


"""simple docstring"""



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
 from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
 UpperCAmelCase      		=			get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase      		=			128_022
UpperCAmelCase      		=			128_028
@require_sentencepiece
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Optional[int]        =   MaMaaaTokenizer
       __A			:      List[str]        =   False
       __A			:      int        =   False
       __A			:      Dict        =   True






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              super().setUp()

              lowercase		:Tuple       							=				['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
              lowercase		:Optional[Any]       							=				dict(zip(snake_case__  ,     range(len(snake_case__				)				)				)				)
              lowercase		:List[str]       							=				Path(self.tmpdirname				)
              save_json(snake_case__  ,     save_dir / VOCAB_FILES_NAMES['''vocab_file''']				)
              if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                     copyfile(snake_case__  ,     save_dir / VOCAB_FILES_NAMES['''spm_file''']				)

              lowercase		:Dict       							=				MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
              tokenizer.save_pretrained(self.tmpdirname				)






       def  __snake_case						(   self  :    Union[str, Any]  ,     **snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              return MaMaaaTokenizer.from_pretrained(self.tmpdirname  ,     **snake_case__				)






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    Any				):

              '''simple docstring'''






              return (
                  "This is a test",
                  "This is a test",
              )






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              lowercase		:Tuple       							=				'''</s>'''
              lowercase		:List[str]       							=				0

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






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Tuple       							=				self.get_tokenizer()
              lowercase		:Tuple       							=				list(tokenizer.get_vocab().keys()				)

              self.assertEqual(vocab_keys[0]  ,     '''</s>'''				)
              self.assertEqual(vocab_keys[1]  ,     '''<unk>'''				)
              self.assertEqual(vocab_keys[-1]  ,     '''<s>'''				)
              self.assertEqual(len(snake_case__				)  ,     tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)






       @unittest.skip('''Skip this test while all models are still to be uploaded.'''				)
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self.get_tokenizer()

              lowercase		:int       							=				tokenizer.tokenize('''This is a test'''				)
              self.assertListEqual(snake_case__  ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(snake_case__				)  ,     [2, 3, 4, 5, 6]  ,     )

              lowercase		:List[Any]       							=				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
              self.assertListEqual(snake_case__  ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

              lowercase		:Tuple       							=				tokenizer.convert_tokens_to_string(snake_case__				)
              self.assertEqual(snake_case__  ,     '''This is a test'''				)







