



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

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


if is_sentencepiece_available():
    from .tokenization_xglm import XGLMTokenizer
else:
    __lowerCamelCase								=   None


__lowerCamelCase								=   logging.get_logger(__name__)

__lowerCamelCase								=   {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase								=   {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase								=   {
    """facebook/xglm-564M""": 2_0_4_8,
}



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     VOCAB_FILES_NAMES
 lowerCamelCase_       =     PRETRAINED_VOCAB_FILES_MAP
 lowerCamelCase_       =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCamelCase_       =     ['''input_ids''', '''attention_mask''']
 lowerCamelCase_       =     XGLMTokenizer
 def __init__(  self		,      a=None		,      a=None		,      a="<s>"		,      a="</s>"		,      a="</s>"		,      a="<s>"		,      a="<unk>"		,      a="<pad>"		,      **a		,      )		->							List[Any]:




   """simple docstring"""







   _A          =      7
   _A          =      [f'''<madeupword{i}>''' for i in range(self.num_madeup_words   )]

   _A          =      kwargs.get('''additional_special_tokens'''		,      []   )
   kwargs["additional_special_tokens"] += [
       word for word in madeup_words if word not in kwargs["additional_special_tokens"]
   ]

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

   _A          =      vocab_file
   _A          =      False if not self.vocab_file else True
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   if token_ids_a is None:
     return [self.sep_token_id] + token_ids_a
   _A          =      [self.sep_token_id]
   return sep + token_ids_a + sep + sep + token_ids_a
 def lowercase_						(  self		,      a		,      a = None   )		->							List[int]:




   """simple docstring"""







   _A          =      [self.sep_token_id]

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



 def lowercase_						(  self		,      a		,      a = None   )		->							Tuple[str]:




   """simple docstring"""







   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
   _A          =      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,)




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
 lowerCamelCase_       =     1_0
 def lowercase_						(  self		,      **a   )		->							Tuple:




   """simple docstring"""







   _A          =      {
       '''num_train_timesteps''': 1_1_0_0,
       '''beta_start''': 0.0_001,
       '''beta_end''': 0.02,
       '''beta_schedule''': '''linear''',
   }

   config.update(**a   )
   return config
 def lowercase_						(  self   )		->							Dict:




   """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 lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,      [0.0_002, 0.002, 0.02]   ):
     self.check_over_configs(beta_start=a		,      beta_end=a   )
 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   for schedule in ["linear", "scaled_linear"]:
     self.check_over_configs(beta_schedule=a   )
 def lowercase_						(  self   )		->							List[Any]:




   """simple docstring"""







   for prediction_type in ["epsilon", "v_prediction"]:
     self.check_over_configs(prediction_type=a   )
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

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

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config(prediction_type='''v_prediction'''   )
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps   )

   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma
   _A          =      sample.to(a   )

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

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 108.4_439   ) < 1e-2
   assert abs(result_mean.item() - 0.1_412   ) < 1e-3



 def lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   _A          =      self.scheduler_classes[0]
   _A          =      self.get_scheduler_config()
   _A          =      scheduler_class(**a   )

   scheduler.set_timesteps(self.num_inference_steps		,      device=a   )
   _A          =      torch.manual_seed(0   )

   _A          =      self.dummy_model()
   _A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
   _A          =      sample.to(a   )

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

     _A          =      model(a		,      a   )

     _A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
     _A          =      output.prev_sample

   _A          =      torch.sum(torch.abs(a   )   )
   _A          =      torch.mean(torch.abs(a   )   )

   assert abs(result_sum.item() - 152.3_192   ) < 1e-2
   assert abs(result_mean.item() - 0.1_983   ) < 1e-3