





'''simple docstring'''






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class  UpperCAmelCase     (			SCREAMING_SNAKE_CASE_   ):
      _lowercase:			Union[str, Any]						     =   (DDIMParallelScheduler,)
      _lowercase:			List[str]						     =   (('''eta''', 0.0), ('''num_inference_steps''', 50))




      def 				lowercase__    (			self			: str			,			**__snake_case			: Optional[int]  )				->      Any:
            _lowerCAmelCase							     =							{
                'num_train_timesteps': 10_00,
                'beta_start': 0.00_01,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'clip_sample': True,
            }

            config.update(**_a  )
            return config




      def 				lowercase__    (			self			: int			,			**__snake_case			: Dict  )				->      Dict:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config(**_a  )
            _lowerCAmelCase							     =							scheduler_class(**_a  )

            _lowerCAmelCase							     =							10, 0.0

            _lowerCAmelCase							     =							self.dummy_model()
            _lowerCAmelCase							     =							self.dummy_sample_deter

            scheduler.set_timesteps(_a  )

            for t in scheduler.timesteps:
                  _lowerCAmelCase							     =							model(_a			,			_a  )
                  _lowerCAmelCase							     =							scheduler.step(_a			,			_a			,			_a			,			_a  ).prev_sample

            return sample




      def 				lowercase__    (			self			: List[Any]  )				->      Tuple:
            for timesteps in [1_00, 5_00, 10_00]:
                  self.check_over_configs(num_train_timesteps=_a  )




      def 				lowercase__    (			self			: List[Any]  )				->      Optional[Any]:
            for steps_offset in [0, 1]:
                  self.check_over_configs(steps_offset=_a  )

            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config(steps_offset=1  )
            _lowerCAmelCase							     =							scheduler_class(**_a  )
            scheduler.set_timesteps(5  )
            assert torch.equal(scheduler.timesteps			,			torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]  )  )




      def 				lowercase__    (			self			: Any  )				->      List[str]:
            for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,			[0.0_02, 0.02, 0.2, 2]  ):
                  self.check_over_configs(beta_start=_a			,			beta_end=_a  )




      def 				lowercase__    (			self			: int  )				->      List[Any]:
            for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=_a  )




      def 				lowercase__    (			self			: int  )				->      List[Any]:
            for prediction_type in ["epsilon", "v_prediction"]:
                  self.check_over_configs(prediction_type=_a  )




      def 				lowercase__    (			self			: Union[str, Any]  )				->      Any:
            for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=_a  )




      def 				lowercase__    (			self			: Union[str, Any]  )				->      List[Any]:
            for timestep_spacing in ["trailing", "leading"]:
                  self.check_over_configs(timestep_spacing=_a  )




      def 				lowercase__    (			self			: List[Any]  )				->      Dict:
            for rescale_betas_zero_snr in [True, False]:
                  self.check_over_configs(rescale_betas_zero_snr=_a  )




      def 				lowercase__    (			self			: Union[str, Any]  )				->      Tuple:
            self.check_over_configs(thresholding=_a  )
            for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a			,			prediction_type=_a			,			sample_max_value=_a			,			)




      def 				lowercase__    (			self			: Dict  )				->      List[str]:
            for t in [1, 10, 49]:
                  self.check_over_forward(time_step=_a  )




      def 				lowercase__    (			self			: Optional[int]  )				->      Any:
            for t, num_inference_steps in zip([1, 10, 50]			,			[10, 50, 5_00]  ):
                  self.check_over_forward(time_step=_a			,			num_inference_steps=_a  )




      def 				lowercase__    (			self			: List[str]  )				->      List[Any]:
            for t, eta in zip([1, 10, 49]			,			[0.0, 0.5, 1.0]  ):
                  self.check_over_forward(time_step=_a			,			eta=_a  )




      def 				lowercase__    (			self			: Dict  )				->      Optional[Any]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**_a  )

            assert torch.sum(torch.abs(scheduler._get_variance(0			,			0  ) - 0.0  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_20			,			4_00  ) - 0.1_47_71  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_80			,			9_60  ) - 0.3_24_60  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0			,			0  ) - 0.0  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(4_87			,			4_86  ) - 0.0_09_79  )  ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(9_99			,			9_98  ) - 0.02  )  ) < 1E-5




      def 				lowercase__    (			self			: Optional[Any]  )				->      Optional[int]:
            _lowerCAmelCase							     =							self.scheduler_classes[0]
            _lowerCAmelCase							     =							self.get_scheduler_config()
            _lowerCAmelCase							     =							scheduler_class(**_a  )