       @slow
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Dict       							=				{'''input_ids''': [[1_2_8_0_2_2, 1_1_0_1_0_8, 3_9_7, 1_1, 3_8_2_7_2, 2_2_4_7, 1_2_4_8_1_1, 2_8_5, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 3_9_5_3_4, 4_4_2_8, 3_9_7, 1_0_1_9, 1_8_1_0_5, 1_5_8_6, 2_0_7, 7, 4_1_3_3_7, 1_6_7_8_6, 2_4_1, 7, 2_0_2_1_4, 1_7, 1_2_5_6_9_0, 1_0_3_9_8, 7, 4_4_3_7_8, 5_8_0_6_9, 6_8_3_4_2, 7_7_9_8, 7_3_4_3, 1_1, 2_9_9, 3_3_3_1_0, 4, 1_5_8, 3_7_3_5_0, 9_4_0_7_7, 4_5_6_9, 2_9_9, 3_3_3_1_0, 9_0, 4, 5_2_8_4_0, 2_9_0, 4, 3_1_2_7_0, 1_1_2, 2_9_9, 6_8_2, 4, 5_2_8_4_0, 3_9_9_5_3, 1_4_0_7_9, 1_9_3, 5_2_5_1_9, 9_0_8_9_4, 1_7_8_9_4, 1_2_0_6_9_7, 1_1, 4_0_4_4_5, 5_5_1, 1_7, 1_0_1_9, 5_2_5_1_9, 9_0_8_9_4, 1_7_7_5_6, 9_6_3, 1_1, 4_0_4_4_5, 4_8_0, 1_7, 9_7_9_2, 1_1_2_0, 5_1_7_3, 1_3_9_3, 6_2_4_0, 1_6_7_8_6, 2_4_1, 1_2_0_9_9_6, 2_8, 1_2_4_5, 1_3_9_3, 1_1_8_2_4_0, 1_1_1_2_3, 1_0_1_9, 9_3_6_1_2, 2_6_9_1, 1_0_6_1_8, 9_8_0_5_8, 1_2_0_4_0_9, 1_9_2_8, 2_7_9, 4, 4_0_6_8_3, 3_6_7, 1_7_8, 2_0_7, 1_0_1_9, 1_0_3, 1_0_3_1_2_1, 5_0_6, 6_5_2_9_6, 5, 2], [1_2_8_0_2_2, 2_1_2_1_7, 3_6_7, 1_1_7, 1_2_5_4_5_0, 1_2_8, 7_1_9, 7, 7_3_0_8, 4_0, 9_3_6_1_2, 1_2_6_6_9, 1_1_1_6, 1_6_7_0_4, 7_1, 1_7_7_8_5, 3_6_9_9, 1_5_5_9_2, 3_5, 1_4_4, 9_5_8_4, 2_4_1, 1_1_9_4_3, 7_1_3, 9_5_0, 7_9_9, 2_2_4_7, 8_8_4_2_7, 1_5_0, 1_4_9, 1_1_8_8_1_3, 1_2_0_7_0_6, 1_0_1_9, 1_0_6_9_0_6, 8_1_5_1_8, 2_8, 1_2_2_4, 2_2_7_9_9, 3_9_7, 5, 2, 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_2_8_0_2_2, 1_6_5_8, 1_2_3_3_1_1, 5_1_5_5, 5_5_7_8, 4_7_2_2, 2_7_9, 1_4_9_4_7, 2_3_6_6, 1_1_2_0, 1_1_9_7, 1_4, 1_3_4_8, 9_2_3_2, 5, 2, 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]], '''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, 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], [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, 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=snake_case__  ,     model_name='''facebook/m2m100_418M'''  ,     revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''  ,     )







@require_torch
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(			unittest.TestCase     ):
       __A			:      List[str]        =   "facebook/m2m100_418M"
       __A			:      int        =   [
           "In my opinion, there are two levels of response from the French government.",
           "NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
       ]
       __A			:      Dict        =   [
           "Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
           "L'affaire NSA souligne l'absence totale de débat sur le renseignement",
       ]

       # fmt: off
       __A			:      Tuple        =   [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]






       @classmethod
       def  __snake_case						(   cls  :    List[str]				):

              '''simple docstring'''






              lowercase		:MaMaaaTokenizer       							=				MaMaaaTokenizer.from_pretrained(
                  cls.checkpoint_name  ,     src_lang='''en'''  ,     tgt_lang='''fr'''				)
              lowercase		:str       							=				1
              return cls






       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              self.assertEqual(self.tokenizer.get_lang_id('''ar'''				)  ,     1_2_8_0_0_6				)
              self.assertEqual(self.tokenizer.get_lang_id('''en'''				)  ,     1_2_8_0_2_2				)
              self.assertEqual(self.tokenizer.get_lang_id('''ro'''				)  ,     1_2_8_0_7_6				)
              self.assertEqual(self.tokenizer.get_lang_id('''mr'''				)  ,     1_2_8_0_6_3				)






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				self.tokenizer.get_vocab()
              self.assertEqual(len(snake_case__				)  ,     self.tokenizer.vocab_size				)
              self.assertEqual(vocab['''<unk>''']  ,     3				)
              self.assertIn(self.tokenizer.get_lang_token('''en'''				)  ,     snake_case__				)






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:str       							=				'''en'''
              lowercase		:List[str]       							=				self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
              self.assertListEqual(self.expected_src_tokens  ,     snake_case__				)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              self.assertIn(snake_case__  ,     self.tokenizer.all_special_ids				)
              # fmt: off
              lowercase		:Optional[Any]       							=				[FR_CODE, 5_3_6_4, 8_2, 8_6_4_2, 4, 2_9_4, 4_7, 8, 1_4_0_2_8, 1_3_6, 3_2_8_6, 9_7_0_6, 6, 9_0_7_9_7, 6, 1_4_4_0_1_2, 1_6_2, 8_8_1_2_8, 3_0_0_6_1, 5, 2]
              # fmt: on
              lowercase		:List[str]       							=				self.tokenizer.decode(snake_case__  ,     skip_special_tokens=snake_case__				)
              lowercase		:str       							=				self.tokenizer.decode(generated_ids[1:]  ,     skip_special_tokens=snake_case__				)
              self.assertEqual(snake_case__  ,     snake_case__				)
              self.assertNotIn(self.tokenizer.eos_token  ,     snake_case__				)






