



'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase	:       List[str]      	=	get_tests_dir("fixtures/test_sentencepiece.model")


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

_lowerCamelCase	:       Optional[int]      	=	25_6047
_lowerCamelCase	:       Dict      	=	25_6145

@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE (  _a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  NllbTokenizer
    _SCREAMING_SNAKE_CASE	  =  NllbTokenizerFast
    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  {}






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase					=  NllbTokenizer(UpperCamelCase__  ,						keep_accents=UpperCamelCase__ )
       tokenizer.save_pretrained(self.tmpdirname )






    def  A       (  self      :					int ):


       """simple docstring"""







       UpperCamelCase					=  NllbTokenizer(UpperCamelCase__  ,						keep_accents=UpperCamelCase__ )

       UpperCamelCase					=  tokenizer.tokenize('This is a test' )
       self.assertListEqual(UpperCamelCase__  ,						['▁This', '▁is', '▁a', '▁t', 'est'] )

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(UpperCamelCase__ )  ,						[value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]  ,						)

       UpperCamelCase					=  tokenizer.tokenize('I was born in 92000, and this is falsé.' )
       self.assertListEqual(
           UpperCamelCase__  ,						[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '9',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               'é',
               '.',
           ]  ,						)
       UpperCamelCase					=  tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
       self.assertListEqual(
           UpperCamelCase__  ,						[
               value + tokenizer.fairseq_offset
               for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
           ]  ,						)

       UpperCamelCase					=  tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
       self.assertListEqual(
           UpperCamelCase__  ,						[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '<unk>',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               '<unk>',
               '.',
           ]  ,						)






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
             UpperCamelCase					=  self.rust_tokenizer_class.from_pretrained(UpperCamelCase__  ,						**UpperCamelCase__ )
             UpperCamelCase					=  self.tokenizer_class.from_pretrained(UpperCamelCase__  ,						**UpperCamelCase__ )

             UpperCamelCase					=  tempfile.mkdtemp()

             UpperCamelCase					=  tokenizer_r.save_pretrained(UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.save_pretrained(UpperCamelCase__ )

             # Checks it save with the same files + the tokenizer.json file for the fast one
             self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
             UpperCamelCase					=  tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
             self.assertSequenceEqual(UpperCamelCase__  ,						UpperCamelCase__ )

             # Checks everything loads correctly in the same way
             UpperCamelCase					=  tokenizer_r.from_pretrained(UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.from_pretrained(UpperCamelCase__ )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                self.assertTrue(hasattr(UpperCamelCase__  ,						UpperCamelCase__ ) )

             shutil.rmtree(UpperCamelCase__ )

             # Save tokenizer rust, legacy_format=True
             UpperCamelCase					=  tempfile.mkdtemp()

             UpperCamelCase					=  tokenizer_r.save_pretrained(UpperCamelCase__  ,						legacy_format=UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.save_pretrained(UpperCamelCase__ )

             # Checks it save with the same files
             self.assertSequenceEqual(UpperCamelCase__  ,						UpperCamelCase__ )

             # Checks everything loads correctly in the same way
             UpperCamelCase					=  tokenizer_r.from_pretrained(UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.from_pretrained(UpperCamelCase__ )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                self.assertTrue(hasattr(UpperCamelCase__  ,						UpperCamelCase__ ) )

             shutil.rmtree(UpperCamelCase__ )

             # Save tokenizer rust, legacy_format=False
             UpperCamelCase					=  tempfile.mkdtemp()

             UpperCamelCase					=  tokenizer_r.save_pretrained(UpperCamelCase__  ,						legacy_format=UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.save_pretrained(UpperCamelCase__ )

             # Checks it saved the tokenizer.json file
             self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )

             # Checks everything loads correctly in the same way
             UpperCamelCase					=  tokenizer_r.from_pretrained(UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_p.from_pretrained(UpperCamelCase__ )

             # Check special tokens are set accordingly on Rust and Python
             for key in tokenizer_pp.special_tokens_map:
                self.assertTrue(hasattr(UpperCamelCase__  ,						UpperCamelCase__ ) )

             shutil.rmtree(UpperCamelCase__ )






    @require_torch
    def  A       (  self      :					Dict ):


