



import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			(UniPCMultistepScheduler,)
     _A       =			(('num_inference_steps', 25),)







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "solver_order": 2,
               "solver_type": "bh2",
           }

           config.update(**__a    )
           return config







     def 						_a      (self				,			__a=0				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )
           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )
           lowerCamelCase								=		self.dummy_sample
           lowerCamelCase								=		0.1 * sample
           lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )
                 scheduler.set_timesteps(__a    )
                 # copy over dummy past residuals
                 lowerCamelCase								=		dummy_past_residuals[: scheduler.config.solver_order]

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

                 lowerCamelCase			,							lowerCamelCase								=		sample, sample
                 for t in range(__a				,			time_step + scheduler.config.solver_order + 1    ):
                       lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                       lowerCamelCase								=		new_scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample

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







     def 						_a      (self				,			__a=0				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )
           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )
           lowerCamelCase								=		self.dummy_sample
           lowerCamelCase								=		0.1 * sample
           lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config()
                 lowerCamelCase								=		scheduler_class(**__a    )
                 scheduler.set_timesteps(__a    )

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

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

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

                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                 lowerCamelCase								=		new_scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample

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







     def 						_a      (self				,			__a=None				,			**__a    ):






           '''simple docstring'''



           if scheduler is None:
                 lowerCamelCase								=		self.scheduler_classes[0]
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		self.scheduler_classes[0]
           lowerCamelCase								=		self.get_scheduler_config(**__a    )
           lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		10
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter
           scheduler.set_timesteps(__a    )

           for i, t in enumerate(scheduler.timesteps    ):
                 lowerCamelCase								=		model(__a				,			__a    )
                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a    ).prev_sample

           return sample







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )

           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config()
                 lowerCamelCase								=		scheduler_class(**__a    )

                 lowerCamelCase								=		self.dummy_sample
                 lowerCamelCase								=		0.1 * sample

                 if num_inference_steps is not None and hasattr(__a				,			"set_timesteps"    ):
                       scheduler.set_timesteps(__a    )
                 elif num_inference_steps is not None and not hasattr(__a				,			"set_timesteps"    ):
                       lowerCamelCase								=		num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowerCamelCase								=		dummy_past_residuals[: scheduler.config.solver_order]

                 lowerCamelCase								=		scheduler.timesteps[5]
                 lowerCamelCase								=		scheduler.timesteps[6]

                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		UniPCMultistepScheduler(**self.get_scheduler_config()    )
           lowerCamelCase								=		self.full_loop(scheduler=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2464    ) < 1E-3

           lowerCamelCase								=		DPMSolverSinglestepScheduler.from_config(scheduler.config    )
           lowerCamelCase								=		DEISMultistepScheduler.from_config(scheduler.config    )
           lowerCamelCase								=		DPMSolverMultistepScheduler.from_config(scheduler.config    )
           lowerCamelCase								=		UniPCMultistepScheduler.from_config(scheduler.config    )

           lowerCamelCase								=		self.full_loop(scheduler=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2464    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(thresholding=__a    )
           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=__a				,			prediction_type=__a				,			sample_max_value=__a				,			solver_order=__a				,			solver_type=__a				,			)







     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''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=__a				,			solver_type=__a				,			prediction_type=__a				,			)
                             lowerCamelCase								=		self.full_loop(
                                 solver_order=__a				,			solver_type=__a				,			prediction_type=__a				,			)
                             assert not torch.isnan(__a    ).any(), "Samples have nan numbers"







     def 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(lower_order_final=__a    )
           self.check_over_configs(lower_order_final=__a    )







     def 						_a      (self    ):






           '''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=__a				,			time_step=0    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop()
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2464    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop(prediction_type="v_prediction"    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.1014    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.scheduler_classes[0]
           lowerCamelCase								=		self.get_scheduler_config(thresholding=__a				,			dynamic_thresholding_ratio=0    )
           lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		10
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter.half()
           scheduler.set_timesteps(__a    )

           for i, t in enumerate(scheduler.timesteps    ):
                 lowerCamelCase								=		model(__a				,			__a    )
                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a    ).prev_sample

           assert sample.dtype == torch.floataa







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )

