






"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (LMSDiscreteScheduler,)
    __magic_name__         =     10





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Optional[Any]				=   {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]			):
        self.check_over_configs(beta_start=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




      for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			List[Any]				=   self.dummy_model()
      a     :			str				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			int				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Union[str, Any]				=   output.prev_sample

      a     :			Any				=   torch.sum(torch.abs(A			)			)
      a     :			List[str]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config(prediction_type='v_prediction'			)
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			Tuple				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Any				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Tuple				=   output.prev_sample

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Optional[int]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 0.00_17			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Union[str, Any]				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a     :			Union[str, Any]				=   sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Optional[int]				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			str				=   output.prev_sample

      a     :			Dict				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			Optional[Any]				=   scheduler_class(**A				,		use_karras_sigmas=A			)

      scheduler.set_timesteps(self.num_inference_steps				,		device=A			)

      a     :			Any				=   self.dummy_model()
      a     :			Union[str, Any]				=   self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      a     :			Any				=   sample.to(A			)

      for t in scheduler.timesteps:
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			Tuple				=   model(A				,		A			)

        a     :			List[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			int				=   output.prev_sample

      a     :			Tuple				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 38_12.99_27			) < 2E-2
      assert abs(result_mean.item() - 4.96_48			) < 1E-3








'''simple docstring'''







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

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


if is_sentencepiece_available():
   from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
   A      :     Optional[int]										= None


A      :     Optional[Any]										= logging.get_logger(__name__)

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

A      :     Optional[int]										= {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

A      :     List[str]										= {
    '''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  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    VOCAB_FILES_NAMES
    a													=    PRETRAINED_VOCAB_FILES_MAP
    a													=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=    ["input_ids", "attention_mask"]
    a													=    XLMRobertaTokenizer


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

           super().__init__(
               SCREAMING_SNAKE_CASE      ,							tokenizer_file=SCREAMING_SNAKE_CASE      ,							bos_token=SCREAMING_SNAKE_CASE      ,							eos_token=SCREAMING_SNAKE_CASE      ,							sep_token=SCREAMING_SNAKE_CASE      ,							cls_token=SCREAMING_SNAKE_CASE      ,							unk_token=SCREAMING_SNAKE_CASE      ,							pad_token=SCREAMING_SNAKE_CASE      ,							mask_token=SCREAMING_SNAKE_CASE      ,							**SCREAMING_SNAKE_CASE      ,							)

           _A     :	Dict      =					vocab_file
           _A     :	Optional[Any]      =					False if not self.vocab_file else True


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[int]      ,							SCREAMING_SNAKE_CASE			:							Optional[List[int]] = None):

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _A     :	Optional[Any]      =					[self.cls_token_id]
           _A     :	Optional[int]      =					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


    def    A    (  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							List[int]      ,							SCREAMING_SNAKE_CASE			:							Optional[List[int]] = None):
           _A     :	Any      =					[self.sep_token_id]
           _A     :	Optional[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    A    (  self			:							Dict      ,							SCREAMING_SNAKE_CASE			:							str      ,							SCREAMING_SNAKE_CASE			:							Optional[str] = None):
           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(SCREAMING_SNAKE_CASE):
                  logger.error(F'Vocabulary path ({save_directory}) should be a directory.')
                  return
           _A     :	Any      =					os.path.join(
               SCREAMING_SNAKE_CASE      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

           if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE):
                  copyfile(self.vocab_file      ,							SCREAMING_SNAKE_CASE)

           return (out_vocab_file,)






