

"""simple docstring"""






import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def   lowercase_	(    _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					if (
					    (cp >= 0x4e_00 and cp <= 0x9f_ff)
					    or (cp >= 0x34_00 and cp <= 0x4d_bf)  #
					    or (cp >= 0x2_00_00 and cp <= 0x2_a6_df)  #
					    or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f)  #
					    or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f)  #
					    or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af)  #
					    or (cp >= 0xf9_00 and cp <= 0xfa_ff)
					    or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f)  #
					):  #
										return True

					return False



def   lowercase_	(    _lowercase       :		str	):




					'''simple docstring'''






					for char in word:
										UpperCAmelCase			:   str      =				ord(_lowercase	)
										if not _is_chinese_char(_lowercase	):
															return 0
					return 1



def   lowercase_	(    _lowercase       :		List[str]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				set()

					for token in tokens:
										UpperCAmelCase			:   Optional[int]      =				len(_lowercase	) > 1 and is_chinese(_lowercase	)
										if chinese_word:
															word_set.add(_lowercase	)
					UpperCAmelCase			:   Dict      =				list(_lowercase	)
					return word_list



def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		set()	):




					'''simple docstring'''






					if not chinese_word_set:
										return bert_tokens
					UpperCAmelCase			:   Optional[Any]      =				max([len(_lowercase	) for w in chinese_word_set]	)

					UpperCAmelCase			:   str      =				bert_tokens
					UpperCAmelCase			:   Optional[int]      =				0, len(_lowercase	)
					while start < end:
										UpperCAmelCase			:   Tuple      =				True
										if is_chinese(bert_word[start]	):
															UpperCAmelCase			:   str      =				min(end - start							,      _lowercase	)
															for i in range(_lowercase							,      1							,      -1	):
																				UpperCAmelCase			:   Any      =				"".join(bert_word[start : start + i]	)
																				if whole_word in chinese_word_set:
																									for j in range(start + 1							,      start + i	):
																														UpperCAmelCase			:   List[str]      =				"##" + bert_word[j]
																									UpperCAmelCase			:   List[Any]      =				start + i
																									UpperCAmelCase			:   int      =				False
																									break
										if single_word:
															start += 1
					return bert_word



def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		LTP							,      _lowercase       :		BertTokenizer	):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				[]

					for i in range(0							,      len(_lowercase	)							,      1_00	):
										UpperCAmelCase			:   Union[str, Any]      =				ltp_tokenizer.pipeline(lines[i : i + 1_00]							,      tasks=["cws"]	).cws
										UpperCAmelCase			:   str      =				[get_chinese_word(_lowercase	) for r in res]
										ltp_res.extend(_lowercase	)
					assert len(_lowercase	) == len(_lowercase	)

					UpperCAmelCase			:   Dict      =				[]
					for i in range(0							,      len(_lowercase	)							,      1_00	):
										UpperCAmelCase			:   Tuple      =				bert_tokenizer(lines[i : i + 1_00]							,      add_special_tokens=_lowercase							,      truncation=_lowercase							,      max_length=5_12	)
										bert_res.extend(res["input_ids"]	)
					assert len(_lowercase	) == len(_lowercase	)

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

					assert len(_lowercase	) == len(_lowercase	)

					return ref_ids



def   lowercase_	(    _lowercase       :		List[Any]	):




					'''simple docstring'''






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

					UpperCAmelCase			:   Dict      =				prepare_ref(_lowercase							,      _lowercase							,      _lowercase	)

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


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

	snake_case_   :   Union[str, Any]        =       parser.parse_args()
	main(args)





"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						(UniPCMultistepScheduler,)
			SCREAMING_SNAKE_CASE__     				=						(('''num_inference_steps''', 25),)







			def 			__lowerCAmelCase				(			self			: Tuple			,  **lowercase			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "solver_type": "bh2",
								}

								config.update(**lowercase  )
								return config







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[str]=0			,  **lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Dict      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   str      =				self.dummy_sample
								UpperCAmelCase			:   Tuple      =				0.1 * sample
								UpperCAmelCase			:   int      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Optional[Any]      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )
													# copy over dummy past residuals
													UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase  )
																		UpperCAmelCase			:   Any      =				scheduler_class.from_pretrained(lowercase  )
																		new_scheduler.set_timesteps(lowercase  )
																		# copy over dummy past residuals
																		UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: new_scheduler.config.solver_order]

													UpperCAmelCase      ,							UpperCAmelCase			:   List[Any]      =				sample, sample
													for t in range(lowercase			,  time_step + scheduler.config.solver_order + 1  ):
																		UpperCAmelCase			:   List[str]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
																		UpperCAmelCase			:   List[Any]      =				new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

																		assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: int=0			,  **lowercase			: Tuple  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				dict(self.forward_default_kwargs  )
								UpperCAmelCase			:   Union[str, Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )
								UpperCAmelCase			:   Tuple      =				self.dummy_sample
								UpperCAmelCase			:   Any      =				0.1 * sample
								UpperCAmelCase			:   int      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Optional[int]      =				self.get_scheduler_config()
													UpperCAmelCase			:   List[Any]      =				scheduler_class(**lowercase  )
													scheduler.set_timesteps(lowercase  )

													# copy over dummy past residuals (must be after setting timesteps)
													UpperCAmelCase			:   Dict      =				dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(lowercase  )
																		UpperCAmelCase			:   str      =				scheduler_class.from_pretrained(lowercase  )
																		# copy over dummy past residuals
																		new_scheduler.set_timesteps(lowercase  )

																		# copy over dummy past residual (must be after setting timesteps)
																		UpperCAmelCase			:   Tuple      =				dummy_past_residuals[: new_scheduler.config.solver_order]

