


import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 			SCREAMING_SNAKE_CASE__  (    __UpperCamelCase						,   unittest.TestCase      ):
      snake_case__    :					List[Any]			       =						LxmertTokenizer
      snake_case__    :					Optional[Any]			       =						LxmertTokenizerFast
      snake_case__    :					Dict			       =						True
      snake_case__    :					Union[str, Any]			       =						True
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             super().setUp()

             a_ :		Optional[int]							=  [
                 """[UNK]""",
                 """[CLS]""",
                 """[SEP]""",
                 """want""",
                 """##want""",
                 """##ed""",
                 """wa""",
                 """un""",
                 """runn""",
                 """##ing""",
                 """,""",
                 """low""",
                 """lowest""",
             ]
             a_ :		Tuple							=  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    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      List[Any]:
             a_ :		Union[str, Any]							=  """UNwant\u00E9d,running"""
             a_ :		List[Any]							=  """unwanted, running"""
             return input_text, output_text
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      List[str]:
             a_ :		Optional[int]							=  self.tokenizer_class(self.vocab_file					)

             a_ :		str							=  tokenizer.tokenize('UNwant\u00E9d,running'					)
             self.assertListEqual(_lowerCAmelCase					,							['un', '##want', '##ed', ',', 'runn', '##ing']					)
             self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase					)					,							[7, 4, 5, 1_0, 8, 9]					)

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

             a_ :		Optional[Any]							=  self.get_tokenizer()
             a_ :		int							=  self.get_rust_tokenizer()

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

             a_ :		int							=  tokenizer.tokenize(_lowerCAmelCase					)
             a_ :		Dict							=  rust_tokenizer.tokenize(_lowerCAmelCase					)
             self.assertListEqual(_lowerCAmelCase					,							_lowerCAmelCase					)

             a_ :		List[Any]							=  tokenizer.encode(_lowerCAmelCase					,							add_special_tokens=_lowerCAmelCase					)
             a_ :		Dict							=  rust_tokenizer.encode(_lowerCAmelCase					,							add_special_tokens=_lowerCAmelCase					)
             self.assertListEqual(_lowerCAmelCase					,							_lowerCAmelCase					)

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






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

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   lowercase__      ):
      snake_case__    :					Dict					       =						1
      @register_to_config
      def __init__(	self    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	Tuple=2_0_0_0					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]=0.1					,							SCREAMING_SNAKE_CASE__    :	Optional[int]=2_0					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]=1E-3					)		->      Optional[int]:
             a_ :		Tuple							=  None
             a_ :		int							=  None
             a_ :		Tuple							=  None
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Union[str, torch.device] = None					)		->      List[str]:
             a_ :		Tuple							=  torch.linspace(1					,							self.config.sampling_eps					,							SCREAMING_SNAKE_CASE__					,							device=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[Any]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	Tuple=None					)		->      Tuple:
             if self.timesteps is None:
                    raise ValueError(
                        '`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'					)

             # TODO(Patrick) better comments + non-PyTorch
             # postprocess model score
             a_ :		Tuple							=  (
                 -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
             )
             a_ :		int							=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
             a_ :		Dict							=  std.flatten()
             while len(std.shape					) < len(score.shape					):
                    a_ :		str							=  std.unsqueeze(-1					)
             a_ :		List[str]							=  -score / std

             # compute
             a_ :		List[str]							=  -1.0 / len(self.timesteps					)

             a_ :		Optional[int]							=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
             a_ :		Optional[Any]							=  beta_t.flatten()
             while len(beta_t.shape					) < len(x.shape					):
                    a_ :		List[str]							=  beta_t.unsqueeze(-1					)
             a_ :		Optional[Any]							=  -0.5 * beta_t * x

             a_ :		Tuple							=  torch.sqrt(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  drift - diffusion**2 * score
             a_ :		List[str]							=  x + drift * dt

             # add noise
             a_ :		Optional[Any]							=  randn_tensor(x.shape					,							layout=x.layout					,							generator=SCREAMING_SNAKE_CASE__					,							device=x.device					,							dtype=x.dtype					)
             a_ :		Optional[Any]							=  x_mean + diffusion * math.sqrt(-dt					) * noise

             return x, x_mean

      def __len__(	self    :	int					)		->      Tuple:
             return self.config.num_train_timesteps