       """simple docstring"""







       if not self.test_seqaseq:
          return

       UpperCamelCase					=  self.get_tokenizers()
       for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
             # Longer text that will definitely require truncation.
             UpperCamelCase					=  [
                 ' UN Chief Says There Is No Military Solution in Syria',
                 ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
                 ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
                 ' will only worsen the violence and misery for millions of people.',
             ]
             UpperCamelCase					=  [
                 'Şeful ONU declară că nu există o soluţie militară în Siria',
                 'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
                 ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
                 ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
             ]
             try:
                UpperCamelCase					=  tokenizer.prepare_seqaseq_batch(
                    src_texts=UpperCamelCase__  ,						tgt_texts=UpperCamelCase__  ,						max_length=3  ,						max_target_length=1_0  ,						return_tensors='pt'  ,						src_lang='eng_Latn'  ,						tgt_lang='ron_Latn'  ,						)
             except NotImplementedError:
                return
             self.assertEqual(batch.input_ids.shape[1]  ,						3 )
             self.assertEqual(batch.labels.shape[1]  ,						1_0 )
             # max_target_length will default to max_length if not specified
             UpperCamelCase					=  tokenizer.prepare_seqaseq_batch(
                 UpperCamelCase__  ,						tgt_texts=UpperCamelCase__  ,						max_length=3  ,						return_tensors='pt' )
             self.assertEqual(batch.input_ids.shape[1]  ,						3 )
             self.assertEqual(batch.labels.shape[1]  ,						3 )

             UpperCamelCase					=  tokenizer.prepare_seqaseq_batch(
                 src_texts=UpperCamelCase__  ,						max_length=3  ,						max_target_length=1_0  ,						return_tensors='pt' )
             self.assertEqual(batch_encoder_only.input_ids.shape[1]  ,						3 )
             self.assertEqual(batch_encoder_only.attention_mask.shape[1]  ,						3 )
             self.assertNotIn('decoder_input_ids'  ,						UpperCamelCase__ )






    @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
             UpperCamelCase					=  [AddedToken('<special>'  ,						lstrip=UpperCamelCase__ )]

             UpperCamelCase					=  self.rust_tokenizer_class.from_pretrained(
                 UpperCamelCase__  ,						additional_special_tokens=UpperCamelCase__  ,						**UpperCamelCase__ )
             UpperCamelCase					=  tokenizer_r.encode('Hey this is a <special> token' )

             UpperCamelCase					=  tokenizer_r.encode('<special>'  ,						add_special_tokens=UpperCamelCase__ )[0]

             self.assertTrue(special_token_id in r_output )

             if self.test_slow_tokenizer:
                UpperCamelCase					=  self.rust_tokenizer_class.from_pretrained(
                    UpperCamelCase__  ,						additional_special_tokens=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
                UpperCamelCase					=  self.tokenizer_class.from_pretrained(
                    UpperCamelCase__  ,						additional_special_tokens=UpperCamelCase__  ,						**UpperCamelCase__ )

                UpperCamelCase					=  tokenizer_p.encode('Hey this is a <special> token' )

                UpperCamelCase					=  tokenizer_cr.encode('Hey this is a <special> token' )

                self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
                self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
                self.assertTrue(special_token_id in p_output )
                self.assertTrue(special_token_id in cr_output )

@require_torch
@require_sentencepiece
@require_tokenizers
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  """facebook/nllb-200-distilled-600M"""
    _SCREAMING_SNAKE_CASE	  =  [
        """ UN Chief Says There Is No Military Solution in Syria""",
        """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
    ]
    _SCREAMING_SNAKE_CASE	  =  [
        """Şeful ONU declară că nu există o soluţie militară în Siria""",
        """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
        """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
        """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
    ]
    _SCREAMING_SNAKE_CASE	  =  [
        256_047,
        16_297,
        134_408,
        8_165,
        248_066,
        14_734,
        950,
        1_135,
        105_721,
        3_573,
        83,
        27_352,
        108,
        49_486,
        2,
    ]






    @classmethod
    def  A       (  cls      :					str ):


       """simple docstring"""







       UpperCamelCase					=  NllbTokenizer.from_pretrained(
           cls.checkpoint_name  ,						src_lang='eng_Latn'  ,						tgt_lang='ron_Latn' )
       UpperCamelCase					=  1
       return cls






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']  ,						2_5_6_0_0_1 )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']  ,						2_5_6_0_0_2 )
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']  ,						2_5_6_0_5_7 )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
       self.assertListEqual(self.expected_src_tokens  ,						UpperCamelCase__ )






    def  A       (  self      :					Any ):


       """simple docstring"""







       self.assertIn(UpperCamelCase__  ,						self.tokenizer.all_special_ids )
       # fmt: off
       UpperCamelCase					=  [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
       # fmt: on

