






import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Tuple     	=  (DDIMParallelScheduler,)
	snake_case__							:     Dict     	=  (("""eta""", 0.0), ("""num_inference_steps""", 5_0))





	def 						_A (		self				:					Optional[Any] ,  **__lowerCamelCase				:					Any	):
		UpperCamelCase     :Any          =	{
		    """num_train_timesteps""": 1_000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """clip_sample""": True,
		}

		config.update(**__lowerCamelCase	)
		return config





	def 						_A (		self				:					Optional[Any] ,  **__lowerCamelCase				:					List[Any]	):
		UpperCamelCase     :Dict          =	self.scheduler_classes[0]
		UpperCamelCase     :List[str]          =	self.get_scheduler_config(**__lowerCamelCase	)
		UpperCamelCase     :List[str]          =	scheduler_class(**__lowerCamelCase	)

		UpperCamelCase     ,      UpperCamelCase     :int          =	10, 0.0

		UpperCamelCase     :Any          =	self.dummy_model()
		UpperCamelCase     :int          =	self.dummy_sample_deter

		scheduler.set_timesteps(__lowerCamelCase	)

		for t in scheduler.timesteps:
			UpperCamelCase     :Tuple          =	model(__lowerCamelCase ,  __lowerCamelCase	)
			UpperCamelCase     :Optional[int]          =	scheduler.step(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	).prev_sample

		return sample





	def 						_A (		self				:					Optional[int]	):
		for timesteps in [100, 500, 1_000]:
			self.check_over_configs(num_train_timesteps=__lowerCamelCase	)





	def 						_A (		self				:					Any	):
		for steps_offset in [0, 1]:
			self.check_over_configs(steps_offset=__lowerCamelCase	)

		UpperCamelCase     :int          =	self.scheduler_classes[0]
		UpperCamelCase     :List[str]          =	self.get_scheduler_config(steps_offset=1	)
		UpperCamelCase     :List[str]          =	scheduler_class(**__lowerCamelCase	)
		scheduler.set_timesteps(5	)
		assert torch.equal(scheduler.timesteps ,  torch.LongTensor([801, 601, 401, 201, 1]	)	)





	def 						_A (		self				:					List[str]	):
		for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,  [0.002, 0.02, 0.2, 2]	):
			self.check_over_configs(beta_start=__lowerCamelCase ,  beta_end=__lowerCamelCase	)





	def 						_A (		self				:					Tuple	):
		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=__lowerCamelCase	)





	def 						_A (		self				:					Optional[int]	):
		for prediction_type in ["epsilon", "v_prediction"]:
			self.check_over_configs(prediction_type=__lowerCamelCase	)





	def 						_A (		self				:					List[Any]	):
		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=__lowerCamelCase	)





	def 						_A (		self				:					List[str]	):
		for timestep_spacing in ["trailing", "leading"]:
			self.check_over_configs(timestep_spacing=__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		for rescale_betas_zero_snr in [True, False]:
			self.check_over_configs(rescale_betas_zero_snr=__lowerCamelCase	)





	def 						_A (		self				:					str	):
		self.check_over_configs(thresholding=__lowerCamelCase	)
		for threshold in [0.5, 1.0, 2.0]:
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(
				    thresholding=__lowerCamelCase ,  prediction_type=__lowerCamelCase ,  sample_max_value=__lowerCamelCase ,  )





	def 						_A (		self				:					Tuple	):
		for t in [1, 10, 49]:
			self.check_over_forward(time_step=__lowerCamelCase	)





	def 						_A (		self				:					List[str]	):
		for t, num_inference_steps in zip([1, 10, 50] ,  [10, 50, 500]	):
			self.check_over_forward(time_step=__lowerCamelCase ,  num_inference_steps=__lowerCamelCase	)





	def 						_A (		self				:					Dict	):
		for t, eta in zip([1, 10, 49] ,  [0.0, 0.5, 1.0]	):
			self.check_over_forward(time_step=__lowerCamelCase ,  eta=__lowerCamelCase	)





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :str          =	self.scheduler_classes[0]
		UpperCamelCase     :Tuple          =	self.get_scheduler_config()
		UpperCamelCase     :int          =	scheduler_class(**__lowerCamelCase	)

		assert torch.sum(torch.abs(scheduler._get_variance(0 ,  0	) - 0.0	)	) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(420 ,  400	) - 0.14771	)	) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(980 ,  960	) - 0.32460	)	) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(0 ,  0	) - 0.0	)	) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(487 ,  486	) - 0.00979	)	) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(999 ,  998	) - 0.02	)	) < 1E-5





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[Any]          =	self.scheduler_classes[0]
		UpperCamelCase     :Optional[Any]          =	self.get_scheduler_config()
		UpperCamelCase     :List[Any]          =	scheduler_class(**__lowerCamelCase	)

