"""simple docstring"""






import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase       :			Union[str, Any]   	=get_tests_dir("""fixtures/test_sentencepiece.model""")





@require_sentencepiece
@require_tokenizers
class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  str				    =      XLMRobertaTokenizer
      snake_case__     :  str				    =      XLMRobertaTokenizerFast
      snake_case__     :  str				    =      True
      snake_case__     :  Optional[int]				    =      True

      def 							A__			(		self		):







            """simple docstring"""



            super().setUp()

            # We have a SentencePiece fixture for testing
            lowercase								=      XLMRobertaTokenizer(__lowerCAmelCase						,			keep_accents=__lowerCAmelCase		)
            tokenizer.save_pretrained(self.tmpdirname		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """<pad>"""
            lowercase								=      1

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

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      list(self.get_tokenizer().get_vocab().keys()		)

            self.assertEqual(vocab_keys[0]						,			"""<s>"""		)
            self.assertEqual(vocab_keys[1]						,			"""<pad>"""		)
            self.assertEqual(vocab_keys[-1]						,			"""<mask>"""		)
            self.assertEqual(len(__lowerCAmelCase		)						,			1002		)

      def 							A__			(		self		):







            """simple docstring"""



            self.assertEqual(self.get_tokenizer().vocab_size						,			1002		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      XLMRobertaTokenizer(__lowerCAmelCase						,			keep_accents=__lowerCAmelCase		)

            lowercase								=      tokenizer.tokenize("""This is a test"""		)
            self.assertListEqual(__lowerCAmelCase						,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

            self.assertListEqual(
                tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)						,			[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]						,			)

            lowercase								=      tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
            self.assertListEqual(
                __lowerCAmelCase						,			[
                    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""",
                    """.""",
                ]						,			)
            lowercase								=      tokenizer.convert_tokens_to_ids(__lowerCAmelCase		)
            self.assertListEqual(
                __lowerCAmelCase						,			[
                    value + tokenizer.fairseq_offset
                    for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                    #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                ]						,			)

            lowercase								=      tokenizer.convert_ids_to_tokens(__lowerCAmelCase		)
            self.assertListEqual(
                __lowerCAmelCase						,			[
                    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		):







            """simple docstring"""



            if not self.test_slow_tokenizer:
                  # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                  return

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

                        lowercase								=      tempfile.mkdtemp()

                        lowercase								=      tokenizer_r.save_pretrained(__lowerCAmelCase		)
                        lowercase								=      tokenizer_p.save_pretrained(__lowerCAmelCase		)

                        # 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		)		)
                        lowercase								=      tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f		)
                        self.assertSequenceEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

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

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                              self.assertTrue(hasattr(__lowerCAmelCase						,			__lowerCAmelCase		)		)
                              # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                              # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                        shutil.rmtree(__lowerCAmelCase		)

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

                        lowercase								=      tokenizer_r.save_pretrained(__lowerCAmelCase						,			legacy_format=__lowerCAmelCase		)
                        lowercase								=      tokenizer_p.save_pretrained(__lowerCAmelCase		)

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

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

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

                        shutil.rmtree(__lowerCAmelCase		)

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

                        lowercase								=      tokenizer_r.save_pretrained(__lowerCAmelCase						,			legacy_format=__lowerCAmelCase		)
                        lowercase								=      tokenizer_p.save_pretrained(__lowerCAmelCase		)

                        # 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
                        lowercase								=      tokenizer_r.from_pretrained(__lowerCAmelCase		)
                        lowercase								=      tokenizer_p.from_pretrained(__lowerCAmelCase		)

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

                        shutil.rmtree(__lowerCAmelCase		)

      @cached_property
      def 							A__			(		self		):







