
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=(DPMSolverSinglestepScheduler,)
				_lowercase    	=(('''num_inference_steps''', 25),)




				def   __a		(      self       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			{
							    "num_train_timesteps": 1_000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "solver_order": 2,
							    "prediction_type": "epsilon",
							    "thresholding": False,
							    "sample_max_value": 1.0,
							    "algorithm_type": "dpmsolver++",
							    "solver_type": "midpoint",
							    "lambda_min_clipped": -float("inf"  ),
							    "variance_type": None,
							}

							config.update(**_UpperCamelCase  )
							return config




				def   __a		(      self       ,       _UpperCamelCase=0       ,       **_UpperCamelCase  )       ->   List[str]:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )
							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       _UpperCamelCase  )
							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(**_UpperCamelCase  )
										lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )
										scheduler.set_timesteps(_UpperCamelCase  )
										# copy over dummy past residuals
										lowerCAmelCase_      			=			dummy_past_residuals[: scheduler.config.solver_order]

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

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

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




				def   __a		(      self  )       ->   str:
							pass




				def   __a		(      self       ,       _UpperCamelCase=0       ,       **_UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			dict(self.forward_default_kwargs  )
							lowerCAmelCase_      			=			kwargs.pop("num_inference_steps"       ,       _UpperCamelCase  )
							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(**_UpperCamelCase  )
										scheduler.set_timesteps(_UpperCamelCase  )

										# 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(_UpperCamelCase  )
													lowerCAmelCase_      			=			scheduler_class.from_pretrained(_UpperCamelCase  )
													# copy over dummy past residuals
													new_scheduler.set_timesteps(_UpperCamelCase  )

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

										lowerCAmelCase_      			=			scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  ).prev_sample
										lowerCAmelCase_      			=			new_scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       **_UpperCamelCase  ).prev_sample

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




				def   __a		(      self       ,       _UpperCamelCase=None       ,       **_UpperCamelCase  )       ->   Tuple:
							if scheduler is None:
										lowerCAmelCase_      			=			self.scheduler_classes[0]
										lowerCAmelCase_      			=			self.get_scheduler_config(**_UpperCamelCase  )
										lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(**_UpperCamelCase  )
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			10
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							scheduler.set_timesteps(_UpperCamelCase  )

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

							return sample




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
							lowerCAmelCase_      			=			50
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter
							scheduler.set_timesteps(_UpperCamelCase  )

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]  ):
										lowerCAmelCase_      			=			model(_UpperCamelCase       ,       _UpperCamelCase  )
										lowerCAmelCase_      			=			scheduler.step(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  ).prev_sample

							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2574  ) < 1e-3




				def   __a		(      self  )       ->   Any:
							for timesteps in [25, 50, 100, 999, 1_000]:
										self.check_over_configs(num_train_timesteps=_UpperCamelCase  )




				def   __a		(      self  )       ->   str:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							lowerCAmelCase_      			=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()  )
							lowerCAmelCase_      			=			self.full_loop(scheduler=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2791  ) < 1e-3

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

							lowerCAmelCase_      			=			self.full_loop(scheduler=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2791  ) < 1e-3




				def   __a		(      self  )       ->   int:
							self.check_over_configs(thresholding=_UpperCamelCase  )
							for order in [1, 2, 3]:
										for solver_type in ["midpoint", "heun"]:
													for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "sample"]:
																			self.check_over_configs(
																			    thresholding=_UpperCamelCase       ,       prediction_type=_UpperCamelCase       ,       sample_max_value=_UpperCamelCase       ,       algorithm_type="dpmsolver++"       ,       solver_order=_UpperCamelCase       ,       solver_type=_UpperCamelCase       ,       )




				def   __a		(      self  )       ->   Optional[Any]:
							for prediction_type in ["epsilon", "v_prediction"]:
										self.check_over_configs(prediction_type=_UpperCamelCase  )