            _lowerCAmelCase							     =							10, 0.0
            scheduler.set_timesteps(_a  )

            _lowerCAmelCase							     =							self.dummy_model()
            _lowerCAmelCase							     =							self.dummy_sample_deter
            _lowerCAmelCase							     =							self.dummy_sample_deter + 0.1
            _lowerCAmelCase							     =							self.dummy_sample_deter - 0.1

            _lowerCAmelCase							     =							samplea.shape[0]
            _lowerCAmelCase							     =							torch.stack([samplea, samplea, samplea]			,			dim=0  )
            _lowerCAmelCase							     =							torch.arange(_a  )[0:3, None].repeat(1			,			_a  )

            _lowerCAmelCase							     =							model(samples.flatten(0			,			1  )			,			timesteps.flatten(0			,			1  )  )
            _lowerCAmelCase							     =							scheduler.batch_step_no_noise(_a			,			timesteps.flatten(0			,			1  )			,			samples.flatten(0			,			1  )			,			_a  )

            _lowerCAmelCase							     =							torch.sum(torch.abs(_a  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(_a  )  )

            assert abs(result_sum.item() - 11_47.79_04  ) < 1E-2
            assert abs(result_mean.item() - 0.49_82  ) < 1E-3




      def 				lowercase__    (			self			: Optional[Any]  )				->      Union[str, Any]:
            _lowerCAmelCase							     =							self.full_loop()

            _lowerCAmelCase							     =							torch.sum(torch.abs(_a  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(_a  )  )

            assert abs(result_sum.item() - 1_72.00_67  ) < 1E-2
            assert abs(result_mean.item() - 0.22_39_67  ) < 1E-3




      def 				lowercase__    (			self			: List[Any]  )				->      Dict:
            _lowerCAmelCase							     =							self.full_loop(prediction_type="""v_prediction"""  )

            _lowerCAmelCase							     =							torch.sum(torch.abs(_a  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(_a  )  )

            assert abs(result_sum.item() - 52.53_02  ) < 1E-2
            assert abs(result_mean.item() - 0.06_84  ) < 1E-3




      def 				lowercase__    (			self			: Optional[int]  )				->      Optional[Any]:
            # We specify different beta, so that the first alpha is 0.99
            _lowerCAmelCase							     =							self.full_loop(set_alpha_to_one=_a			,			beta_start=0.01  )
            _lowerCAmelCase							     =							torch.sum(torch.abs(_a  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(_a  )  )

            assert abs(result_sum.item() - 1_49.82_95  ) < 1E-2
            assert abs(result_mean.item() - 0.19_51  ) < 1E-3




      def 				lowercase__    (			self			: int  )				->      Dict:
            # We specify different beta, so that the first alpha is 0.99
            _lowerCAmelCase							     =							self.full_loop(set_alpha_to_one=_a			,			beta_start=0.01  )
            _lowerCAmelCase							     =							torch.sum(torch.abs(_a  )  )
            _lowerCAmelCase							     =							torch.mean(torch.abs(_a  )  )

            assert abs(result_sum.item() - 1_49.07_84  ) < 1E-2
            assert abs(result_mean.item() - 0.19_41  ) < 1E-3











'''simple docstring'''





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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_mbart import MBartTokenizer
else:
   _UpperCamelCase											=						None


_UpperCamelCase											=						logging.get_logger(__name__)


_UpperCamelCase											=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=						{
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase											=						{
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
_UpperCamelCase											=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=VOCAB_FILES_NAMES
  a_												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_												=PRETRAINED_VOCAB_FILES_MAP
  a_												=["""input_ids""", """attention_mask"""]
  a_												=MBartTokenizer

  a_												=[]
  a_												=[]