		UpperCamelCase     ,      UpperCamelCase     :Optional[Any]          =	10, 0.0
		scheduler.set_timesteps(__lowerCamelCase	)

		UpperCamelCase     :Any          =	self.dummy_model()
		UpperCamelCase     :Union[str, Any]          =	self.dummy_sample_deter
		UpperCamelCase     :Optional[Any]          =	self.dummy_sample_deter + 0.1
		UpperCamelCase     :List[str]          =	self.dummy_sample_deter - 0.1

		UpperCamelCase     :List[Any]          =	samplea.shape[0]
		UpperCamelCase     :Dict          =	torch.stack([samplea, samplea, samplea] ,  dim=0	)
		UpperCamelCase     :List[Any]          =	torch.arange(__lowerCamelCase	)[0:3, None].repeat(1 ,  __lowerCamelCase	)

		UpperCamelCase     :str          =	model(samples.flatten(0 ,  1	) ,  timesteps.flatten(0 ,  1	)	)
		UpperCamelCase     :str          =	scheduler.batch_step_no_noise(__lowerCamelCase ,  timesteps.flatten(0 ,  1	) ,  samples.flatten(0 ,  1	) ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :Tuple          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		assert abs(result_sum.item() - 1147.7904	) < 1E-2
		assert abs(result_mean.item() - 0.4982	) < 1E-3





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :Tuple          =	self.full_loop()

		UpperCamelCase     :Any          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :Tuple          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		assert abs(result_sum.item() - 172.0067	) < 1E-2
		assert abs(result_mean.item() - 0.223967	) < 1E-3





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :Optional[int]          =	self.full_loop(prediction_type="""v_prediction"""	)

		UpperCamelCase     :List[Any]          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :Optional[int]          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		assert abs(result_sum.item() - 52.5302	) < 1E-2
		assert abs(result_mean.item() - 0.0684	) < 1E-3





	def 						_A (		self				:					List[str]	):
		# We specify different beta, so that the first alpha is 0.99
		UpperCamelCase     :int          =	self.full_loop(set_alpha_to_one=__lowerCamelCase ,  beta_start=0.01	)
		UpperCamelCase     :Optional[Any]          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :List[Any]          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		assert abs(result_sum.item() - 149.8295	) < 1E-2
		assert abs(result_mean.item() - 0.1951	) < 1E-3





	def 						_A (		self				:					List[Any]	):
		# We specify different beta, so that the first alpha is 0.99
		UpperCamelCase     :Tuple          =	self.full_loop(set_alpha_to_one=__lowerCamelCase ,  beta_start=0.01	)
		UpperCamelCase     :Tuple          =	torch.sum(torch.abs(__lowerCamelCase	)	)
		UpperCamelCase     :List[Any]          =	torch.mean(torch.abs(__lowerCamelCase	)	)

		assert abs(result_sum.item() - 149.0784	) < 1E-2
		assert abs(result_mean.item() - 0.1941	) < 1E-3







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:
					A    :					Tuple =      None

A    :					Optional[Any] =      logging.get_logger(__name__)
A    :					Tuple =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A    :					List[str] =      {
    '''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''',
    },
}

A    :					List[str] =      {
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

A    :					Optional[int] =      '''▁'''


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       str							=    VOCAB_FILES_NAMES
			__lowerCamelCase	:       List[Any]							=    PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase	:       Dict							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase	:       List[str]							=    AlbertTokenizer






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Tuple=None							,	__lowerCAmelCase   :    List[str]=None							,	__lowerCAmelCase   :    Optional[Any]=True							,	__lowerCAmelCase   :    List[Any]=True							,	__lowerCAmelCase   :    str=False							,	__lowerCAmelCase   :    Union[str, Any]="[CLS]"							,	__lowerCAmelCase   :    int="[SEP]"							,	__lowerCAmelCase   :    Dict="<unk>"							,	__lowerCAmelCase   :    Dict="[SEP]"							,	__lowerCAmelCase   :    Union[str, Any]="<pad>"							,	__lowerCAmelCase   :    str="[CLS]"							,	__lowerCAmelCase   :    int="[MASK]"							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Optional[Any]:







					"""simple docstring"""






					A__        =   (
					    AddedToken(__lowerCAmelCase							,	lstrip=__lowerCAmelCase							,	rstrip=__lowerCAmelCase							,	normalized=__lowerCAmelCase   )
					    if isinstance(__lowerCAmelCase							,	__lowerCAmelCase   )
					    else mask_token
					)

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

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






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







					"""simple docstring"""






					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    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    List[int]							,	__lowerCAmelCase   :    Optional[List[int]] = None   )	->	List[int]:







					"""simple docstring"""






					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    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    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(__lowerCAmelCase   ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
							return
					A__        =   os.path.join(
					    __lowerCAmelCase							,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

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

					return (out_vocab_file,)



