"""simple docstring"""






import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
       __lowerCAmelCase       :			int   	="""pt"""
elif is_tf_available():
       __lowerCAmelCase       :			Optional[Any]   	="""tf"""
else:
       __lowerCAmelCase       :			int   	="""jax"""





class 					_A		(			lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  int				    =      PerceiverTokenizer
      snake_case__     :  Any				    =      False

      def 							A__			(		self		):







            """simple docstring"""



            super().setUp()
            lowercase								=      PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname		)

      @cached_property
      def 							A__			(		self		):







            """simple docstring"""



            return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""		)

      def 							A__			(		self						,			**__lowerCAmelCase		):







            """simple docstring"""



            return self.tokenizer_class.from_pretrained(self.tmpdirname						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=False						,			__lowerCAmelCase=20						,			__lowerCAmelCase=5		):







            """simple docstring"""



            lowercase								=      []
            for i in range(len(__lowerCAmelCase		)		):
                  try:
                        lowercase								=      tokenizer.decode([i]						,			clean_up_tokenization_spaces=__lowerCAmelCase		)
                  except UnicodeDecodeError:
                        pass
                  toks.append((i, tok)		)

            lowercase								=      list(filter(lambda __lowerCAmelCase		: re.match(r"""^[ a-zA-Z]+$"""						,			t[1]		)						,			__lowerCAmelCase		)		)
            lowercase								=      list(filter(lambda __lowerCAmelCase		: [t[0]] == tokenizer.encode(t[1]						,			add_special_tokens=__lowerCAmelCase		)						,			__lowerCAmelCase		)		)
            if max_length is not None and len(__lowerCAmelCase		) > max_length:
                  lowercase								=      toks[:max_length]
            if min_length is not None and len(__lowerCAmelCase		) < min_length and len(__lowerCAmelCase		) > 0:
                  while len(__lowerCAmelCase		) < min_length:
                        lowercase								=      toks + toks
        # toks_str = [t[1] for t in toks]
            lowercase								=      [t[0] for t in toks]

            # Ensure consistency
            lowercase								=      tokenizer.decode(__lowerCAmelCase						,			clean_up_tokenization_spaces=__lowerCAmelCase		)
            if " " not in output_txt and len(__lowerCAmelCase		) > 1:
                  lowercase								=      (
                      tokenizer.decode([toks_ids[0]]						,			clean_up_tokenization_spaces=__lowerCAmelCase		)
                      + """ """
                      + tokenizer.decode(toks_ids[1:]						,			clean_up_tokenization_spaces=__lowerCAmelCase		)
                  )
            if with_prefix_space:
                  lowercase								=      """ """ + output_txt
            lowercase								=      tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
            return output_txt, output_ids

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.perceiver_tokenizer
            lowercase								=      """Unicode €."""
            lowercase								=      tokenizer(__lowerCAmelCase		)
            lowercase								=      [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
            self.assertEqual(encoded["""input_ids"""]						,			__lowerCAmelCase		)

            # decoding
            lowercase								=      tokenizer.decode(__lowerCAmelCase		)
            self.assertEqual(__lowerCAmelCase						,			"""[CLS]Unicode €.[SEP]"""		)

            lowercase								=      tokenizer("""e è é ê ë"""		)
            lowercase								=      [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
            self.assertEqual(encoded["""input_ids"""]						,			__lowerCAmelCase		)
            # decoding
            lowercase								=      tokenizer.decode(__lowerCAmelCase		)
            self.assertEqual(__lowerCAmelCase						,			"""[CLS]e è é ê ë[SEP]"""		)

            # encode/decode, but with `encode` instead of `__call__`
            self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""		)		)						,			"""[CLS]e è é ê ë[SEP]"""		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.perceiver_tokenizer
            lowercase								=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            # fmt: off
            lowercase								=      [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
            # fmt: on
            lowercase								=      tokenizer(__lowerCAmelCase						,			padding=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase		)
            self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)

            if FRAMEWORK != "jax":
                  lowercase								=      list(batch.input_ids.numpy()[0]		)
            else:
                  lowercase								=      list(batch.input_ids.tolist()[0]		)

            self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

            self.assertEqual((2, 38)						,			batch.input_ids.shape		)
            self.assertEqual((2, 38)						,			batch.attention_mask.shape		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.perceiver_tokenizer
            lowercase								=      ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
            lowercase								=      tokenizer(__lowerCAmelCase						,			padding=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase		)
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn("""input_ids"""						,			__lowerCAmelCase		)
            self.assertIn("""attention_mask"""						,			__lowerCAmelCase		)
            self.assertNotIn("""decoder_input_ids"""						,			__lowerCAmelCase		)
            self.assertNotIn("""decoder_attention_mask"""						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.perceiver_tokenizer
            lowercase								=      [
                """Summary of the text.""",
                """Another summary.""",
            ]
            lowercase								=      tokenizer(
                text_target=__lowerCAmelCase						,			max_length=32						,			padding="""max_length"""						,			truncation=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase		)
            self.assertEqual(32						,			targets["""input_ids"""].shape[1]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'		):
                        self.assertNotEqual(tokenizer.model_max_length						,			42		)

