



from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
  from transformers.models.albert.modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertModel,
  )

class      snake_case__       :







      """simple docstring"""




      def __init__(     self      ,     __lowercase      ,     __lowercase=1_3      ,     __lowercase=7      ,     __lowercase=True      ,     __lowercase=True      ,     __lowercase=True      ,     __lowercase=True      ,     __lowercase=9_9      ,     __lowercase=1_6      ,     __lowercase=3_2      ,     __lowercase=2      ,     __lowercase=4      ,     __lowercase=3_7      ,     __lowercase="gelu"      ,     __lowercase=0.1      ,     __lowercase=0.1      ,     __lowercase=5_1_2      ,     __lowercase=1_6      ,     __lowercase=2      ,     __lowercase=0.0_2      ,     __lowercase=3      ,     __lowercase=4      ,     __lowercase=None      ,     )      ->   int:
         """simple docstring"""




         a__     :		Tuple		 =						parent
         a__     :		Any		 =						1_3
         a__     :		int		 =						7
         a__     :		Dict		 =						True
         a__     :		Optional[Any]		 =						True
         a__     :		Tuple		 =						True
         a__     :		List[Any]		 =						True
         a__     :		List[str]		 =						9_9
         a__     :		int		 =						1_6
         a__     :		List[str]		 =						3_2
         a__     :		Optional[int]		 =						2
         a__     :		int		 =						4
         a__     :		Tuple		 =						3_7
         a__     :		Dict		 =						"""gelu"""
         a__     :		List[Any]		 =						0.1
         a__     :		List[str]		 =						0.1
         a__     :		List[str]		 =						5_1_2
         a__     :		Dict		 =						1_6
         a__     :		List[str]		 =						2
         a__     :		Any		 =						0.0_2
         a__     :		Dict		 =						3
         a__     :		List[Any]		 =						4
         a__     :		Dict		 =						None

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
         """simple docstring"""




         a__     :		Any		 =						ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size						)

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

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

         a__     :		List[Any]		 =						None
         a__     :		Tuple		 =						None
         a__     :		str		 =						None
         if self.use_labels:
            a__     :		Any		 =						ids_tensor([self.batch_size]      ,     self.type_sequence_label_size						)
            a__     :		Optional[Any]		 =						ids_tensor([self.batch_size, self.seq_length]      ,     self.num_labels						)
            a__     :		List[str]		 =						ids_tensor([self.batch_size]      ,     self.num_choices						)

         a__     :		Optional[int]		 =						AlbertConfig(
             vocab_size=self.vocab_size      ,     hidden_size=self.hidden_size      ,     embedding_size=self.embedding_size      ,     num_hidden_layers=self.num_hidden_layers      ,     num_attention_heads=self.num_attention_heads      ,     intermediate_size=self.intermediate_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      ,     initializer_range=self.initializer_range      ,     )

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

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   str:
         """simple docstring"""




         a__     :		int		 =						TFAlbertModel(config=__lowercase						)
         # inputs = {'input_ids': input_ids,
         #           'attention_mask': input_mask,
         #           'token_type_ids': token_type_ids}
         # sequence_output, pooled_output = model(**inputs)
         a__     :		Optional[int]		 =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         a__     :		int		 =						model(__lowercase						)

         a__     :		List[str]		 =						[input_ids, input_mask]
         a__     :		List[Any]		 =						model(__lowercase						)

         a__     :		Tuple		 =						model(__lowercase						)

         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 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   Union[str, Any]:
         """simple docstring"""




         a__     :		List[str]		 =						self.num_labels
         a__     :		Dict		 =						TFAlbertForPreTraining(config=__lowercase						)
         a__     :		Optional[Any]		 =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         a__     :		int		 =						model(__lowercase						)
         self.parent.assertEqual(result.prediction_logits.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)						)
         self.parent.assertEqual(result.sop_logits.shape      ,     (self.batch_size, self.num_labels)						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   List[Any]:
         """simple docstring"""