            """simple docstring"""



            return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.NamedTemporaryFile() as f:
                  shutil.copyfile(__lowerCAmelCase						,			f.name		)
                  lowercase								=      XLMRobertaTokenizer(f.name						,			keep_accents=__lowerCAmelCase		)
                  lowercase								=      pickle.dumps(__lowerCAmelCase		)
            pickle.loads(__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            if not self.test_rust_tokenizer:
                  return

            lowercase								=      self.get_tokenizer()
            lowercase								=      self.get_rust_tokenizer()

            lowercase								=      """I was born in 92000, and this is falsé."""

            lowercase								=      tokenizer.tokenize(__lowerCAmelCase		)
            lowercase								=      rust_tokenizer.tokenize(__lowerCAmelCase		)
            self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

            lowercase								=      tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
            lowercase								=      rust_tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
            self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

            lowercase								=      self.get_rust_tokenizer()
            lowercase								=      tokenizer.encode(__lowerCAmelCase		)
            lowercase								=      rust_tokenizer.encode(__lowerCAmelCase		)
            self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      """Hello World!"""
            lowercase								=      [0, 3_5378, 6661, 38, 2]
            # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
            # xlmr.eval()
            # xlmr.encode(symbols)

            self.assertListEqual(__lowerCAmelCase						,			self.big_tokenizer.encode(__lowerCAmelCase		)		)

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      (
                """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
                """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
            )
            lowercase								=      [
                0,
                3293,
                83,
                10,
                4552,
                4989,
                7986,
                678,
                10,
                5915,
                111,
                17_9459,
                12_4850,
                4,
                6044,
                237,
                12,
                6,
                5,
                6,
                4,
                6780,
                705,
                15,
                1388,
                44,
                378,
                1_0114,
                711,
                152,
                20,
                6,
                5,
                2_2376,
                642,
                1221,
                1_5190,
                3_4153,
                450,
                5608,
                959,
                1119,
                5_7702,
                136,
                186,
                47,
                1098,
                2_9367,
                47,
                # 4426, # What fairseq tokenizes from "<unk>": "_<"
                # 3678, # What fairseq tokenizes from "<unk>": "unk"
                # 2740, # What fairseq tokenizes from "<unk>": ">"
                3,  # What we tokenize from "<unk>": "<unk>"
                6,  # Residue from the tokenization: an extra sentencepiece underline
                4,
                6044,
                237,
                6284,
                5_0901,
                528,
                31,
                90,
                34,
                927,
                2,
            ]
            # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
            # xlmr.eval()
            # xlmr.encode(symbols)

            self.assertListEqual(__lowerCAmelCase						,			self.big_tokenizer.encode(__lowerCAmelCase		)		)







      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      {"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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]], """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, 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], [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]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=__lowerCAmelCase						,			model_name="""xlm-roberta-base"""						,			revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""						,			)




"""simple docstring"""






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__lowerCAmelCase       :			Tuple   	=8


def      UpperCAmelCase__			(							lowerCAmelCase__	:str   ,						lowerCAmelCase__	:int=BITS						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x * 2_5_5).int().clamp(0   ,						2_5_5						)

      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c h w -> b c 1 h w"""						)

      lowercase								=      ((x & mask) != 0).float()
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b c d h w -> b (c d) h w"""						)
      lowercase								=      bits * 2 - 1
      return bits


def      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:int=BITS						)					->       List[Any]:
      '''simple docstring'''


      lowercase								=      x.device

      lowercase								=      (x > 0).int()
      lowercase								=      2 ** torch.arange(bits - 1   ,						-1   ,						-1   ,						device=lowerCAmelCase__   ,						dtype=torch.intaa						)

      lowercase								=      rearrange(lowerCAmelCase__   ,						"""d -> d 1 1"""						)
      lowercase								=      rearrange(lowerCAmelCase__   ,						"""b (c d) h w -> b c d h w"""   ,						d=8						)
      lowercase								=      reduce(x * mask   ,						"""b c d h w -> b c h w"""   ,						"""sum"""						)
      return (dec / 2_5_5).clamp(0.0   ,						1.0						)


def      UpperCAmelCase__			(							self	:Union[str, Any]   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:float = 0.0   ,						lowerCAmelCase__	:bool = True   ,						lowerCAmelCase__	:Optional[Any]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDIMSchedulerOutput, Tuple]:
      '''simple docstring'''


      if self.num_inference_steps is None:
            raise ValueError(
                """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""						)