        # Now let's start the test
            lowercase								=      self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'		):
                        # Isolate this from the other tests because we save additional tokens/etc
                        lowercase								=      tempfile.mkdtemp()

                        lowercase								=      """ He is very happy, UNwant\u00E9d,running"""
                        lowercase								=      tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
                        tokenizer.save_pretrained(__lowerCAmelCase		)

                        lowercase								=      tokenizer.__class__.from_pretrained(__lowerCAmelCase		)
                        lowercase								=      after_tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
                        self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

                        shutil.rmtree(__lowerCAmelCase		)

            lowercase								=      self.get_tokenizers(model_max_length=42		)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'		):
                        # Isolate this from the other tests because we save additional tokens/etc
                        lowercase								=      tempfile.mkdtemp()

                        lowercase								=      """ He is very happy, UNwant\u00E9d,running"""
                        tokenizer.add_tokens(["""bim""", """bambam"""]		)
                        lowercase								=      tokenizer.additional_special_tokens
                        additional_special_tokens.append("""new_additional_special_token"""		)
                        tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}		)
                        lowercase								=      tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
                        tokenizer.save_pretrained(__lowerCAmelCase		)

                        lowercase								=      tokenizer.__class__.from_pretrained(__lowerCAmelCase		)
                        lowercase								=      after_tokenizer.encode(__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase		)
                        self.assertListEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                        self.assertIn("""new_additional_special_token"""						,			after_tokenizer.additional_special_tokens		)
                        self.assertEqual(after_tokenizer.model_max_length						,			42		)

                        lowercase								=      tokenizer.__class__.from_pretrained(__lowerCAmelCase						,			model_max_length=43		)
                        self.assertEqual(tokenizer.model_max_length						,			43		)

                        shutil.rmtree(__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      []
            if self.test_slow_tokenizer:
                  tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

            if self.test_rust_tokenizer:
                  tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

            for tokenizer_class, tokenizer_utils in tokenizer_list:
                  with tempfile.TemporaryDirectory() as tmp_dir:
                        tokenizer_utils.save_pretrained(__lowerCAmelCase		)

                        with open(os.path.join(__lowerCAmelCase						,			"""special_tokens_map.json"""		)						,			encoding="""utf-8"""		) as json_file:
                              lowercase								=      json.load(__lowerCAmelCase		)

                        with open(os.path.join(__lowerCAmelCase						,			"""tokenizer_config.json"""		)						,			encoding="""utf-8"""		) as json_file:
                              lowercase								=      json.load(__lowerCAmelCase		)

                        lowercase								=      [f'<extra_id_{i}>' for i in range(125		)]

                        lowercase								=      added_tokens_extra_ids + [
                            """an_additional_special_token"""
                        ]
                        lowercase								=      added_tokens_extra_ids + [
                            """an_additional_special_token"""
                        ]

                        with open(os.path.join(__lowerCAmelCase						,			"""special_tokens_map.json"""		)						,			"""w"""						,			encoding="""utf-8"""		) as outfile:
                              json.dump(__lowerCAmelCase						,			__lowerCAmelCase		)
                        with open(os.path.join(__lowerCAmelCase						,			"""tokenizer_config.json"""		)						,			"""w"""						,			encoding="""utf-8"""		) as outfile:
                              json.dump(__lowerCAmelCase						,			__lowerCAmelCase		)

                        # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                        # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                        # "special_tokens_map.json" files
                        lowercase								=      tokenizer_class.from_pretrained(
                            __lowerCAmelCase						,			)
                        self.assertIn(
                            """an_additional_special_token"""						,			tokenizer_without_change_in_init.additional_special_tokens		)
                        self.assertEqual(
                            ["""an_additional_special_token"""]						,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                                tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]		)		)						,			)

                        # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                        lowercase								=      added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""						,			lstrip=__lowerCAmelCase		)]
                        lowercase								=      tokenizer_class.from_pretrained(
                            __lowerCAmelCase						,			additional_special_tokens=__lowerCAmelCase						,			)

                        self.assertIn("""a_new_additional_special_token"""						,			tokenizer.additional_special_tokens		)
                        self.assertEqual(
                            ["""a_new_additional_special_token"""]						,			tokenizer.convert_ids_to_tokens(
                                tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]		)		)						,			)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([178]		)						,			"""�"""		)

      def 							A__			(		self		):







            """simple docstring"""



            pass

      def 							A__			(		self		):







            """simple docstring"""



            pass

      def 							A__			(		self		):







            """simple docstring"""



            pass

      def 							A__			(		self		):







            """simple docstring"""



            pass







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.get_tokenizers(fast=__lowerCAmelCase						,			do_lower_case=__lowerCAmelCase		)
            for tokenizer in tokenizers:
                  with self.subTest(f'{tokenizer.__class__.__name__}'		):
                        lowercase								=      ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                        lowercase								=      tokenizer.convert_tokens_to_string(__lowerCAmelCase		)

                        self.assertIsInstance(__lowerCAmelCase						,			__lowerCAmelCase		)




"""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		)



