





import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Optional[Any]			 =	(IPNDMScheduler,)
   _UpperCamelCase   :		str			 =	(('num_inference_steps', 50),)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str      ,					**a      :    List[Any]	)->       List[Any]:





       """simple docstring"""







       lowercase__           =  {'num_train_timesteps': 1_000}
       config.update(**__A	)
       return config


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]      ,					a      :    int=0      ,					**a      :    Any	)->       List[str]:





       """simple docstring"""







       lowercase__           =  dict(self.forward_default_kwargs	)
       lowercase__           =  kwargs.pop('num_inference_steps'      ,					__A	)
       lowercase__           =  self.dummy_sample
       lowercase__           =  0.1 * sample
       lowercase__           =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
           lowercase__           =  self.get_scheduler_config(**__A	)
           lowercase__           =  scheduler_class(**__A	)
           scheduler.set_timesteps(__A	)
           # copy over dummy past residuals
           lowercase__           =  dummy_past_residuals[:]

           if time_step is None:
               lowercase__           =  scheduler.timesteps[len(scheduler.timesteps	) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__A	)
               lowercase__           =  scheduler_class.from_pretrained(__A	)
               new_scheduler.set_timesteps(__A	)
               # copy over dummy past residuals
               lowercase__           =  dummy_past_residuals[:]

           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample
           lowercase__           =  new_scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample

           assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample
           lowercase__           =  new_scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample

           assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


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





       """simple docstring"""







       pass


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict      ,					a      :    str=0      ,					**a      :    Any	)->       List[str]:





       """simple docstring"""







       lowercase__           =  dict(self.forward_default_kwargs	)
       lowercase__           =  kwargs.pop('num_inference_steps'      ,					__A	)
       lowercase__           =  self.dummy_sample
       lowercase__           =  0.1 * sample
       lowercase__           =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
           lowercase__           =  self.get_scheduler_config()
           lowercase__           =  scheduler_class(**__A	)
           scheduler.set_timesteps(__A	)

           # copy over dummy past residuals (must be after setting timesteps)
           lowercase__           =  dummy_past_residuals[:]

           if time_step is None:
               lowercase__           =  scheduler.timesteps[len(scheduler.timesteps	) // 2]

           with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(__A	)
               lowercase__           =  scheduler_class.from_pretrained(__A	)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(__A	)

               # copy over dummy past residual (must be after setting timesteps)
               lowercase__           =  dummy_past_residuals[:]

           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample
           lowercase__           =  new_scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample

           assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample
           lowercase__           =  new_scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample

           assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					**a      :    Dict	)->       Optional[int]:





       """simple docstring"""







       lowercase__           =  self.scheduler_classes[0]
       lowercase__           =  self.get_scheduler_config(**__A	)
       lowercase__           =  scheduler_class(**__A	)

       lowercase__           =  10
       lowercase__           =  self.dummy_model()
       lowercase__           =  self.dummy_sample_deter
       scheduler.set_timesteps(__A	)

       for i, t in enumerate(scheduler.timesteps	):
           lowercase__           =  model(__A      ,					__A	)
           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A	).prev_sample

       for i, t in enumerate(scheduler.timesteps	):
           lowercase__           =  model(__A      ,					__A	)
           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A	).prev_sample

       return sample


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Union[str, Any]:





       """simple docstring"""







       lowercase__           =  dict(self.forward_default_kwargs	)

       lowercase__           =  kwargs.pop('num_inference_steps'      ,					__A	)

       for scheduler_class in self.scheduler_classes:
           lowercase__           =  self.get_scheduler_config()
           lowercase__           =  scheduler_class(**__A	)

           lowercase__           =  self.dummy_sample
           lowercase__           =  0.1 * sample

           if num_inference_steps is not None and hasattr(__A      ,					'set_timesteps'	):
               scheduler.set_timesteps(__A	)
           elif num_inference_steps is not None and not hasattr(__A      ,					'set_timesteps'	):
               lowercase__           =  num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowercase__           =  [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
           lowercase__           =  dummy_past_residuals[:]

           lowercase__           =  scheduler.timesteps[5]
           lowercase__           =  scheduler.timesteps[6]

           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample
           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample

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

           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample
           lowercase__           =  scheduler.step(__A      ,					__A      ,					__A      ,					**__A	).prev_sample

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       Union[str, Any]:





       """simple docstring"""







       for timesteps in [100, 1_000]:
           self.check_over_configs(num_train_timesteps=__A      ,					time_step=__A	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int	)->       List[str]:





       """simple docstring"""







       for t, num_inference_steps in zip([1, 5, 10]      ,					[10, 50, 100]	):
           self.check_over_forward(num_inference_steps=__A      ,					time_step=__A	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Tuple:





       """simple docstring"""







       lowercase__           =  self.full_loop()
       lowercase__           =  torch.mean(torch.abs(__A	)	)

       assert abs(result_mean.item() - 2_540_529	) < 10









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_camembert import CamembertTokenizer
else:
      lowercase_          =		None


lowercase_          =		logging.get_logger(__name__)

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

lowercase_          =		{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

lowercase_          =		{
    """camembert-base""": 512,
}

lowercase_          =		"""▁"""
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		int			 =	VOCAB_FILES_NAMES
   _UpperCamelCase   :		Dict			 =	PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase   :		Dict			 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase   :		Dict			 =	['input_ids', 'attention_mask']
   _UpperCamelCase   :		Optional[int]			 =	CamembertTokenizer


   def __init__(   self      :    List[Any]      ,					a      :    Union[str, Any]=None      ,					a      :    str=None      ,					a      :    Tuple="<s>"      ,					a      :    Tuple="</s>"      ,					a      :    Optional[int]="</s>"      ,					a      :    int="<s>"      ,					a      :    Dict="<unk>"      ,					a      :    Optional[Any]="<pad>"      ,					a      :    Union[str, Any]="<mask>"      ,					a      :    Any=["<s>NOTUSED", "</s>NOTUSED"]      ,					**a      :    int      ,					)->       str:





     """simple docstring"""







     lowercase__           =  AddedToken(a      ,					lstrip=a      ,					rstrip=a	) if isinstance(a      ,					a	) else mask_token

     super().__init__(
         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      ,					additional_special_tokens=a      ,					**a      ,					)

     lowercase__           =  vocab_file
     lowercase__           =  False if not self.vocab_file else True


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    List[int]      ,					a      :    Optional[List[int]] = None	)->       List[int]:





     """simple docstring"""







     lowercase__           =  [self.sep_token_id]
     lowercase__           =  [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 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]      ,					a      :    str      ,					a      :    Optional[str] = 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
     lowercase__           =  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,)