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      lowercase								=      timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      lowercase								=      self.alphas_cumprod[timestep]
      lowercase								=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      lowercase								=      1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      lowercase								=      self.bit_scale
      if self.config.clip_sample:
            lowercase								=      torch.clamp(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						)
      lowercase								=      eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            lowercase								=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      lowercase								=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      lowercase								=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            lowercase								=      model_output.device if torch.is_tensor(lowerCAmelCase__						) else """cpu"""
            lowercase								=      torch.randn(model_output.shape   ,						dtype=model_output.dtype   ,						generator=lowerCAmelCase__						).to(lowerCAmelCase__						)
            lowercase								=      self._get_variance(lowerCAmelCase__   ,						lowerCAmelCase__						) ** 0.5 * eta * noise

            lowercase								=      prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)


def      UpperCAmelCase__			(							self	:Any   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:torch.FloatTensor   ,						lowerCAmelCase__	:List[str]="epsilon"   ,						lowerCAmelCase__	:Optional[int]=None   ,						lowerCAmelCase__	:bool = True   ,						)					->       Union[DDPMSchedulerOutput, Tuple]:
      '''simple docstring'''


      lowercase								=      timestep

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

      # 1. compute alphas, betas
      lowercase								=      self.alphas_cumprod[t]
      lowercase								=      self.alphas_cumprod[t - 1] if t > 0 else self.one
      lowercase								=      1 - alpha_prod_t
      lowercase								=      1 - alpha_prod_t_prev

      # 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 prediction_type == "epsilon":
            lowercase								=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            lowercase								=      model_output
      else:
            raise ValueError(f'Unsupported prediction_type {prediction_type}.'						)

      # 3. Clip "predicted x_0"
      lowercase								=      self.bit_scale
      if self.config.clip_sample:
            lowercase								=      torch.clamp(lowerCAmelCase__   ,						-scale   ,						lowerCAmelCase__						)

      # 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
      lowercase								=      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      lowercase								=      self.alphas[t] ** 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
      lowercase								=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      lowercase								=      0
      if t > 0:
            lowercase								=      torch.randn(
                model_output.size()   ,						dtype=model_output.dtype   ,						layout=model_output.layout   ,						generator=lowerCAmelCase__						).to(model_output.device						)
            lowercase								=      (self._get_variance(lowerCAmelCase__   ,						predicted_variance=lowerCAmelCase__						) ** 0.5) * noise

      lowercase								=      pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=lowerCAmelCase__   ,						pred_original_sample=lowerCAmelCase__						)






class 					_A		(			lowerCAmelCase      ):

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 1.0						,			):







            """simple docstring"""



            super().__init__()
            lowercase								=      bit_scale
            lowercase								=      (
                ddim_bit_scheduler_step if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else ddpm_bit_scheduler_step
            )

            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 256						,			__lowerCAmelCase = 50						,			__lowerCAmelCase = None						,			__lowerCAmelCase = 1						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      torch.randn(
                (batch_size, self.unet.config.in_channels, height, width)						,			generator=__lowerCAmelCase						,			)
            lowercase								=      decimal_to_bits(__lowerCAmelCase		) * self.bit_scale
            lowercase								=      latents.to(self.device		)

            self.scheduler.set_timesteps(__lowerCAmelCase		)

            for t in self.progress_bar(self.scheduler.timesteps		):
                  # predict the noise residual
                  lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample

                  # compute the previous noisy sample x_t -> x_t-1
                  lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

            lowercase								=      bits_to_decimal(__lowerCAmelCase		)

            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



