

import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Any						      =      (UniPCMultistepScheduler,)
							_UpperCAmelCase    :  List[Any]						      =      (('''num_inference_steps''', 25),)
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,**SCREAMING_SNAKE_CASE__		:			int):
								__lowerCamelCase				:				str					       = {
								    'num_train_timesteps': 1_0_0_0,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'solver_order': 2,
								    'solver_type': 'bh2',
								}

								config.update(**SCREAMING_SNAKE_CASE__)
								return config
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=0   ,**SCREAMING_SNAKE_CASE__		:			List[Any]):
								__lowerCamelCase				:				Union[str, Any]					       = dict(self.forward_default_kwargs)
								__lowerCamelCase				:				List[Any]					       = kwargs.pop('num_inference_steps'   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = self.dummy_sample
								__lowerCamelCase				:				List[Any]					       = 0.1 * sample
								__lowerCamelCase				:				Optional[Any]					       = [residual + 0.2, residual + 0.15, residual + 0.10]

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

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase				:				Optional[int]					       = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__)
										new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)
										# copy over dummy past residuals
										__lowerCamelCase				:				Union[str, Any]					       = dummy_past_residuals[: new_scheduler.config.solver_order]

									__lowerCamelCase       , __lowerCamelCase				:				Union[str, Any]					       = sample, sample
									for t in range(SCREAMING_SNAKE_CASE__   ,time_step + scheduler.config.solver_order + 1):
										__lowerCamelCase				:				Any					       = scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__).prev_sample
										__lowerCamelCase				:				Tuple					       = new_scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__).prev_sample

										assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=0   ,**SCREAMING_SNAKE_CASE__		:			Dict):
								__lowerCamelCase				:				List[Any]					       = dict(self.forward_default_kwargs)
								__lowerCamelCase				:				Any					       = kwargs.pop('num_inference_steps'   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Optional[int]					       = self.dummy_sample
								__lowerCamelCase				:				Dict					       = 0.1 * sample
								__lowerCamelCase				:				Union[str, Any]					       = [residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
									__lowerCamelCase				:				Optional[int]					       = self.get_scheduler_config()
									__lowerCamelCase				:				List[str]					       = scheduler_class(**SCREAMING_SNAKE_CASE__)
									scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)

									# copy over dummy past residuals (must be after setting timesteps)
									__lowerCamelCase				:				Union[str, Any]					       = dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase				:				List[Any]					       = scheduler_class.from_pretrained(SCREAMING_SNAKE_CASE__)
										# copy over dummy past residuals
										new_scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)

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

									__lowerCamelCase				:				Optional[int]					       = scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__).prev_sample
									__lowerCamelCase				:				Tuple					       = new_scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__).prev_sample

									assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
							def     lowerCAmelCase     (		self		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			List[str]=None   ,**SCREAMING_SNAKE_CASE__		:			List[Any]):
								if scheduler is None:
									__lowerCamelCase				:				Any					       = self.scheduler_classes[0]
									__lowerCamelCase				:				Optional[int]					       = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				List[str]					       = scheduler_class(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = self.scheduler_classes[0]
								__lowerCamelCase				:				Optional[Any]					       = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = scheduler_class(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = 1_0
								__lowerCamelCase				:				Any					       = self.dummy_model()
								__lowerCamelCase				:				List[Any]					       = self.dummy_sample_deter
								scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)

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

								return sample
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				List[Any]					       = dict(self.forward_default_kwargs)

								__lowerCamelCase				:				Union[str, Any]					       = kwargs.pop('num_inference_steps'   ,SCREAMING_SNAKE_CASE__)

								for scheduler_class in self.scheduler_classes:
									__lowerCamelCase				:				Any					       = self.get_scheduler_config()
									__lowerCamelCase				:				int					       = scheduler_class(**SCREAMING_SNAKE_CASE__)

									__lowerCamelCase				:				int					       = self.dummy_sample
									__lowerCamelCase				:				Any					       = 0.1 * sample

									if num_inference_steps is not None and hasattr(SCREAMING_SNAKE_CASE__   ,'set_timesteps'):
										scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)
									elif num_inference_steps is not None and not hasattr(SCREAMING_SNAKE_CASE__   ,'set_timesteps'):
										__lowerCamelCase				:				Optional[Any]					       = num_inference_steps

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

									__lowerCamelCase				:				Optional[Any]					       = scheduler.timesteps[5]
									__lowerCamelCase				:				Optional[int]					       = scheduler.timesteps[6]

									__lowerCamelCase				:				Any					       = scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__).prev_sample
									__lowerCamelCase				:				Optional[int]					       = scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__).prev_sample

									self.assertEqual(output_a.shape   ,sample.shape)
									self.assertEqual(output_a.shape   ,output_a.shape)
							def     lowerCAmelCase     (		self		:			Tuple):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								__lowerCamelCase				:				Dict					       = UniPCMultistepScheduler(**self.get_scheduler_config())
								__lowerCamelCase				:				Dict					       = self.full_loop(scheduler=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Any					       = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__))