         a__     :		Union[str, Any]		 =						TFAlbertForMaskedLM(config=__lowercase						)
         a__     :		Tuple		 =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         a__     :		Tuple		 =						model(__lowercase						)
         self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.seq_length, self.vocab_size)						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   List[str]:
         """simple docstring"""




         a__     :		List[str]		 =						self.num_labels
         a__     :		List[str]		 =						TFAlbertForSequenceClassification(config=__lowercase						)
         a__     :		List[Any]		 =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         a__     :		List[str]		 =						model(__lowercase						)
         self.parent.assertEqual(result.logits.shape      ,     (self.batch_size, self.num_labels)						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   int:
         """simple docstring"""




         a__     :		List[Any]		 =						TFAlbertForQuestionAnswering(config=__lowercase						)
         a__     :		Optional[int]		 =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
         a__     :		Optional[Any]		 =						model(__lowercase						)
         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 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   List[Any]:
         """simple docstring"""




         a__     :		int		 =						self.num_choices
         a__     :		int		 =						TFAlbertForMultipleChoice(config=__lowercase						)
         a__     :		List[str]		 =						tf.tile(tf.expand_dims(__lowercase      ,     1						)      ,     (1, self.num_choices, 1)						)
         a__     :		Tuple		 =						tf.tile(tf.expand_dims(__lowercase      ,     1						)      ,     (1, self.num_choices, 1)						)
         a__     :		Optional[int]		 =						tf.tile(tf.expand_dims(__lowercase      ,     1						)      ,     (1, self.num_choices, 1)						)
         a__     :		str		 =						{
             """input_ids""": multiple_choice_inputs_ids,
             """attention_mask""": multiple_choice_input_mask,
             """token_type_ids""": multiple_choice_token_type_ids,
         }
         a__     :		List[str]		 =						model(__lowercase						)
         self.parent.assertListEqual(list(result["""logits"""].shape						)      ,     [self.batch_size, self.num_choices]						)

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   List[str]:
         """simple docstring"""




         a__     :		Optional[Any]		 =						self.num_labels
         a__     :		List[str]		 =						TFAlbertForTokenClassification(config=__lowercase						)
         a__     :		List[Any]		 =						{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         a__     :		List[str]		 =						model(__lowercase						)
         self.parent.assertListEqual(list(result["""logits"""].shape						)      ,     [self.batch_size, self.seq_length, self.num_labels]						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
         """simple docstring"""




         a__     :		Dict		 =						self.prepare_config_and_inputs()
         (
             a__		
         )     :		Optional[Any]		 =						config_and_inputs
         a__     :		Any		 =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict




@require_tf
class      snake_case__       (A__      ,			A__      ,			unittest.TestCase	):







      """simple docstring"""




      __lowerCAmelCase  :Any			      =  (
          (
              TFAlbertModel,
              TFAlbertForPreTraining,
              TFAlbertForMaskedLM,
              TFAlbertForSequenceClassification,
              TFAlbertForQuestionAnswering,
              TFAlbertForTokenClassification,
              TFAlbertForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      __lowerCAmelCase  :Optional[int]			      =  (
          {
              "feature-extraction": TFAlbertModel,
              "fill-mask": TFAlbertForMaskedLM,
              "question-answering": TFAlbertForQuestionAnswering,
              "text-classification": TFAlbertForSequenceClassification,
              "token-classification": TFAlbertForTokenClassification,
              "zero-shot": TFAlbertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      __lowerCAmelCase  :int			      =  False
      __lowerCAmelCase  :Optional[Any]			      =  False

      def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase=False						)      ->   List[Any]:
         """simple docstring"""




         a__     :		Any		 =						super()._prepare_for_class(__lowercase      ,     __lowercase      ,     return_labels=__lowercase						)

         if return_labels:
            if model_class in get_values(__lowercase						):
               a__     :		Any		 =						tf.zeros(self.model_tester.batch_size      ,     dtype=tf.intaa						)

         return inputs_dict

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         a__     :		List[str]		 =						TFAlbertModelTester(self						)
         a__     :		Tuple		 =						ConfigTester(self      ,     config_class=__lowercase      ,     hidden_size=3_7						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
         """simple docstring"""




         self.config_tester.run_common_tests()