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




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


a_						:     int	     =		logging.get_logger(__name__)

a_						:     List[str]	     =		{'vocab_file': 'spiece.model'}

a_						:     Optional[Any]	     =		{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    }
}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     def __init__(self				,			__a				,			__a=False				,			__a=True				,			__a=False				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<sep>"				,			__a="<pad>"				,			__a="<cls>"				,			__a="<mask>"				,			__a=["<eop>", "<eod>"]				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		AddedToken(__a				,			lstrip=__a				,			rstrip=__a    ) if isinstance(__a				,			__a    ) else mask_token

           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__a				,			remove_space=__a				,			keep_accents=__a				,			bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			sep_token=__a				,			pad_token=__a				,			cls_token=__a				,			mask_token=__a				,			additional_special_tokens=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		3

           lowerCamelCase								=		do_lower_case
           lowerCamelCase								=		remove_space
           lowerCamelCase								=		keep_accents
           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )

           try:
                 import jieba
           except ModuleNotFoundError as error:
                 raise error.__class__(
                     "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                     "See https://pypi.org/project/jieba/ for installation."    )
           lowerCamelCase								=		jieba
           lowerCamelCase								=		str.maketrans(" \n"				,			"\u2582\u2583"    )







     @property
     # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
     def 						_a      (self    ):






           '''simple docstring'''



           return len(self.sp_model    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{self.convert_ids_to_tokens(__a    ): i for i in range(self.vocab_size    )}
           vocab.update(self.added_tokens_encoder    )
           return vocab







     def __getstate__(self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.__dict__.copy()
           lowerCamelCase								=		None
           return state







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

           # for backward compatibility
           if not hasattr(self				,			"sp_model_kwargs"    ):
                 lowerCamelCase								=		{}

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           if self.remove_space:
                 lowerCamelCase								=		" ".join(inputs.strip().split()    )
           else:
                 lowerCamelCase								=		inputs
           lowerCamelCase								=		outputs.replace("``"				,			"\""    ).replace("''"				,			"\""    )

           if not self.keep_accents:
                 lowerCamelCase								=		unicodedata.normalize("NFKD"				,			__a    )
                 lowerCamelCase								=		"".join([c for c in outputs if not unicodedata.combining(__a    )]    )
           if self.do_lower_case:
                 lowerCamelCase								=		outputs.lower()

           return outputs







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.preprocess_text(__a    )
           lowerCamelCase								=		self.sp_model.encode(__a				,			out_type=__a    )
           lowerCamelCase								=		[]
           for piece in pieces:
                 if len(__a    ) > 1 and piece[-1] == str(","    ) and piece[-2].isdigit():
                       lowerCamelCase								=		self.sp_model.EncodeAsPieces(piece[:-1].replace(__a				,			""    )    )
                       if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]    ) == 1:
                                   lowerCamelCase								=		cur_pieces[1:]
                             else:
                                   lowerCamelCase								=		cur_pieces[0][1:]
                       cur_pieces.append(piece[-1]    )
                       new_pieces.extend(__a    )
                 else:
                       new_pieces.append(__a    )

           return new_pieces







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.PieceToId(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.IdToPiece(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		"".join(__a    ).replace(__a				,			" "    ).strip()
           return out_string







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[self.cls_token_id]
           if token_ids_a is None:
                 return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls







     def 						_a      (self				,			__a				,			__a = None				,			__a = False    ):






           '''simple docstring'''



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

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







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[2]

           if token_ids_a is None:
                 return len(token_ids_a + sep    ) * [0] + cls_segment_id
           return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		os.path.join(
               __a				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

           if os.path.abspath(self.vocab_file    ) != os.path.abspath(__a    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file				,			__a    )
           elif not os.path.isfile(self.vocab_file    ):
                 with open(__a				,			"wb"    ) as fi:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		super()._decode(*__a				,			**__a    )
           lowerCamelCase								=		text.replace(" "				,			""    ).replace("\u2582"				,			" "    ).replace("\u2583"				,			"\n"    )
           return text