"""simple docstring"""




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	torch.FloatTensor
					UpperCamelCase_ :	torch.FloatTensor

class       a_      (     __UpperCamelCase ,		__UpperCamelCase			):
					UpperCamelCase_ :	Dict			=    1




					@register_to_config
					def __init__(	self						:     Optional[int]  ,						snake_case__						:     int = 2000  ,						snake_case__						:     float = 0.15  ,						snake_case__						:     float = 0.01  ,						snake_case__						:     float = 1348.0  ,						snake_case__						:     float = 1E-5  ,						snake_case__						:     int = 1  ,						):
							# standard deviation of the initial noise distribution
							lowerCAmelCase__        =   sigma_max

							# setable values
							lowerCAmelCase__        =   None

							self.set_sigmas(snake_case__  ,						snake_case__  ,						snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[int] = None     ):
							return sample




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     int  ,						snake_case__						:     float = None  ,						snake_case__						:     Union[str, torch.device] = None     ):
							lowerCAmelCase__        =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

							lowerCAmelCase__        =   torch.linspace(1  ,						snake_case__  ,						snake_case__  ,						device=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     int  ,						snake_case__						:     float = None  ,						snake_case__						:     float = None  ,						snake_case__						:     float = None     ):
							lowerCAmelCase__        =   sigma_min if sigma_min is not None else self.config.sigma_min
							lowerCAmelCase__        =   sigma_max if sigma_max is not None else self.config.sigma_max
							lowerCAmelCase__        =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
									self.set_timesteps(snake_case__  ,						snake_case__     )

							lowerCAmelCase__        =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							lowerCAmelCase__        =   torch.exp(torch.linspace(math.log(snake_case__     )  ,						math.log(snake_case__     )  ,						snake_case__     )     )
							lowerCAmelCase__        =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							return torch.where(
							    timesteps == 0  ,						torch.zeros_like(t.to(timesteps.device     )     )  ,						self.discrete_sigmas[timesteps - 1].to(timesteps.device     )  ,						)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     int  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[torch.Generator] = None  ,						snake_case__						:     bool = True  ,						):
							if self.timesteps is None:
									raise ValueError(
									    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""     )

							lowerCAmelCase__        =   timestep * torch.ones(
							    sample.shape[0]  ,						device=sample.device     )  # torch.repeat_interleave(timestep, sample.shape[0])
							lowerCAmelCase__        =   (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
							lowerCAmelCase__        =   timesteps.to(self.discrete_sigmas.device     )

							lowerCAmelCase__        =   self.discrete_sigmas[timesteps].to(sample.device     )
							lowerCAmelCase__        =   self.get_adjacent_sigma(snake_case__  ,						snake_case__     ).to(sample.device     )
							lowerCAmelCase__        =   torch.zeros_like(snake_case__     )
							lowerCAmelCase__        =   (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
							lowerCAmelCase__        =   diffusion.flatten()
							while len(diffusion.shape     ) < len(sample.shape     ):
									lowerCAmelCase__        =   diffusion.unsqueeze(-1     )
							lowerCAmelCase__        =   drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							lowerCAmelCase__        =   randn_tensor(
							    sample.shape  ,						layout=sample.layout  ,						generator=snake_case__  ,						device=sample.device  ,						dtype=sample.dtype     )
							lowerCAmelCase__        =   sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							lowerCAmelCase__        =   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=snake_case__  ,						prev_sample_mean=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     Optional[torch.Generator] = None  ,						snake_case__						:     bool = True  ,						):
							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
							lowerCAmelCase__        =   randn_tensor(sample.shape  ,						layout=sample.layout  ,						generator=snake_case__     ).to(sample.device     )

							# compute step size from the model_output, the noise, and the snr
							lowerCAmelCase__        =   torch.norm(model_output.reshape(model_output.shape[0]  ,						-1     )  ,						dim=-1     ).mean()
							lowerCAmelCase__        =   torch.norm(noise.reshape(noise.shape[0]  ,						-1     )  ,						dim=-1     ).mean()
							lowerCAmelCase__        =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							lowerCAmelCase__        =   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
							lowerCAmelCase__        =   step_size.flatten()
							while len(step_size.shape     ) < len(sample.shape     ):
									lowerCAmelCase__        =   step_size.unsqueeze(-1     )
							lowerCAmelCase__        =   sample + step_size * model_output
							lowerCAmelCase__        =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
									return (prev_sample,)

							return SchedulerOutput(prev_sample=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.FloatTensor  ,						snake_case__						:     torch.FloatTensor  ,						):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							lowerCAmelCase__        =   timesteps.to(original_samples.device     )
							lowerCAmelCase__        =   self.discrete_sigmas.to(original_samples.device     )[timesteps]
							lowerCAmelCase__        =   (
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(snake_case__     ) * sigmas[:, None, None, None]
							)
							lowerCAmelCase__        =   noise + original_samples
							return noisy_samples




					def __len__(	self						:     List[Any]     ):
							return self.config.num_train_timesteps





"""simple docstring"""




import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    LayoutLMTokenizer
					UpperCamelCase_ :	List[Any]			=    LayoutLMTokenizerFast
					UpperCamelCase_ :	Dict			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

							lowerCAmelCase__        =   [
							    """[UNK]""",
							    """[CLS]""",
							    """[SEP]""",
							    """want""",
							    """##want""",
							    """##ed""",
							    """wa""",
							    """un""",
							    """runn""",
							    """##ing""",
							    """,""",
							    """low""",
							    """lowest""",
							]
							lowerCAmelCase__        =   os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["""vocab_file"""]     )
							with open(self.vocab_file  ,						"""w"""  ,						encoding="""utf-8"""     ) as vocab_writer:
									vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						**snake_case__						:     Union[str, Any]     ):
							return LayoutLMTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Tuple     ):
							lowerCAmelCase__        =   """UNwant\u00E9d,running"""
							lowerCAmelCase__        =   """unwanted, running"""
							return input_text, output_text




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   self.tokenizer_class(self.vocab_file     )

							lowerCAmelCase__        =   tokenizer.tokenize("""UNwant\u00E9d,running"""     )
							self.assertListEqual(snake_case__  ,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]     )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__     )  ,						[7, 4, 5, 10, 8, 9]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							pass