       UpperCamelCase					=  self.tokenizer.decode(UpperCamelCase__  ,						skip_special_tokens=UpperCamelCase__ )
       UpperCamelCase					=  self.tokenizer.decode(generated_ids[1:]  ,						skip_special_tokens=UpperCamelCase__ )
       self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
       self.assertNotIn(self.tokenizer.eos_token  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       UpperCamelCase					=  ['this is gunna be a long sentence ' * 2_0]
       assert isinstance(src_text[0]  ,						UpperCamelCase__ )
       UpperCamelCase					=  1_0
       UpperCamelCase					=  self.tokenizer(UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						truncation=UpperCamelCase__ ).input_ids[0]
       self.assertEqual(ids[-1]  ,						2 )
       self.assertEqual(ids[0]  ,						UpperCamelCase__ )
       self.assertEqual(len(UpperCamelCase__ )  ,						UpperCamelCase__ )






    def  A       (  self      :					int ):


       """simple docstring"""







       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] )  ,						[2_5_6_2_0_3, 3] )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       UpperCamelCase					=  tempfile.mkdtemp()
       UpperCamelCase					=  self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(UpperCamelCase__ )
       UpperCamelCase					=  NllbTokenizer.from_pretrained(UpperCamelCase__ )
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids  ,						UpperCamelCase__ )






    @require_torch
    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer(
           self.src_text  ,						text_target=self.tgt_text  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=len(self.expected_src_tokens )  ,						return_tensors='pt'  ,						)
       UpperCamelCase					=  shift_tokens_right(
           batch['labels']  ,						self.tokenizer.pad_token_id  ,						self.tokenizer.lang_code_to_id['ron_Latn'] )

       self.assertIsInstance(UpperCamelCase__  ,						UpperCamelCase__ )

       self.assertEqual((2, 1_5)  ,						batch.input_ids.shape )
       self.assertEqual((2, 1_5)  ,						batch.attention_mask.shape )
       UpperCamelCase					=  batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens  ,						UpperCamelCase__ )
       self.assertEqual(UpperCamelCase__  ,						batch.decoder_input_ids[0, 0] )  # EOS
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens  ,						[EN_CODE] )
       self.assertEqual(self.tokenizer.suffix_tokens  ,						[self.tokenizer.eos_token_id] )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer(self.src_text  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=3  ,						return_tensors='pt' )
       UpperCamelCase					=  self.tokenizer(
           text_target=self.tgt_text  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=1_0  ,						return_tensors='pt' )
       UpperCamelCase					=  targets['input_ids']
       UpperCamelCase					=  shift_tokens_right(
           UpperCamelCase__  ,						self.tokenizer.pad_token_id  ,						decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]  ,						)

       self.assertEqual(batch.input_ids.shape[1]  ,						3 )
       self.assertEqual(batch.decoder_input_ids.shape[1]  ,						1_0 )






    @require_torch
    def  A       (  self      :					str ):


       """simple docstring"""







       UpperCamelCase					=  self.tokenizer._build_translation_inputs(
           'A test'  ,						return_tensors='pt'  ,						src_lang='eng_Latn'  ,						tgt_lang='fra_Latn' )

       self.assertEqual(
           nested_simplify(UpperCamelCase__ )  ,						{
               # A, test, EOS, en_XX
               'input_ids': [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
               'attention_mask': [[1, 1, 1, 1]],
               # ar_AR
               'forced_bos_token_id': 2_5_6_0_5_7,
           }  ,						)






    @require_torch
    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  True
       UpperCamelCase					=  self.tokenizer(
           'UN Chief says there is no military solution in Syria'  ,						src_lang='eng_Latn'  ,						tgt_lang='fra_Latn' )
       self.assertEqual(
           inputs.input_ids  ,						[1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7] )

       UpperCamelCase					=  False
       UpperCamelCase					=  self.tokenizer(
           'UN Chief says there is no military solution in Syria'  ,						src_lang='eng_Latn'  ,						tgt_lang='fra_Latn' )
       self.assertEqual(
           inputs.input_ids  ,						[2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2] )






'''simple docstring'''


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

import numpy as np
import torch

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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None







def 				__lowerCamelCase				(   A__    ,						A__=0.999    ,						A__="cosine"    ,						)       ->	Tuple:






   """simple docstring"""
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(A__    ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(A__    ):
         return math.exp(t * -12.0    )