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase		:str       							=				tempfile.mkdtemp()
              lowercase		:Union[str, Any]       							=				self.tokenizer.lang_token_to_id
              self.tokenizer.save_pretrained(snake_case__				)
              lowercase		:Union[str, Any]       							=				MaMaaaTokenizer.from_pretrained(snake_case__				)
              self.assertDictEqual(new_tok.lang_token_to_id  ,     snake_case__				)






       @require_torch
       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              lowercase		:Tuple       							=				'''en'''
              lowercase		:List[Any]       							=				'''fr'''

              lowercase		:Optional[int]       							=				self.tokenizer(self.src_text  ,     text_target=self.tgt_text  ,     padding=snake_case__  ,     return_tensors='''pt'''				)

              lowercase		:Optional[int]       							=				shift_tokens_right(
                  batch['''labels''']  ,     self.tokenizer.pad_token_id  ,     self.tokenizer.eos_token_id				)

              for k in batch:
                     lowercase		:int       							=				batch[k].tolist()
              # batch = {k: v.tolist() for k,v in batch.items()}
              # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
              # batch.decoder_inputs_ids[0][0] ==
              assert batch.input_ids[1][0] == EN_CODE
              assert batch.input_ids[1][-1] == 2
              assert batch.labels[1][0] == FR_CODE
              assert batch.labels[1][-1] == 2
              assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






       @require_torch
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:int       							=				'''mr'''
              self.assertListEqual(self.tokenizer.prefix_tokens  ,     [self.tokenizer.get_lang_id('''mr'''				)]				)
              self.assertListEqual(self.tokenizer.suffix_tokens  ,     [self.tokenizer.eos_token_id]				)

              lowercase		:List[str]       							=				'''zh'''
              self.assertListEqual(self.tokenizer.prefix_tokens  ,     [self.tokenizer.get_lang_id('''zh'''				)]				)
              self.assertListEqual(self.tokenizer.suffix_tokens  ,     [self.tokenizer.eos_token_id]				)






       @require_torch
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:Any       							=				'''mr'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens  ,     [self.tokenizer.get_lang_id('''mr'''				)]				)
              self.assertListEqual(self.tokenizer.suffix_tokens  ,     [self.tokenizer.eos_token_id]				)
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens  ,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)

              lowercase		:Any       							=				'''zh'''
              self.tokenizer._switch_to_target_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens  ,     [self.tokenizer.get_lang_id('''zh'''				)]				)
              self.assertListEqual(self.tokenizer.suffix_tokens  ,     [self.tokenizer.eos_token_id]				)
              self.tokenizer._switch_to_input_mode()
              self.assertListEqual(self.tokenizer.prefix_tokens  ,     [self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)







       @require_torch
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				self.tokenizer._build_translation_inputs('''A test'''  ,     return_tensors='''pt'''  ,     src_lang='''en'''  ,     tgt_lang='''ar'''				)

              self.assertEqual(
                  nested_simplify(snake_case__				)  ,     {
                      # en_XX, A, test, EOS
                      '''input_ids''': [[1_2_8_0_2_2, 5_8, 4_1_8_3, 2]],
                      '''attention_mask''': [[1, 1, 1, 1]],
                      # ar_AR
                      '''forced_bos_token_id''': 1_2_8_0_0_6,
                  }  ,     )





"""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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      torch.FloatTensor
       __A			:      torch.FloatTensor







class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase     ):
       __A			:      Any        =   1






