
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput




@dataclass
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     4_2
	_A								=     4_2




class  lowerCamelCase__							(				__lowercase     ,						__lowercase):




	'''simple docstring'''




	_A								=     1







	@register_to_config
	def __init__(					self		:Any     ,   a		:Optional[int] = 2_0_0_0     ,   a		:Union[str, Any] = 0.15     ,   a		:int = 0.01     ,   a		:Any = 1_3_4_8.0     ,   a		:Optional[int] = 1E-5     ,   a		:Optional[Any] = 1     ,   )		-> Optional[Any]:
								# standard deviation of the initial noise distribution
								__UpperCamelCase	:   Any			   =					sigma_max

								# setable values
								__UpperCamelCase	:   Tuple			   =					None

								self.set_sigmas(a     ,   a     ,   a     ,   a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:int     ,   a		:Tuple = None						)		-> torch.FloatTensor:
								return sample







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Any     ,   a		:Dict = None     ,   a		:List[Any] = None						)		-> Optional[Any]:
								__UpperCamelCase	:   int			   =					sampling_eps if sampling_eps is not None else self.config.sampling_eps

								__UpperCamelCase	:   List[Any]			   =					torch.linspace(1     ,   a     ,   a     ,   device=a						)







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:int     ,   a		:Union[str, Any] = None     ,   a		:str = None     ,   a		:Union[str, Any] = None						)		-> Dict:
								__UpperCamelCase	:   Union[str, Any]			   =					sigma_min if sigma_min is not None else self.config.sigma_min
								__UpperCamelCase	:   Tuple			   =					sigma_max if sigma_max is not None else self.config.sigma_max
								__UpperCamelCase	:   Optional[int]			   =					sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
															self.set_timesteps(a     ,   a						)

								__UpperCamelCase	:   Dict			   =					sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								__UpperCamelCase	:   Any			   =					torch.exp(torch.linspace(math.log(a						)     ,   math.log(a						)     ,   a						)						)
								__UpperCamelCase	:   Any			   =					torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Any     ,   a		:Any						)		-> List[str]:
								return torch.where(
								    timesteps == 0     ,   torch.zeros_like(t.to(timesteps.device						)						)     ,   self.discrete_sigmas[timesteps - 1].to(timesteps.device						)     ,   )







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[str]     ,   a		:int     ,   a		:Optional[int]     ,   a		:List[Any] = None     ,   a		:Any = True     ,   )		-> Union[SdeVeOutput, Tuple]:
								if self.timesteps is None:
															raise ValueError(
															    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)

								__UpperCamelCase	:   str			   =					timestep * torch.ones(
								    sample.shape[0]     ,   device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								__UpperCamelCase	:   Optional[int]			   =					(timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								__UpperCamelCase	:   List[str]			   =					timesteps.to(self.discrete_sigmas.device						)

								__UpperCamelCase	:   Union[str, Any]			   =					self.discrete_sigmas[timesteps].to(sample.device						)
								__UpperCamelCase	:   Optional[int]			   =					self.get_adjacent_sigma(a     ,   a						).to(sample.device						)
								__UpperCamelCase	:   Tuple			   =					torch.zeros_like(a						)
								__UpperCamelCase	:   Dict			   =					(sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								__UpperCamelCase	:   Optional[Any]			   =					diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
															__UpperCamelCase	:   Optional[int]			   =					diffusion.unsqueeze(-1						)
								__UpperCamelCase	:   Union[str, Any]			   =					drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								__UpperCamelCase	:   Optional[Any]			   =					randn_tensor(
								    sample.shape     ,   layout=sample.layout     ,   generator=a     ,   device=sample.device     ,   dtype=sample.dtype						)
								__UpperCamelCase	:   Any			   =					sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								__UpperCamelCase	:   Tuple			   =					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
															return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=a     ,   prev_sample_mean=a						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:List[str]     ,   a		:Any     ,   a		:Union[str, Any] = None     ,   a		:List[str] = True     ,   )		-> Union[SchedulerOutput, Tuple]:
								if self.timesteps is None:
															raise ValueError(
															    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								__UpperCamelCase	:   Any			   =					randn_tensor(sample.shape     ,   layout=sample.layout     ,   generator=a						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								__UpperCamelCase	:   str			   =					torch.norm(model_output.reshape(model_output.shape[0]     ,   -1						)     ,   dim=-1						).mean()
								__UpperCamelCase	:   str			   =					torch.norm(noise.reshape(noise.shape[0]     ,   -1						)     ,   dim=-1						).mean()
								__UpperCamelCase	:   Dict			   =					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
								__UpperCamelCase	:   int			   =					step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								__UpperCamelCase	:   Union[str, Any]			   =					step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
															__UpperCamelCase	:   List[str]			   =					step_size.unsqueeze(-1						)
								__UpperCamelCase	:   Optional[Any]			   =					sample + step_size * model_output
								__UpperCamelCase	:   str			   =					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
															return (prev_sample,)

								return SchedulerOutput(prev_sample=a						)







	def    _lowerCamelCase				(					self		:Union[str, Any]     ,   a		:Optional[int]     ,   a		:Any     ,   a		:Tuple     ,   )		-> torch.FloatTensor:
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								__UpperCamelCase	:   int			   =					timesteps.to(original_samples.device						)
								__UpperCamelCase	:   Any			   =					self.discrete_sigmas.to(original_samples.device						)[timesteps]
								__UpperCamelCase	:   Union[str, Any]			   =					(
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(a						) * sigmas[:, None, None, None]
								)
								__UpperCamelCase	:   Dict			   =					noise + original_samples
								return noisy_samples







	def __len__(					self		:Dict						)		-> Optional[int]:
								return self.config.num_train_timesteps




