





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:
			__UpperCamelCase:	Optional[int] = None

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

__UpperCamelCase:	List[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""",
    },
}

__UpperCamelCase:	Dict = {
    """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,
}

__UpperCamelCase:	Optional[Any] = """▁"""


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				VOCAB_FILES_NAMES
	_A													=				PRETRAINED_VOCAB_FILES_MAP
	_A													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A													=				AlbertTokenizer

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						Optional[int]=None,					lowerCamelCase_:						Optional[Any]=None,					lowerCamelCase_:						int=True,					lowerCamelCase_:						Optional[int]=True,					lowerCamelCase_:						str=False,					lowerCamelCase_:						Any="[CLS]",					lowerCamelCase_:						List[str]="[SEP]",					lowerCamelCase_:						List[str]="<unk>",					lowerCamelCase_:						List[str]="[SEP]",					lowerCamelCase_:						Any="<pad>",					lowerCamelCase_:						List[Any]="[CLS]",					lowerCamelCase_:						Any="[MASK]",					**lowerCamelCase_:						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.
			lowercase__					:      Any      =		(
			    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_,					)

			lowercase__					:      int      =		do_lower_case
			lowercase__					:      Any      =		remove_space
			lowercase__					:      Tuple      =		keep_accents
			lowercase__					:      Any      =		vocab_file
			lowercase__					:      Union[str, Any]      =		False if not self.vocab_file else True

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None			):
			lowercase__					:      int      =		[self.sep_token_id]
			lowercase__					:      List[str]      =		[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    snake_case__(						self:						Optional[int],					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None			):
			lowercase__					:      List[Any]      =		[self.sep_token_id]
			lowercase__					:      Tuple      =		[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    snake_case__(						self:						List[Any],					lowerCamelCase_:						str,					lowerCamelCase_:						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(lowerCamelCase_			):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
					return
			lowercase__					:      Dict      =		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,)









import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				(EulerDiscreteScheduler,)
	_A													=				10

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						Optional[Any]			):
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],					[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
					self.check_over_configs(beta_start=lowerCamelCase_,					beta_end=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Any      =		torch.manual_seed(0			)

			lowercase__					:      int      =		self.dummy_model()
			lowercase__					:      Tuple      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Union[str, Any]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      Dict      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Any      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      str      =		self.get_scheduler_config(prediction_type='v_prediction'			)
			lowercase__					:      Optional[int]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Optional[int]      =		torch.manual_seed(0			)

			lowercase__					:      str      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Optional[int]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      int      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Dict      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
			assert abs(result_mean.item() - 2.2676E-06			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Dict      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      Dict      =		torch.manual_seed(0			)

			lowercase__					:      Union[str, Any]      =		self.dummy_model()
			lowercase__					:      Any      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[Any]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Optional[int]      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      List[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		output.prev_sample

			lowercase__					:      Union[str, Any]      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Union[str, Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_,					use_karras_sigmas=lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      int      =		torch.manual_seed(0			)

			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      str      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[str]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Any      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		output.prev_sample

			lowercase__					:      int      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1E-2
			assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 1E-3