       @register_to_config
       def __init__(   self  :    int  ,     snake_case__  :    int = 2_0_0_0  ,     snake_case__  :    float = 0.15  ,     snake_case__  :    float = 0.01  ,     snake_case__  :    float = 13_48.0  ,     snake_case__  :    float = 1e-5  ,     snake_case__  :    int = 1  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				sigma_max

              # setable values
              lowercase		:List[str]       							=				None

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






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[int] = None				):

              '''simple docstring'''






              return sample






       def  __snake_case						(   self  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    Union[str, torch.device] = None				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				sampling_eps if sampling_eps is not None else self.config.sampling_eps

              lowercase		:Tuple       							=				torch.linspace(1  ,     snake_case__  ,     snake_case__  ,     device=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    int  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None  ,     snake_case__  :    float = None				):

              '''simple docstring'''






              lowercase		:int       							=				sigma_min if sigma_min is not None else self.config.sigma_min
              lowercase		:Any       							=				sigma_max if sigma_max is not None else self.config.sigma_max
              lowercase		:Tuple       							=				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__				)

              lowercase		:Union[str, Any]       							=				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
              lowercase		:str       							=				torch.exp(torch.linspace(math.log(snake_case__				)  ,     math.log(snake_case__				)  ,     snake_case__				)				)
              lowercase		:List[str]       							=				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]				)






       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Tuple  ,     snake_case__  :    int				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    int  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = 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'''				)

              lowercase		:Optional[int]       							=				timestep * torch.ones(
                  sample.shape[0]  ,     device=sample.device				)  # torch.repeat_interleave(timestep, sample.shape[0])
              lowercase		: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
              lowercase		:Union[str, Any]       							=				timesteps.to(self.discrete_sigmas.device				)

              lowercase		:List[Any]       							=				self.discrete_sigmas[timesteps].to(sample.device				)
              lowercase		:str       							=				self.get_adjacent_sigma(snake_case__  ,     snake_case__				).to(sample.device				)
              lowercase		:Optional[Any]       							=				torch.zeros_like(snake_case__				)
              lowercase		:List[Any]       							=				(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
              lowercase		:Union[str, Any]       							=				diffusion.flatten()
              while len(diffusion.shape				) < len(sample.shape				):
                     lowercase		:Dict       							=				diffusion.unsqueeze(-1				)
              lowercase		:List[Any]       							=				drift - diffusion**2 * model_output

              #  equation 6: sample noise for the diffusion term of
              lowercase		:List[Any]       							=				randn_tensor(
                  sample.shape  ,     layout=sample.layout  ,     generator=snake_case__  ,     device=sample.device  ,     dtype=sample.dtype				)
              lowercase		:int       							=				sample - drift  # subtract because `dt` is a small negative timestep
              # TODO is the variable diffusion the correct scaling term for the noise?
              lowercase		:Any       							=				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  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    Optional[torch.Generator] = None  ,     snake_case__  :    bool = 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
              lowercase		:Tuple       							=				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
              lowercase		:List[Any]       							=				torch.norm(model_output.reshape(model_output.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:List[str]       							=				torch.norm(noise.reshape(noise.shape[0]  ,     -1				)  ,     dim=-1				).mean()
              lowercase		:int       							=				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
              lowercase		:Optional[Any]       							=				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
              lowercase		:Union[str, Any]       							=				step_size.flatten()
              while len(step_size.shape				) < len(sample.shape				):
                     lowercase		:Union[str, Any]       							=				step_size.unsqueeze(-1				)
              lowercase		:Union[str, Any]       							=				sample + step_size * model_output
              lowercase		:Any       							=				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

              if not return_dict:
                     return (prev_sample,)

              return SchedulerOutput(prev_sample=snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     snake_case__  :    torch.FloatTensor  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				timesteps.to(original_samples.device				)
              lowercase		:str       							=				self.discrete_sigmas.to(original_samples.device				)[timesteps]
              lowercase		:Optional[int]       							=				(
                  noise * sigmas[:, None, None, None]
                  if noise is not None
                  else torch.randn_like(snake_case__				) * sigmas[:, None, None, None]
              )
              lowercase		:str       							=				noise + original_samples
              return noisy_samples







       def __len__(   self  :    Optional[Any]				):

              '''simple docstring'''






              return self.config.num_train_timesteps