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

    super().__init__(
        vocab_file=_a			,	tokenizer_file=_a			,	bos_token=_a			,	eos_token=_a			,	sep_token=_a			,	cls_token=_a			,	unk_token=_a			,	pad_token=_a			,	mask_token=_a			,	src_lang=_a			,	tgt_lang=_a			,	additional_special_tokens=_a			,	**_a			,	)

    __lowerCamelCase       :       Optional[Any]           = vocab_file
    __lowerCamelCase       :       List[str]           = False if not self.vocab_file else True

    __lowerCamelCase       :       str           = FAIRSEQ_LANGUAGE_CODES.copy()

    if additional_special_tokens is not None:
      # Only add those special tokens if they are not already there.
      _additional_special_tokens.extend(
          [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

    self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}   )
    __lowerCamelCase       :       Optional[Any]           = {
        lang_code: self.convert_tokens_to_ids(_a   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
    }

    __lowerCamelCase       :       Optional[Any]           = src_lang if src_lang is not None else 'en_XX'
    __lowerCamelCase       :       int           = self.convert_tokens_to_ids(self._src_lang   )
    __lowerCamelCase       :       List[str]           = tgt_lang
    self.set_src_lang_special_tokens(self._src_lang   )




  @property
  def 						_lowercase     ( self      :	List[Any]   )					->					str:
    return self._src_lang




  @src_lang.setter
  def 						_lowercase     ( self      :	Union[str, Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = new_src_lang
    self.set_src_lang_special_tokens(self._src_lang   )




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




  def 						_lowercase     ( self      :	int			,	_a      :	List[int]			,	_a      :	Optional[List[int]] = None   )					->					List[int]:
    __lowerCamelCase       :       Optional[int]           = [self.sep_token_id]
    __lowerCamelCase       :       Union[str, 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]




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Optional[Any]			,	_a      :	str			,	_a      :	Optional[str]			,	_a      :	Optional[str]			,	**_a      :	Optional[int]   )					->					Any:
    if src_lang is None or tgt_lang is None:
      raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'   )
    __lowerCamelCase       :       Optional[Any]           = src_lang
    __lowerCamelCase       :       Dict           = self(_a			,	add_special_tokens=_a			,	return_tensors=_a			,	**_a   )
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = tgt_lang_id
    return inputs




  def 						_lowercase     ( self      :	Any			,	_a      :	List[str]			,	_a      :	str = "en_XX"			,	_a      :	Optional[List[str]] = None			,	_a      :	str = "ro_RO"			,	**_a      :	Tuple			,	)					->					BatchEncoding:
    __lowerCamelCase       :       List[Any]           = src_lang
    __lowerCamelCase       :       str           = tgt_lang
    return super().prepare_seqaseq_batch(_a			,	_a			,	**_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    return self.set_src_lang_special_tokens(self.src_lang   )




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    return self.set_tgt_lang_special_tokens(self.tgt_lang   )




  def 						_lowercase     ( self      :	Tuple			,	_a      :	List[str]   )					->					None:
    __lowerCamelCase       :       Tuple           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       Optional[Any]           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       Dict           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Optional[Any]           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Union[str, Any]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	str   )					->					None:
    __lowerCamelCase       :       Union[str, Any]           = self.convert_tokens_to_ids(_a   )
    __lowerCamelCase       :       int           = []
    __lowerCamelCase       :       List[str]           = [self.eos_token_id, self.cur_lang_code]

    __lowerCamelCase       :       int           = self.convert_ids_to_tokens(self.prefix_tokens   )
    __lowerCamelCase       :       Any           = self.convert_ids_to_tokens(self.suffix_tokens   )

    __lowerCamelCase       :       Optional[int]           = processors.TemplateProcessing(
        single=prefix_tokens_str + ['$A'] + suffix_tokens_str			,	pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str			,	special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,	self.prefix_tokens + self.suffix_tokens   )   )			,	)




  def 						_lowercase     ( self      :	Any			,	_a      :	str			,	_a      :	Optional[str] = None   )					->					Tuple[str]:
    if not self.can_save_slow_tokenizer:
      raise ValueError(
          'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
          'tokenizer.'   )

    if not os.path.isdir(_a   ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory.'   )
      return
    __lowerCamelCase       :       List[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   ):
      copyfile(self.vocab_file			,	_a   )

    return (out_vocab_file,)