'''simple docstring'''




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a   :			Any					  =    get_tests_dir("""fixtures/spiece.model""")

@require_sentencepiece
@require_tokenizers
class 			UpperCamelCase_ (							__magic_name__      ,     unittest.TestCase			):
	lowercase          = AlbertTokenizer
	lowercase          = AlbertTokenizerFast
	lowercase          = True
	lowercase          = True
	lowercase          = True





	def  _lowercase(  self    )			->			str:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase     :		Optional[int]							     =		AlbertTokenizer(A    )
								tokenizer.save_pretrained(self.tmpdirname    )





	def  _lowercase(  self		,		A    )			->			int:
								UpperCAmelCase     :		Optional[int]							     =		"""this is a test"""
								UpperCAmelCase     :		Dict							     =		"""this is a test"""
								return input_text, output_text





	def  _lowercase(  self    )			->			int:
								UpperCAmelCase     :		Tuple							     =		"""<pad>"""
								UpperCAmelCase     :		Optional[int]							     =		0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(A    )		,		A    )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(A    )		,		A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		str							     =		list(self.get_tokenizer().get_vocab().keys()    )

								self.assertEqual(vocab_keys[0]		,		"""<pad>"""    )
								self.assertEqual(vocab_keys[1]		,		"""<unk>"""    )
								self.assertEqual(vocab_keys[-1]		,		"""▁eloquent"""    )
								self.assertEqual(len(A    )		,		30000    )





	def  _lowercase(  self    )			->			Tuple:
								self.assertEqual(self.get_tokenizer().vocab_size		,		30000    )





	def  _lowercase(  self    )			->			Union[str, Any]:
								if not self.test_rust_tokenizer:
															return

								UpperCAmelCase     :		int							     =		self.get_tokenizer()
								UpperCAmelCase     :		List[str]							     =		self.get_rust_tokenizer()

								UpperCAmelCase     :		Optional[Any]							     =		"""I was born in 92000, and this is falsé."""

								UpperCAmelCase     :		str							     =		tokenizer.tokenize(A    )
								UpperCAmelCase     :		Optional[int]							     =		rust_tokenizer.tokenize(A    )
								self.assertListEqual(A		,		A    )

								UpperCAmelCase     :		Any							     =		tokenizer.encode(A		,		add_special_tokens=A    )
								UpperCAmelCase     :		Optional[int]							     =		rust_tokenizer.encode(A		,		add_special_tokens=A    )
								self.assertListEqual(A		,		A    )

								UpperCAmelCase     :		Optional[Any]							     =		self.get_rust_tokenizer()
								UpperCAmelCase     :		Union[str, Any]							     =		tokenizer.encode(A    )
								UpperCAmelCase     :		Optional[int]							     =		rust_tokenizer.encode(A    )
								self.assertListEqual(A		,		A    )





	def  _lowercase(  self    )			->			Any:
								UpperCAmelCase     :		List[Any]							     =		AlbertTokenizer(A		,		keep_accents=A    )

								UpperCAmelCase     :		Optional[int]							     =		tokenizer.tokenize("""This is a test"""    )
								self.assertListEqual(A		,		["""▁this""", """▁is""", """▁a""", """▁test"""]    )

								self.assertListEqual(tokenizer.convert_tokens_to_ids(A    )		,		[48, 25, 21, 1289]    )

								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""    )
								self.assertListEqual(
								    A		,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]    )
								UpperCAmelCase     :		str							     =		tokenizer.convert_tokens_to_ids(A    )
								self.assertListEqual(A		,		[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

								UpperCAmelCase     :		List[str]							     =		tokenizer.convert_ids_to_tokens(A    )
								self.assertListEqual(
								    A		,		["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]		,		)





	def  _lowercase(  self    )			->			Optional[Any]:
								UpperCAmelCase     :		str							     =		AlbertTokenizer(A    )

								UpperCAmelCase     :		Optional[int]							     =		tokenizer.encode("""sequence builders"""    )
								UpperCAmelCase     :		Any							     =		tokenizer.encode("""multi-sequence build"""    )

								UpperCAmelCase     :		Optional[Any]							     =		tokenizer.build_inputs_with_special_tokens(A    )
								UpperCAmelCase     :		List[str]							     =		tokenizer.build_inputs_with_special_tokens(A		,		A    )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]






	@slow
	def  _lowercase(  self    )			->			Dict:
								# fmt: off
								UpperCAmelCase     :		Tuple							     =		{"""attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=A		,		model_name="""albert-base-v2"""		,		revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""		,		)