													UpperCAmelCase			:   Dict      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   Any      =				new_scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

													assert torch.sum(torch.abs(output - new_output  )  ) < 1E-5, "Scheduler outputs are not identical"







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: Optional[int]=None			,  **lowercase			: int  ):





								'''simple docstring'''



								if scheduler is None:
													UpperCAmelCase			:   Dict      =				self.scheduler_classes[0]
													UpperCAmelCase			:   int      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   str      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Optional[int]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   Dict      =				self.get_scheduler_config(**lowercase  )
								UpperCAmelCase			:   int      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   List[str]      =				10
								UpperCAmelCase			:   Optional[Any]      =				self.dummy_model()
								UpperCAmelCase			:   Union[str, Any]      =				self.dummy_sample_deter
								scheduler.set_timesteps(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   List[str]      =				model(lowercase			,  lowercase  )
													UpperCAmelCase			:   Optional[Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample

								return sample







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				dict(self.forward_default_kwargs  )

								UpperCAmelCase			:   List[Any]      =				kwargs.pop("num_inference_steps"			,  lowercase  )

								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   str      =				self.get_scheduler_config()
													UpperCAmelCase			:   Dict      =				scheduler_class(**lowercase  )

													UpperCAmelCase			:   Any      =				self.dummy_sample
													UpperCAmelCase			:   Any      =				0.1 * sample

													if num_inference_steps is not None and hasattr(lowercase			,  "set_timesteps"  ):
																		scheduler.set_timesteps(lowercase  )
													elif num_inference_steps is not None and not hasattr(lowercase			,  "set_timesteps"  ):
																		UpperCAmelCase			:   str      =				num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													UpperCAmelCase			:   Union[str, Any]      =				[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													UpperCAmelCase			:   Dict      =				dummy_past_residuals[: scheduler.config.solver_order]

													UpperCAmelCase			:   str      =				scheduler.timesteps[5]
													UpperCAmelCase			:   List[Any]      =				scheduler.timesteps[6]

													UpperCAmelCase			:   Union[str, Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample
													UpperCAmelCase			:   int      =				scheduler.step(lowercase			,  lowercase			,  lowercase			,  **lowercase  ).prev_sample

													self.assertEqual(output_a.shape			,  sample.shape  )
													self.assertEqual(output_a.shape			,  output_a.shape  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				UniPCMultistepScheduler(**self.get_scheduler_config()  )
								UpperCAmelCase			:   Tuple      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3

								UpperCAmelCase			:   int      =				DPMSolverSinglestepScheduler.from_config(scheduler.config  )
								UpperCAmelCase			:   List[str]      =				DEISMultistepScheduler.from_config(scheduler.config  )
								UpperCAmelCase			:   Optional[int]      =				DPMSolverMultistepScheduler.from_config(scheduler.config  )
								UpperCAmelCase			:   int      =				UniPCMultistepScheduler.from_config(scheduler.config  )

								UpperCAmelCase			:   Dict      =				self.full_loop(scheduler=lowercase  )
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=lowercase  )







			def 			__lowerCAmelCase				(			self			: Union[str, Any]  ):





								'''simple docstring'''



								self.check_over_configs(thresholding=lowercase  )
								for order in [1, 2, 3]:
													for solver_type in ["bh1", "bh2"]:
																		for threshold in [0.5, 1.0, 2.0]:
																							for prediction_type in ["epsilon", "sample"]:
																												self.check_over_configs(
																												    thresholding=lowercase			,  prediction_type=lowercase			,  sample_max_value=lowercase			,  solver_order=lowercase			,  solver_type=lowercase			,  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=lowercase  )







			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								for solver_type in ["bh1", "bh2"]:
													for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																							self.check_over_configs(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							UpperCAmelCase			:   Union[str, Any]      =				self.full_loop(
																							    solver_order=lowercase			,  solver_type=lowercase			,  prediction_type=lowercase			,  )
																							assert not torch.isnan(lowercase  ).any(), "Samples have nan numbers"







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.check_over_configs(lower_order_final=lowercase  )
								self.check_over_configs(lower_order_final=lowercase  )







			def 			__lowerCAmelCase				(			self			: Tuple  ):





								'''simple docstring'''



								for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=lowercase			,  time_step=0  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop()
								UpperCAmelCase			:   Any      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.2_4_6_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				self.full_loop(prediction_type="v_prediction"  )
								UpperCAmelCase			:   int      =				torch.mean(torch.abs(lowercase  )  )

								assert abs(result_mean.item() - 0.1_0_1_4  ) < 1E-3







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.scheduler_classes[0]
								UpperCAmelCase			:   int      =				self.get_scheduler_config(thresholding=lowercase			,  dynamic_thresholding_ratio=0  )
								UpperCAmelCase			:   Tuple      =				scheduler_class(**lowercase  )

								UpperCAmelCase			:   Any      =				10
								UpperCAmelCase			:   Dict      =				self.dummy_model()
								UpperCAmelCase			:   List[str]      =				self.dummy_sample_deter.half()
								scheduler.set_timesteps(lowercase  )

								for i, t in enumerate(scheduler.timesteps  ):
													UpperCAmelCase			:   Any      =				model(lowercase			,  lowercase  )
													UpperCAmelCase			:   List[Any]      =				scheduler.step(lowercase			,  lowercase			,  lowercase  ).prev_sample

								assert sample.dtype == torch.floataa







			def 			__lowerCAmelCase				(			self			: List[str]			,  **lowercase			: Optional[int]  ):





								'''simple docstring'''



								for scheduler_class in self.scheduler_classes:
													UpperCAmelCase			:   Union[str, Any]      =				self.get_scheduler_config(**lowercase  )
													UpperCAmelCase			:   Union[str, Any]      =				scheduler_class(**lowercase  )

													scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
													assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps


