





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


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




			__lowerCamelCase	:       Union[str, Any]							=    (DEISMultistepScheduler,)
			__lowerCamelCase	:       Union[str, Any]							=    (('''num_inference_steps''', 25),)






			def    a_       (			self   :    int							,	**__lowerCAmelCase   :    List[str]   )	->	int:







					"""simple docstring"""






					A__        =   {
					    """num_train_timesteps""": 10_00,
					    """beta_start""": 0.0_0_0_1,
					    """beta_end""": 0.0_2,
					    """beta_schedule""": """linear""",
					    """solver_order""": 2,
					}

					config.update(**__lowerCAmelCase   )
					return config






			def    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Any=0							,	**__lowerCAmelCase   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					A__        =   dict(self.forward_default_kwargs   )
					A__        =   kwargs.pop("""num_inference_steps"""							,	__lowerCAmelCase   )
					A__        =   self.dummy_sample
					A__        =   0.1 * sample
					A__        =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
							A__        =   self.get_scheduler_config(**__lowerCAmelCase   )
							A__        =   scheduler_class(**__lowerCAmelCase   )
							scheduler.set_timesteps(__lowerCAmelCase   )
							# copy over dummy past residuals
							A__        =   dummy_past_residuals[: scheduler.config.solver_order]

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

							A__      ,			A__        =   sample, sample
							for t in range(__lowerCAmelCase							,	time_step + scheduler.config.solver_order + 1   ):
									A__        =   scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   ).prev_sample
									A__        =   new_scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   ).prev_sample

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






			def    a_       (			self   :    str   )	->	List[Any]:







					"""simple docstring"""






					pass






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    Any=0							,	**__lowerCAmelCase   :    Any   )	->	Dict:







					"""simple docstring"""






					A__        =   dict(self.forward_default_kwargs   )
					A__        =   kwargs.pop("""num_inference_steps"""							,	__lowerCAmelCase   )
					A__        =   self.dummy_sample
					A__        =   0.1 * sample
					A__        =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
							A__        =   self.get_scheduler_config()
							A__        =   scheduler_class(**__lowerCAmelCase   )
							scheduler.set_timesteps(__lowerCAmelCase   )

							# copy over dummy past residuals (must be after setting timesteps)
							A__        =   dummy_past_residuals[: scheduler.config.solver_order]

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

									# copy over dummy past residual (must be after setting timesteps)
									A__        =   dummy_past_residuals[: new_scheduler.config.solver_order]

							A__        =   scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   ).prev_sample
							A__        =   new_scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   ).prev_sample

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






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







					"""simple docstring"""






					if scheduler is None:
							A__        =   self.scheduler_classes[0]
							A__        =   self.get_scheduler_config(**__lowerCAmelCase   )
							A__        =   scheduler_class(**__lowerCAmelCase   )

					A__        =   self.scheduler_classes[0]
					A__        =   self.get_scheduler_config(**__lowerCAmelCase   )
					A__        =   scheduler_class(**__lowerCAmelCase   )

					A__        =   10
					A__        =   self.dummy_model()
					A__        =   self.dummy_sample_deter
					scheduler.set_timesteps(__lowerCAmelCase   )

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

					return sample






			def    a_       (			self   :    List[str]   )	->	Any:







					"""simple docstring"""






					A__        =   dict(self.forward_default_kwargs   )

					A__        =   kwargs.pop("""num_inference_steps"""							,	__lowerCAmelCase   )

					for scheduler_class in self.scheduler_classes:
							A__        =   self.get_scheduler_config()
							A__        =   scheduler_class(**__lowerCAmelCase   )

							A__        =   self.dummy_sample
							A__        =   0.1 * sample

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

							# copy over dummy past residuals (must be done after set_timesteps)
							A__        =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
							A__        =   dummy_past_residuals[: scheduler.config.solver_order]

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

							A__        =   scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   ).prev_sample
							A__        =   scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	**__lowerCAmelCase   ).prev_sample

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






			def    a_       (			self   :    Optional[int]   )	->	Tuple:







					"""simple docstring"""






					A__        =   DEISMultistepScheduler(**self.get_scheduler_config()   )
					A__        =   self.full_loop(scheduler=__lowerCAmelCase   )
					A__        =   torch.mean(torch.abs(__lowerCAmelCase   )   )

					assert abs(result_mean.item() - 0.2_3_9_1_6   ) < 1e-3

					A__        =   DPMSolverSinglestepScheduler.from_config(scheduler.config   )
					A__        =   DPMSolverMultistepScheduler.from_config(scheduler.config   )
					A__        =   UniPCMultistepScheduler.from_config(scheduler.config   )
					A__        =   DEISMultistepScheduler.from_config(scheduler.config   )

					A__        =   self.full_loop(scheduler=__lowerCAmelCase   )
					A__        =   torch.mean(torch.abs(__lowerCAmelCase   )   )

					assert abs(result_mean.item() - 0.2_3_9_1_6   ) < 1e-3






			def    a_       (			self   :    int   )	->	List[Any]:







					"""simple docstring"""






					for timesteps in [25, 50, 1_00, 9_99, 10_00]:
							self.check_over_configs(num_train_timesteps=__lowerCAmelCase   )






			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					self.check_over_configs(thresholding=__lowerCAmelCase   )
					for order in [1, 2, 3]:
							for solver_type in ["logrho"]:
									for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    thresholding=__lowerCAmelCase							,	prediction_type=__lowerCAmelCase							,	sample_max_value=__lowerCAmelCase							,	algorithm_type="""deis"""							,	solver_order=__lowerCAmelCase							,	solver_type=__lowerCAmelCase							,	)






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







					"""simple docstring"""






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






			def    a_       (			self   :    List[str]   )	->	Any:







					"""simple docstring"""






					for algorithm_type in ["deis"]:
							for solver_type in ["logrho"]:
									for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(
													    solver_order=__lowerCAmelCase							,	solver_type=__lowerCAmelCase							,	prediction_type=__lowerCAmelCase							,	algorithm_type=__lowerCAmelCase							,	)
													A__        =   self.full_loop(
													    solver_order=__lowerCAmelCase							,	solver_type=__lowerCAmelCase							,	prediction_type=__lowerCAmelCase							,	algorithm_type=__lowerCAmelCase							,	)
													assert not torch.isnan(__lowerCAmelCase   ).any(), "Samples have nan numbers"






			def    a_       (			self   :    Union[str, Any]   )	->	Any:







					"""simple docstring"""






					self.check_over_configs(lower_order_final=__lowerCAmelCase   )
					self.check_over_configs(lower_order_final=__lowerCAmelCase   )






			def    a_       (			self   :    str   )	->	Union[str, Any]:







					"""simple docstring"""






					for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
							self.check_over_forward(num_inference_steps=__lowerCAmelCase							,	time_step=0   )






			def    a_       (			self   :    Dict   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   self.full_loop()
					A__        =   torch.mean(torch.abs(__lowerCAmelCase   )   )

					assert abs(result_mean.item() - 0.2_3_9_1_6   ) < 1e-3






			def    a_       (			self   :    List[Any]   )	->	Dict:







					"""simple docstring"""






					A__        =   self.full_loop(prediction_type="""v_prediction"""   )
					A__        =   torch.mean(torch.abs(__lowerCAmelCase   )   )

					assert abs(result_mean.item() - 0.0_9_1   ) < 1e-3







			def    a_       (			self   :    Optional[Any]   )	->	int:







					"""simple docstring"""






					A__        =   self.scheduler_classes[0]
					A__        =   self.get_scheduler_config(thresholding=__lowerCAmelCase							,	dynamic_thresholding_ratio=0   )
					A__        =   scheduler_class(**__lowerCAmelCase   )

					A__        =   10
					A__        =   self.dummy_model()
					A__        =   self.dummy_sample_deter.half()
					scheduler.set_timesteps(__lowerCAmelCase   )

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

					assert sample.dtype == torch.floataa










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,)