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

   UpperCamelCase					=  []
   for i in range(A__    ):
      UpperCamelCase					=  i / num_diffusion_timesteps
      UpperCamelCase					=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(A__    ) / alpha_bar_fn(A__    )    ,						A__    )    )
   return torch.tensor(A__    ,						dtype=torch.floataa    )

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    @register_to_config
    def __init__(  self      :					List[str]  ,						UpperCamelCase__      :					int = 1_0_0_0  ,						UpperCamelCase__      :					str = "fixed_small_log"  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					Optional[float] = 1.0  ,						UpperCamelCase__      :					str = "epsilon"  ,						UpperCamelCase__      :					str = "squaredcos_cap_v2"  ,						):


       """simple docstring"""







       if beta_schedule != "squaredcos_cap_v2":
          raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )

       UpperCamelCase					=  betas_for_alpha_bar(UpperCamelCase__ )

       UpperCamelCase					=  1.0 - self.betas
       UpperCamelCase					=  torch.cumprod(self.alphas  ,						dim=0 )
       UpperCamelCase					=  torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       UpperCamelCase					=  1.0

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  torch.from_numpy(np.arange(0  ,						UpperCamelCase__ )[::-1].copy() )

       UpperCamelCase					=  variance_type






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       UpperCamelCase					=  (np.arange(0  ,						UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]=None  ,						UpperCamelCase__      :					Optional[int]=None  ,						UpperCamelCase__      :					Tuple=None ):


       """simple docstring"""







       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       UpperCamelCase					=  beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
          UpperCamelCase					=  self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
          UpperCamelCase					=  torch.log(torch.clamp(UpperCamelCase__  ,						min=1E-2_0 ) )
          UpperCamelCase					=  torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
          # NOTE difference with DDPM scheduler
          UpperCamelCase					=  variance.log()
          UpperCamelCase					=  beta.log()

          UpperCamelCase					=  (predicted_variance + 1) / 2
          UpperCamelCase					=  frac * max_log + (1 - frac) * min_log

       return variance






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None  ,						UpperCamelCase__      :					str=None  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
          UpperCamelCase				,      UpperCamelCase					=  torch.split(UpperCamelCase__  ,						sample.shape[1]  ,						dim=1 )
       else:
          UpperCamelCase					=  None

       # 1. compute alphas, betas
       if prev_timestep is None:
          UpperCamelCase					=  t - 1

       UpperCamelCase					=  self.alphas_cumprod[t]
       UpperCamelCase					=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       UpperCamelCase					=  1 - alpha_prod_t
       UpperCamelCase					=  1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
          UpperCamelCase					=  self.betas[t]
          UpperCamelCase					=  self.alphas[t]
       else:
          UpperCamelCase					=  1 - alpha_prod_t / alpha_prod_t_prev
          UpperCamelCase					=  1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
          UpperCamelCase					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
          UpperCamelCase					=  model_output
       else:
          raise ValueError(
              f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
              ' for the UnCLIPScheduler.' )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
          UpperCamelCase					=  torch.clamp(
              UpperCamelCase__  ,						-self.config.clip_sample_range  ,						self.config.clip_sample_range )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase					=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       UpperCamelCase					=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       UpperCamelCase					=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       UpperCamelCase					=  0
       if t > 0:
          UpperCamelCase					=  randn_tensor(
              model_output.shape  ,						dtype=model_output.dtype  ,						generator=UpperCamelCase__  ,						device=model_output.device )

          UpperCamelCase					=  self._get_variance(
              UpperCamelCase__  ,						predicted_variance=UpperCamelCase__  ,						prev_timestep=UpperCamelCase__  ,						)

          if self.variance_type == "fixed_small_log":
             UpperCamelCase					=  variance
          elif self.variance_type == "learned_range":
             UpperCamelCase					=  (0.5 * variance).exp()
          else:
             raise ValueError(
                 f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                 ' for the UnCLIPScheduler.' )

          UpperCamelCase					=  variance * variance_noise

       UpperCamelCase					=  pred_prev_sample + variance

       if not return_dict:
          return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					int  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.IntTensor  ,						):


       """simple docstring"""







       UpperCamelCase					=  self.alphas_cumprod.to(device=original_samples.device  ,						dtype=original_samples.dtype )
       UpperCamelCase					=  timesteps.to(original_samples.device )

       UpperCamelCase					=  alphas_cumprod[timesteps] ** 0.5
       UpperCamelCase					=  sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  (1 - alphas_cumprod[timesteps]) ** 0.5
       UpperCamelCase					=  sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
          UpperCamelCase					=  sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       UpperCamelCase					=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples

