



'''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


a							   =				logging.get_logger(__name__)

a							   =				'▁'

a							   =				{'vocab_file': 'sentencepiece.bpe.model'}

a							   =				{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

a							   =				{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		List[str]        =				VOCAB_FILES_NAMES
							UpperCAmelCase			:		Optional[Any]        =				PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase			:		Optional[Any]        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase			:		Dict        =				["""input_ids""", """attention_mask"""]


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

										snake_case:    str      							={} if sp_model_kwargs is None else sp_model_kwargs

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

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

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										snake_case:    Optional[int]      							={'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										snake_case:    Optional[Any]      							=1

										snake_case:    List[str]      							=len(self.sp_model			) + self.fairseq_offset
										snake_case:    Union[str, Any]      							={v: k for k, v in self.fairseq_tokens_to_ids.items()}


							def __getstate__(				self   :						List[str]			)			->					str:
										snake_case:    int      							=self.__dict__.copy()
										snake_case:    int      							=None
										snake_case:    Optional[int]      							=self.sp_model.serialized_model_proto()
										return state


							def __setstate__(				self   :						Tuple      ,						a_   :						Optional[Any]			)			->					Dict:
										snake_case:    Optional[int]      							=d

										# for backward compatibility
										if not hasattr(self      ,						'sp_model_kwargs'			):
													snake_case:    Any      							={}

										snake_case:    List[str]      							=spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)


							def    UpperCamelCase     (				self   :						str      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None			)			->					List[int]:

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


							def    UpperCamelCase     (				self   :						int      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None      ,						a_   :						bool = False			)			->					List[int]:

										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 None:
													return [1] + ([0] * len(a_			)) + [1]
										return [1] + ([0] * len(a_			)) + [1, 1] + ([0] * len(a_			)) + [1]


							def    UpperCamelCase     (				self   :						str      ,						a_   :						List[int]      ,						a_   :						Optional[List[int]] = None			)			->					List[int]:
										snake_case:    Tuple      							=[self.sep_token_id]
										snake_case:    List[Any]      							=[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    UpperCamelCase     (				self   :						List[str]			)			->					Union[str, Any]:
										return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    str      							={self.convert_ids_to_tokens(a_			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab


							def    UpperCamelCase     (				self   :						List[Any]      ,						a_   :						str			)			->					List[str]:
										return self.sp_model.encode(a_      ,						out_type=a_			)


							def    UpperCamelCase     (				self   :						List[str]      ,						a_   :						List[Any]			)			->					Any:
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										snake_case:    int      							=self.sp_model.PieceToId(a_			)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						Any			)			->					List[str]:
										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset			)


							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Optional[int]			)			->					Union[str, Any]:
										snake_case:    List[Any]      							=''.join(a_			).replace(a_      ,						' '			).strip()
										return out_string




							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						str      ,						a_   :						Optional[str] = None			)			->					Tuple[str]:
										if not os.path.isdir(a_			):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
													return
										snake_case:    str      							=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:
																snake_case:    List[str]      							=self.sp_model.serialized_model_proto()
																fi.write(a_			)

										return (out_vocab_file,)









'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

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


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 1_9_2.7_6_1_4			) < 1E-2
										assert abs(result_mean.item() - 0.2_5_1_0			) < 1E-3


							def    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										assert abs(result_sum.item() - 3_4_7.6_3_5_7			) < 1E-2
										assert abs(result_mean.item() - 0.4_5_2_7			) < 1E-3


							def    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

										with self.assertRaises(a_      ,						msg='`timesteps` must be in descending order.'			):
													scheduler.set_timesteps(timesteps=a_			)


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

										with self.assertRaises(a_      ,						msg='Can only pass one of `num_inference_steps` or `timesteps`.'			):
													scheduler.set_timesteps(num_inference_steps=a_      ,						timesteps=a_			)




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

										with self.assertRaises(
										    a_      ,						msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,						):
													scheduler.set_timesteps(timesteps=a_			)




