char(snake_case     ):
																					return 0
							return 1


def      UpperCamelCase_(	snake_case							:       List[str]     ):

							'''simple docstring'''






							snake_case_						=      set()

							for token in tokens:
														snake_case_						=      len(snake_case     ) > 1 and is_chinese(snake_case     )
														if chinese_word:
																					word_set.add(snake_case     )
							snake_case_						=      list(snake_case     )
							return word_list


def      UpperCamelCase_(	snake_case							:       List[str]							,				snake_case							:       set()     ):

							'''simple docstring'''






							if not chinese_word_set:
														return bert_tokens
							snake_case_						=      max([len(snake_case     ) for w in chinese_word_set]     )

							snake_case_						=      bert_tokens
							snake_case_					,      snake_case_						=      0, len(snake_case     )
							while start < end:
														snake_case_						=      True
														if is_chinese(bert_word[start]     ):
																					snake_case_						=      min(end - start							,				snake_case     )
																					for i in range(snake_case							,				1							,				-1     ):
																												snake_case_						=      "".join(bert_word[start : start + i]     )
																												if whole_word in chinese_word_set:
																																			for j in range(start + 1							,				start + i     ):
																																										snake_case_						=      "##" + bert_word[j]
																																			snake_case_						=      start + i
																																			snake_case_						=      False
																																			break
														if single_word:
																					start += 1
							return bert_word


def      UpperCamelCase_(	snake_case							:       List[str]							,				snake_case							:       LTP							,				snake_case							:       BertTokenizer     ):

							'''simple docstring'''






							snake_case_						=      []

							for i in range(0							,				len(snake_case     )							,				1_0_0     ):
														snake_case_						=      ltp_tokenizer.seg(lines[i : i + 1_0_0]     )[0]
														snake_case_						=      [get_chinese_word(snake_case     ) for r in res]
														ltp_res.extend(snake_case     )
							assert len(snake_case     ) == len(snake_case     )

							snake_case_						=      []
							for i in range(0							,				len(snake_case     )							,				1_0_0     ):
														snake_case_						=      bert_tokenizer(lines[i : i + 1_0_0]							,				add_special_tokens=snake_case							,				truncation=snake_case							,				max_length=5_1_2     )
														bert_res.extend(res["input_ids"]     )
							assert len(snake_case     ) == len(snake_case     )

							snake_case_						=      []
							for input_ids, chinese_word in zip(snake_case							,				snake_case     ):
														snake_case_						=      []
														for id in input_ids:
																					snake_case_						=      bert_tokenizer._convert_id_to_token(snake_case     )
																					input_tokens.append(snake_case     )
														snake_case_						=      add_sub_symbol(snake_case							,				snake_case     )
														snake_case_						=      []
														# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
														for i, token in enumerate(snake_case     ):
																					if token[:2] == "##":
																												snake_case_						=      token[2:]
																												# save chinese tokens' pos
																												if len(snake_case     ) == 1 and _is_chinese_char(ord(snake_case     )     ):
																																			ref_id.append(snake_case     )
														ref_ids.append(snake_case     )

							assert len(snake_case     ) == len(snake_case     )

							return ref_ids


def      UpperCamelCase_(	snake_case							:       int     ):

							'''simple docstring'''






							with open(args.file_name							,				"r"							,				encoding="utf-8"     ) as f:
														snake_case_						=      f.readlines()
							snake_case_						=      [line.strip() for line in data if len(snake_case     ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
							snake_case_						=      LTP(args.ltp     )  # faster in GPU device
							snake_case_						=      BertTokenizer.from_pretrained(args.bert     )

							snake_case_						=      prepare_ref(snake_case							,				snake_case							,				snake_case     )

							with open(args.save_path							,				"w"							,				encoding="utf-8"     ) as f:
														snake_case_						=      [json.dumps(snake_case     ) + "\n" for ref in ref_ids]
														f.writelines(snake_case     )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   Dict      		= argparse.ArgumentParser(description="prepare_chinese_ref")
		parser.add_argument(
		    "--file_name",
		    type=str,
		    default="./resources/chinese-demo.txt",
		    help="file need process, same as training data in lm",
		)
		parser.add_argument(
		    "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
		)
		parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
		parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

		_SCREAMING_SNAKE_CASE							:   str      		= parser.parse_args()
		main(args)











"""simple docstring"""




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		Any	=				torch.cat([classifier_free_guidance_embeddings, image_embeddings]      ,					dim=0      )

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								a      :		List[str]	=				prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