				def   __a		(      self  )       ->   Dict:
							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
										for solver_type in ["midpoint", "heun"]:
													for order in [1, 2, 3]:
																for prediction_type in ["epsilon", "sample"]:
																			self.check_over_configs(
																			    solver_order=_UpperCamelCase       ,       solver_type=_UpperCamelCase       ,       prediction_type=_UpperCamelCase       ,       algorithm_type=_UpperCamelCase       ,       )
																			lowerCAmelCase_      			=			self.full_loop(
																			    solver_order=_UpperCamelCase       ,       solver_type=_UpperCamelCase       ,       prediction_type=_UpperCamelCase       ,       algorithm_type=_UpperCamelCase       ,       )
																			assert not torch.isnan(_UpperCamelCase  ).any(), "Samples have nan numbers"




				def   __a		(      self  )       ->   List[str]:
							self.check_over_configs(lower_order_final=_UpperCamelCase  )
							self.check_over_configs(lower_order_final=_UpperCamelCase  )




				def   __a		(      self  )       ->   Optional[int]:
							self.check_over_configs(lambda_min_clipped=-float("inf"  )  )
							self.check_over_configs(lambda_min_clipped=-5.1  )




				def   __a		(      self  )       ->   List[str]:
							self.check_over_configs(variance_type=_UpperCamelCase  )
							self.check_over_configs(variance_type="learned_range"  )




				def   __a		(      self  )       ->   List[Any]:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
										self.check_over_forward(num_inference_steps=_UpperCamelCase       ,       time_step=0  )




				def   __a		(      self  )       ->   Union[str, Any]:
							lowerCAmelCase_      			=			self.full_loop()
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2791  ) < 1e-3




				def   __a		(      self  )       ->   Optional[int]:
							lowerCAmelCase_      			=			self.full_loop(use_karras_sigmas=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.2248  ) < 1e-3




				def   __a		(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.full_loop(prediction_type="v_prediction"  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.1453  ) < 1e-3




				def   __a		(      self  )       ->   str:
							lowerCAmelCase_      			=			self.full_loop(prediction_type="v_prediction"       ,       use_karras_sigmas=_UpperCamelCase  )
							lowerCAmelCase_      			=			torch.mean(torch.abs(_UpperCamelCase  )  )

							assert abs(result_mean.item() - 0.0649  ) < 1e-3




				def   __a		(      self  )       ->   Tuple:
							lowerCAmelCase_      			=			self.scheduler_classes[0]
							lowerCAmelCase_      			=			self.get_scheduler_config(thresholding=_UpperCamelCase       ,       dynamic_thresholding_ratio=0  )
							lowerCAmelCase_      			=			scheduler_class(**_UpperCamelCase  )

							lowerCAmelCase_      			=			10
							lowerCAmelCase_      			=			self.dummy_model()
							lowerCAmelCase_      			=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(_UpperCamelCase  )

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

							assert sample.dtype == torch.floataa





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

import sentencepiece as spm

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


_A     =  "▁"

_A     =  {"vocab_file": "spiece.model"}

_A     =  {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A     =  {
    "google/pegasus-xsum": 5_12,
}


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			__a ):
				_lowercase    	=VOCAB_FILES_NAMES

				_lowercase    	=VOCAB_FILES_NAMES
				_lowercase    	=PRETRAINED_VOCAB_FILES_MAP
				_lowercase    	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowercase    	=['''input_ids''', '''attention_mask''']




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase="<pad>"       ,       _UpperCamelCase="</s>"       ,       _UpperCamelCase="<unk>"       ,       _UpperCamelCase="<mask_2>"       ,       _UpperCamelCase="<mask_1>"       ,       _UpperCamelCase=None       ,       _UpperCamelCase=103       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   None:
							lowerCAmelCase_      			=			offset
							if additional_special_tokens is not None:
										if not isinstance(_UpperCamelCase       ,       _UpperCamelCase  ):
													raise TypeError(
													    f"""additional_special_tokens should be of type {type(_UpperCamelCase  )}, but is"""
													    f""" {type(_UpperCamelCase  )}"""  )

										lowerCAmelCase_      			=			(
										    ([mask_token_sent] + additional_special_tokens)
										    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
										    else additional_special_tokens
										)
										# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
										additional_special_tokens_extended += [
										    f"""<unk_{i}>""" for i in range(len(_UpperCamelCase  )       ,       self.offset - 1  )
										]