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

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

								__lowerCamelCase				:				Optional[int]					       = self.full_loop(scheduler=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				List[str]					       = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__))

								assert abs(result_mean.item() - 0.2464) < 1E-3
							def     lowerCAmelCase     (		self		:			int):
								for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Tuple):
								self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__)
								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=SCREAMING_SNAKE_CASE__   ,prediction_type=SCREAMING_SNAKE_CASE__   ,sample_max_value=SCREAMING_SNAKE_CASE__   ,solver_order=SCREAMING_SNAKE_CASE__   ,solver_type=SCREAMING_SNAKE_CASE__   ,)
							def     lowerCAmelCase     (		self		:			List[Any]):
								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			List[str]):
								for solver_type in ["bh1", "bh2"]:
									for order in [1, 2, 3]:
										for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    solver_order=SCREAMING_SNAKE_CASE__   ,solver_type=SCREAMING_SNAKE_CASE__   ,prediction_type=SCREAMING_SNAKE_CASE__   ,)
											__lowerCamelCase				:				Union[str, Any]					       = self.full_loop(
											    solver_order=SCREAMING_SNAKE_CASE__   ,solver_type=SCREAMING_SNAKE_CASE__   ,prediction_type=SCREAMING_SNAKE_CASE__   ,)
											assert not torch.isnan(SCREAMING_SNAKE_CASE__).any(), "Samples have nan numbers"
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__)
								self.check_over_configs(lower_order_final=SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			str):
								for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
									self.check_over_forward(num_inference_steps=SCREAMING_SNAKE_CASE__   ,time_step=0)
							def     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				List[Any]					       = self.full_loop()
								__lowerCamelCase				:				Union[str, Any]					       = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__))

								assert abs(result_mean.item() - 0.2464) < 1E-3
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				Optional[int]					       = self.full_loop(prediction_type='v_prediction')
								__lowerCamelCase				:				Optional[Any]					       = torch.mean(torch.abs(SCREAMING_SNAKE_CASE__))

								assert abs(result_mean.item() - 0.1014) < 1E-3
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Tuple					       = self.scheduler_classes[0]
								__lowerCamelCase				:				Optional[Any]					       = self.get_scheduler_config(thresholding=SCREAMING_SNAKE_CASE__   ,dynamic_thresholding_ratio=0)
								__lowerCamelCase				:				Union[str, Any]					       = scheduler_class(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				int					       = 1_0
								__lowerCamelCase				:				List[Any]					       = self.dummy_model()
								__lowerCamelCase				:				Any					       = self.dummy_sample_deter.half()
								scheduler.set_timesteps(SCREAMING_SNAKE_CASE__)

								for i, t in enumerate(scheduler.timesteps):
									__lowerCamelCase				:				Optional[Any]					       = model(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				Tuple					       = scheduler.step(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__).prev_sample

								assert sample.dtype == torch.floataa


							def     lowerCAmelCase     (		self		:			List[str]   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								for scheduler_class in self.scheduler_classes:
									__lowerCamelCase				:				Any					       = self.get_scheduler_config(**SCREAMING_SNAKE_CASE__)
									__lowerCamelCase				:				str					       = scheduler_class(**SCREAMING_SNAKE_CASE__)

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









'''simple docstring'''






import os
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 logging


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







	def __init__(						self			:     List[Any]		,			__snake_case			:     List[Any]		,			__snake_case			:     Tuple="<s>"		,			__snake_case			:     List[Any]="</s>"		,			__snake_case			:     int="</s>"		,			__snake_case			:     Any="<s>"		,			__snake_case			:     Optional[int]="<unk>"		,			__snake_case			:     Union[str, Any]="<pad>"		,			__snake_case			:     Union[str, Any]="<mask>"		,			__snake_case			:     Optional[Dict[str, Any]] = None		,			**__snake_case			:     Dict		,			)			->		None:
								# Mask token behave like a normal word, i.e. include the space before it
								UpperCAmelCase :       int 							=		AddedToken(__snake_case		,			lstrip=__snake_case		,			rstrip=__snake_case		) if isinstance(__snake_case		,			__snake_case		) else mask_token

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:

								if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase :       Union[str, Any] 							=		[self.cls_token_id]
								UpperCAmelCase :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

								if token_ids_a is None:
															return [1] + ([0] * len(__snake_case		)) + [1]
								return [1] + ([0] * len(__snake_case		)) + [1, 1] + ([0] * len(__snake_case		)) + [1]







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[self.cls_token_id]

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







	@property
	def      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





