import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      (IPNDMScheduler,)
  _SCREAMING_SNAKE_CASE     					=      (("""num_inference_steps""", 50),)

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	{"num_train_timesteps": 10_00}
   config.update(**__SCREAMING_SNAKE_CASE							)
   return config

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   Any=0  ,				**__SCREAMING_SNAKE_CASE			:   List[str]							):
   __a							=	dict(self.forward_default_kwargs							)
   __a							=	kwargs.pop("num_inference_steps"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.dummy_sample
   __a							=	0.1 * sample
   __a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample
    __a							=	new_scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample

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

    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample
    __a							=	new_scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample

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

  def 		_UpperCAmelCase (		self			:   Any							):
   pass

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   Tuple=0  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   __a							=	dict(self.forward_default_kwargs							)
   __a							=	kwargs.pop("num_inference_steps"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.dummy_sample
   __a							=	0.1 * sample
   __a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config()
    __a							=	scheduler_class(**__SCREAMING_SNAKE_CASE							)
    scheduler.set_timesteps(__SCREAMING_SNAKE_CASE							)

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

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

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

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

    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample
    __a							=	new_scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample

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

    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample
    __a							=	new_scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample

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

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				**__SCREAMING_SNAKE_CASE			:   Optional[Any]							):
   __a							=	self.scheduler_classes[0]
   __a							=	self.get_scheduler_config(**__SCREAMING_SNAKE_CASE							)
   __a							=	scheduler_class(**__SCREAMING_SNAKE_CASE							)

   __a							=	10
   __a							=	self.dummy_model()
   __a							=	self.dummy_sample_deter
   scheduler.set_timesteps(__SCREAMING_SNAKE_CASE							)

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

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

   return sample

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	dict(self.forward_default_kwargs							)

   __a							=	kwargs.pop("num_inference_steps"  ,				__SCREAMING_SNAKE_CASE							)

   for scheduler_class in self.scheduler_classes:
    __a							=	self.get_scheduler_config()
    __a							=	scheduler_class(**__SCREAMING_SNAKE_CASE							)

    __a							=	self.dummy_sample
    __a							=	0.1 * sample

    if num_inference_steps is not None and hasattr(__SCREAMING_SNAKE_CASE  ,				"set_timesteps"							):
     scheduler.set_timesteps(__SCREAMING_SNAKE_CASE							)
    elif num_inference_steps is not None and not hasattr(__SCREAMING_SNAKE_CASE  ,				"set_timesteps"							):
     __a							=	num_inference_steps

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

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

    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample
    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample

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

    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample
    __a							=	scheduler.step(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							).prev_sample

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

  def 		_UpperCAmelCase (		self			:   str							):
   for timesteps in [1_00, 10_00]:
    self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE  ,				time_step=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Dict							):
   for t, num_inference_steps in zip([1, 5, 10]  ,				[10, 50, 1_00]							):
    self.check_over_forward(num_inference_steps=__SCREAMING_SNAKE_CASE  ,				time_step=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self.full_loop()
   __a							=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE							)							)

   assert abs(result_mean.item() - 2_54_05_29							) < 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_albert import AlbertTokenizer
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		None

SCREAMING_SNAKE_CASE			:						str				  =		logging.get_logger(__name__)
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		"""▁"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      AlbertTokenizer

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=False  ,				__SCREAMING_SNAKE_CASE			:   Dict="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Dict="<unk>"  ,				__SCREAMING_SNAKE_CASE			:   str="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Any="<pad>"  ,				__SCREAMING_SNAKE_CASE			:   List[str]="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="[MASK]"  ,				**__SCREAMING_SNAKE_CASE			:   Tuple  ,				):
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   __a							=	(
       AddedToken(__SCREAMING_SNAKE_CASE  ,				lstrip=__SCREAMING_SNAKE_CASE  ,				rstrip=__SCREAMING_SNAKE_CASE  ,				normalized=__SCREAMING_SNAKE_CASE							)
       if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
       else mask_token
   )

   super().__init__(
       __SCREAMING_SNAKE_CASE  ,				tokenizer_file=__SCREAMING_SNAKE_CASE  ,				do_lower_case=__SCREAMING_SNAKE_CASE  ,				remove_space=__SCREAMING_SNAKE_CASE  ,				keep_accents=__SCREAMING_SNAKE_CASE  ,				bos_token=__SCREAMING_SNAKE_CASE  ,				eos_token=__SCREAMING_SNAKE_CASE  ,				unk_token=__SCREAMING_SNAKE_CASE  ,				sep_token=__SCREAMING_SNAKE_CASE  ,				pad_token=__SCREAMING_SNAKE_CASE  ,				cls_token=__SCREAMING_SNAKE_CASE  ,				mask_token=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	do_lower_case
   __a							=	remove_space
   __a							=	keep_accents
   __a							=	vocab_file
   __a							=	False if not self.vocab_file else True

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[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							) * [0] + len(token_ids_a + sep							) * [1]

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__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							=	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,)