										if len(set(_UpperCamelCase  )  ) != len(_UpperCamelCase  ):
													raise ValueError(
													    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
													    f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""  )
										lowerCAmelCase_      			=			additional_special_tokens_extended
							else:
										lowerCAmelCase_      			=			[mask_token_sent] if mask_token_sent is not None else []
										additional_special_tokens += [f"""<unk_{i}>""" for i in range(2       ,       self.offset  )]

							lowerCAmelCase_      			=			{} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=_UpperCamelCase       ,       unk_token=_UpperCamelCase       ,       mask_token=_UpperCamelCase       ,       pad_token=_UpperCamelCase       ,       mask_token_sent=_UpperCamelCase       ,       offset=_UpperCamelCase       ,       additional_special_tokens=_UpperCamelCase       ,       sp_model_kwargs=self.sp_model_kwargs       ,       **_UpperCamelCase       ,       )
							lowerCAmelCase_      			=			mask_token_sent
							lowerCAmelCase_      			=			vocab_file
							lowerCAmelCase_      			=			spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(_UpperCamelCase  )

							# add special tokens to encoder dict
							lowerCAmelCase_      			=			{
							    0: self.pad_token,
							    1: self.eos_token,
							}

							if self.mask_token_sent is not None:
										self.encoder.update(
										    {
										        2: self.mask_token_sent,
										        3: self.mask_token,
										    }  )

							if self.offset > 0:
										# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
										# mask_token_sent is already added to list -> so start at 1
										self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,       self.offset - 1  )}  )

							lowerCAmelCase_      			=			{v: k for k, v in self.encoder.items()}




				@property
				def   __a		(      self  )       ->   int:
							return len(self.sp_model  ) + self.offset




				def   __a		(      self  )       ->   Dict[str, int]:
							lowerCAmelCase_      			=			{self.convert_ids_to_tokens(_UpperCamelCase  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab




				def __getstate__(      self  )       ->   List[Any]:
							lowerCAmelCase_      			=			self.__dict__.copy()
							lowerCAmelCase_      			=			None
							return state




				def __setstate__(      self       ,       _UpperCamelCase  )       ->   Optional[int]:
							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       ,       _UpperCamelCase  )       ->   List[str]:
							return self.sp_model.encode(_UpperCamelCase       ,       out_type=_UpperCamelCase  )




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							if token in self.decoder:
										return self.decoder[token]
							elif token in self.added_tokens_decoder:
										return self.added_tokens_decoder[token]
							lowerCAmelCase_      			=			self.sp_model.piece_to_id(_UpperCamelCase  )
							return sp_id + self.offset




				def   __a		(      self       ,       _UpperCamelCase  )       ->   str:
							if index in self.encoder:
										return self.encoder[index]
							elif index in self.added_tokens_encoder:
										return self.added_tokens_encoder[index]
							else:
										lowerCAmelCase_      			=			self.sp_model.IdToPiece(index - self.offset  )
							return token




				def   __a		(      self       ,       _UpperCamelCase  )       ->   Optional[Any]:
							lowerCAmelCase_      			=			[]
							lowerCAmelCase_      			=			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(_UpperCamelCase  ) + token
													lowerCAmelCase_      			=			[]
										else:
													current_sub_tokens.append(_UpperCamelCase  )
							out_string += self.sp_model.decode(_UpperCamelCase  )
							return out_string.strip()




				def   __a		(      self       ,       _UpperCamelCase=False  )       ->   Optional[int]:
							return 1




				def   __a		(      self       ,       _UpperCamelCase  )       ->   int:
							lowerCAmelCase_      			=			set(self.all_special_ids  )  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id  )  # <unk> is only sometimes special

							return [1 if x in all_special_ids else 0 for x in seq]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False  )       ->   List[int]:
							if already_has_special_tokens:
										return self._special_token_mask(_UpperCamelCase  )
							elif token_ids_a is None:
										return self._special_token_mask(_UpperCamelCase  ) + [1]
							else:
										return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase=None  )       ->   List[int]:
							if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   Tuple[str]:
							if not os.path.isdir(_UpperCamelCase  ):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
										return
							lowerCAmelCase_      			=			os.path.join(
							    _UpperCamelCase       ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

							return (out_vocab_file,)



