"""simple docstring"""






import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_PRETRAINING_MAPPING,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
           MobileBertModel,
       )





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=13						,			__lowerCAmelCase=7						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=99						,			__lowerCAmelCase=64						,			__lowerCAmelCase=32						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=37						,			__lowerCAmelCase="gelu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=512						,			__lowerCAmelCase=16						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=3						,			__lowerCAmelCase=4						,			__lowerCAmelCase=None						,			):







            """simple docstring"""



            lowercase								=      parent
            lowercase								=      batch_size
            lowercase								=      seq_length
            lowercase								=      is_training
            lowercase								=      use_input_mask
            lowercase								=      use_token_type_ids
            lowercase								=      use_labels
            lowercase								=      vocab_size
            lowercase								=      hidden_size
            lowercase								=      embedding_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      max_position_embeddings
            lowercase								=      type_vocab_size
            lowercase								=      type_sequence_label_size
            lowercase								=      initializer_range
            lowercase								=      num_labels
            lowercase								=      num_choices
            lowercase								=      scope

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size		)

            lowercase								=      None
            if self.use_input_mask:
                  lowercase								=      random_attention_mask([self.batch_size, self.seq_length]		)

            lowercase								=      None
            if self.use_token_type_ids:
                  lowercase								=      ids_tensor([self.batch_size, self.seq_length]						,			self.type_vocab_size		)

            lowercase								=      None
            lowercase								=      None
            lowercase								=      None
            if self.use_labels:
                  lowercase								=      ids_tensor([self.batch_size]						,			self.type_sequence_label_size		)
                  lowercase								=      ids_tensor([self.batch_size, self.seq_length]						,			self.num_labels		)
                  lowercase								=      ids_tensor([self.batch_size]						,			self.num_choices		)

            lowercase								=      self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

      def 							A__			(		self		):







            """simple docstring"""



            return MobileBertConfig(
                vocab_size=self.vocab_size						,			hidden_size=self.hidden_size						,			num_hidden_layers=self.num_hidden_layers						,			num_attention_heads=self.num_attention_heads						,			intermediate_size=self.intermediate_size						,			embedding_size=self.embedding_size						,			hidden_act=self.hidden_act						,			hidden_dropout_prob=self.hidden_dropout_prob						,			attention_probs_dropout_prob=self.attention_probs_dropout_prob						,			max_position_embeddings=self.max_position_embeddings						,			type_vocab_size=self.type_vocab_size						,			is_decoder=__lowerCAmelCase						,			initializer_range=self.initializer_range						,			)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MobileBertModel(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase		)
            lowercase								=      model(__lowerCAmelCase		)

            self.parent.assertEqual(result.last_hidden_state.shape						,			(self.batch_size, self.seq_length, self.hidden_size)		)
            self.parent.assertEqual(result.pooler_output.shape						,			(self.batch_size, self.hidden_size)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MobileBertForMaskedLM(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MobileBertForNextSentencePrediction(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(
                __lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			labels=__lowerCAmelCase						,			)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, 2)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MobileBertForPreTraining(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(
                __lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			labels=__lowerCAmelCase						,			next_sentence_label=__lowerCAmelCase						,			)
            self.parent.assertEqual(result.prediction_logits.shape						,			(self.batch_size, self.seq_length, self.vocab_size)		)
            self.parent.assertEqual(result.seq_relationship_logits.shape						,			(self.batch_size, 2)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MobileBertForQuestionAnswering(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(
                __lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			start_positions=__lowerCAmelCase						,			end_positions=__lowerCAmelCase						,			)
            self.parent.assertEqual(result.start_logits.shape						,			(self.batch_size, self.seq_length)		)
            self.parent.assertEqual(result.end_logits.shape						,			(self.batch_size, self.seq_length)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.num_labels
            lowercase								=      MobileBertForSequenceClassification(__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_labels)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.num_labels
            lowercase								=      MobileBertForTokenClassification(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			labels=__lowerCAmelCase		)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.seq_length, self.num_labels)		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.num_choices
            lowercase								=      MobileBertForMultipleChoice(config=__lowerCAmelCase		)
            model.to(__lowerCAmelCase		)
            model.eval()
            lowercase								=      input_ids.unsqueeze(1		).expand(-1						,			self.num_choices						,			-1		).contiguous()
            lowercase								=      token_type_ids.unsqueeze(1		).expand(-1						,			self.num_choices						,			-1		).contiguous()
            lowercase								=      input_mask.unsqueeze(1		).expand(-1						,			self.num_choices						,			-1		).contiguous()
            lowercase								=      model(
                __lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			token_type_ids=__lowerCAmelCase						,			labels=__lowerCAmelCase						,			)
            self.parent.assertEqual(result.logits.shape						,			(self.batch_size, self.num_choices)		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.prepare_config_and_inputs()
            (
                (
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				(
                lowercase
            )				,				
            )								=      config_and_inputs
            lowercase								=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
            return config, inputs_dict






@require_torch
class 					_A		(			lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  str				    =      (
          (
              MobileBertModel,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      snake_case__     :  Optional[Any]				    =      (
          {
              'feature-extraction': MobileBertModel,
              'fill-mask': MobileBertForMaskedLM,
              'question-answering': MobileBertForQuestionAnswering,
              'text-classification': MobileBertForSequenceClassification,
              'token-classification': MobileBertForTokenClassification,
              'zero-shot': MobileBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__     :  List[Any]				    =      True

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







            """simple docstring"""



            lowercase								=      super()._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase						,			return_labels=__lowerCAmelCase		)

            if return_labels:
                  if model_class in get_values(__lowerCAmelCase		):
                        lowercase								=      torch.zeros(
                            (self.model_tester.batch_size, self.model_tester.seq_length)						,			dtype=torch.long						,			device=__lowerCAmelCase		)
                        lowercase								=      torch.zeros(
                            self.model_tester.batch_size						,			dtype=torch.long						,			device=__lowerCAmelCase		)
            return inputs_dict

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MobileBertModelTester(self		)
            lowercase								=      ConfigTester(self						,			config_class=__lowerCAmelCase						,			hidden_size=37		)

      def 							A__			(		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_model(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCAmelCase		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCAmelCase		)






def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       int:
      '''simple docstring'''


      return torch.tensor(
          lowerCAmelCase__   ,						dtype=torch.long   ,						device=lowerCAmelCase__   ,						)


__lowerCAmelCase       :			Optional[Any]   	=1E-3






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

      @slow
      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MobileBertModel.from_pretrained("""google/mobilebert-uncased"""		).to(__lowerCAmelCase		)
            lowercase								=      _long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]		)
            with torch.no_grad():
                  lowercase								=      model(__lowerCAmelCase		)[0]
            lowercase								=      torch.Size((1, 9, 512)		)
            self.assertEqual(output.shape						,			__lowerCAmelCase		)
            lowercase								=      torch.tensor(
                [
                    [
                        [-2.4_736_526E07, 8.2_691_656E04, 1.6_521_838E05],
                        [-5.7_541_704E-01, 3.9_056_022E00, 4.4_011_507E00],
                        [2.6_047_359E00, 1.5_677_652E00, -1.7_324_188E-01],
                    ]
                ]						,			device=__lowerCAmelCase						,			)

            # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
            # ~1 difference, it's therefore not a good idea to measure using addition.
            # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
            # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
            lowercase								=      torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE		)
            lowercase								=      torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE		)

            self.assertTrue(lower_bound and upper_bound		)




"""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		)



