






# 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 __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      42
	UpperCamelCase__								=      42





class __lowerCamelCase (      snake_case__     ,       snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      1

	@register_to_config
	def __init__(							self       ,    UpperCAmelCase = 2000       ,    UpperCAmelCase = 0.15       ,    UpperCAmelCase = 0.01       ,    UpperCAmelCase = 13_48.0       ,    UpperCAmelCase = 1e-5       ,    UpperCAmelCase = 1       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			sigma_max

							# setable values
							_UpperCAmelCase									=			None

							self.set_sigmas(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							return sample

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			sampling_eps if sampling_eps is not None else self.config.sampling_eps

							_UpperCAmelCase									=			torch.linspace(1       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    device=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							_UpperCAmelCase									=			sigma_min if sigma_min is not None else self.config.sigma_min
							_UpperCAmelCase									=			sigma_max if sigma_max is not None else self.config.sigma_max
							_UpperCAmelCase									=			sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
													self.set_timesteps(UpperCAmelCase       ,    UpperCAmelCase    )

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							return torch.where(
							    timesteps == 0       ,    torch.zeros_like(t.to(timesteps.device    )    )       ,    self.discrete_sigmas[timesteps - 1].to(timesteps.device    )       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    ):
							"""simple docstring"""







							if self.timesteps is None:
													raise ValueError(
													    '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'    )

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

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

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    ):
							"""simple docstring"""







							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									=			randn_tensor(sample.shape       ,    layout=sample.layout       ,    generator=UpperCAmelCase    ).to(sample.device    )

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

							if not return_dict:
													return (prev_sample,)

							return SchedulerOutput(prev_sample=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			timesteps.to(original_samples.device    )
							_UpperCAmelCase									=			self.discrete_sigmas.to(original_samples.device    )[timesteps]
							_UpperCAmelCase									=			(
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(UpperCAmelCase    ) * sigmas[:, None, None, None]
							)
							_UpperCAmelCase									=			noise + original_samples
							return noisy_samples

	def __len__(							self    ):
							"""simple docstring"""







							return self.config.num_train_timesteps








import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      TransfoXLTokenizer
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase									=			[
							    '<unk>',
							    '[CLS]',
							    '[SEP]',
							    'want',
							    'unwanted',
							    'wa',
							    'un',
							    'running',
							    ',',
							    'low',
							    'l',
							]
							_UpperCAmelCase									=			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        UpperCamelCase    (							self       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			True
							return TransfoXLTokenizer.from_pretrained(self.tmpdirname       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<unk> UNwanted , running'
							_UpperCAmelCase									=			'<unk> unwanted, running'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(vocab_file=self.vocab_file       ,    lower_case=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('<unk> UNwanted , running'    )
							self.assertListEqual(UpperCAmelCase       ,    ['<unk>', 'unwanted', ',', 'running']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [0, 4, 8, 7]    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['hello', '!', 'how', 'are', 'you', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '    )       ,    ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TransfoXLTokenizer(lower_case=UpperCAmelCase    )
							_UpperCAmelCase									=			'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
							_UpperCAmelCase									=			[
							    'Hello',
							    '(',
							    'bracket',
							    ')',
							    'and',
							    'side',
							    '@-@',
							    'scrolled',
							    '[',
							    'and',
							    ']',
							    'Henry',
							    '\'s',
							    '$',
							    '5',
							    '@,@',
							    '000',
							    'with',
							    '3',
							    '@.@',
							    '34',
							    'm',
							    '.',
							    'What',
							    '\'s',
							    'up',
							    '!',
							    '?',
							]

							self.assertListEqual(tokenizer.tokenize(UpperCAmelCase    )       ,    UpperCAmelCase    )

							self.assertEqual(tokenizer.convert_tokens_to_string(UpperCAmelCase    )       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			len(UpperCAmelCase    )

							tokenizer.add_tokens(['new1', 'new2']    )
							tokenizer.move_added_token('new1'       ,    1    )

							# Check that moved token is not copied (duplicate)
							self.assertEqual(len(UpperCAmelCase    )       ,    original_len + 2    )
							# Check that token is moved to specified id
							self.assertEqual(tokenizer.encode('new1'    )       ,    [1]    )
							self.assertEqual(tokenizer.decode([1]    )       ,    'new1'    )