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
         """simple docstring"""




         a__     :		List[Any]		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_model(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
         """simple docstring"""




         a__     :		Tuple		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_pretraining(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         a__     :		Dict		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_masked_lm(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Any:
         """simple docstring"""




         a__     :		List[Any]		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_multiple_choice(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         a__     :		Optional[Any]		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_sequence_classification(*__lowercase						)

      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
         """simple docstring"""




         a__     :		Tuple		 =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_albert_for_question_answering(*__lowercase						)

      @slow
      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            a__     :		str		 =						TFAlbertModel.from_pretrained(__lowercase						)
            self.assertIsNotNone(__lowercase						)




@require_tf
class      snake_case__       (unittest.TestCase	):







      """simple docstring"""




      @slow
      def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
         """simple docstring"""




         a__     :		Optional[int]		 =						TFAlbertForPreTraining.from_pretrained("""albert-base-v2"""						)
         a__     :		List[Any]		 =						tf.constant([[0, 1, 2, 3, 4, 5]]						)
         a__     :		Any		 =						model(__lowercase						)[0]

         a__     :		str		 =						[1, 6, 3_0_0_0_0]
         self.assertEqual(output.shape      ,     __lowercase						)

         a__     :		Tuple		 =						tf.constant(
             [
                 [
                     [4.5_9_5_6_6_8, 0.7_4_4_6_2_7_5_4, -1.8_1_8_1_4_7],
                     [4.5_9_5_4_3_4_7, 0.7_4_5_4_1_8_4, -1.8_1_8_8_2_5_8],
                     [4.5_9_5_4_9_0_5, 0.7_4_4_8_2_3_5, -1.8_1_8_2_3_1_6],
                 ]
             ]						)
         tf.debugging.assert_near(output[:, :3, :3]      ,     __lowercase      ,     atol=1E-4						)









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

import numpy as np
import torch

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


_lowercase	:   Tuple    	=logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :torch.FloatTensor




class      snake_case__       (A__      ,			A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[int]			      =  1

						@register_to_config
						def __init__(     self      ,     __lowercase = 4_0      ,     __lowercase = 0.0_0_2      ,     __lowercase = 8_0.0      ,     __lowercase = 0.5      ,     __lowercase = 1.0      ,     __lowercase = 7.0      ,     __lowercase = True      ,     )      ->   str:
									"""simple docstring"""




									a__     :		Dict		 =						sigma_max

									a__     :		int		 =						np.linspace(0      ,     1      ,     __lowercase						)
									a__     :		int		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									# setable values
									a__     :		Optional[int]		 =						None
									a__     :		Union[str, Any]		 =						torch.from_numpy(__lowercase						)
									a__     :		Tuple		 =						torch.from_numpy(__lowercase						)
									a__     :		Optional[Any]		 =						False
									a__     :		List[str]		 =						False

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase=None						)      ->   Union[str, Any]:
									"""simple docstring"""




									if schedule_timesteps is None:
												a__     :		int		 =						self.timesteps

									a__     :		Optional[int]		 =						(schedule_timesteps == timestep).nonzero()
									return indices.item()

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   torch.FloatTensor:
									"""simple docstring"""




									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		str		 =						timestep.to(self.timesteps.device						)
									a__     :		Optional[Any]		 =						self.index_for_timestep(__lowercase						)
									a__     :		Tuple		 =						self.sigmas[step_idx]

									a__     :		Optional[Any]		 =						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

									a__     :		List[str]		 =						True
									return sample

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Dict:
									"""simple docstring"""




									if not isinstance(__lowercase      ,     np.ndarray						):
												a__     :		Optional[Any]		 =						np.array(__lowercase      ,     dtype=np.floataa						)

									a__     :		str		 =						1_0_0_0 * 0.2_5 * np.log(sigmas + 1E-44						)

									return timesteps

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase = None      ,     __lowercase = None      ,     __lowercase = None      ,     )      ->   List[Any]:
									"""simple docstring"""




									if num_inference_steps is None and timesteps is None:
												raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""						)

									if num_inference_steps is not None and timesteps is not None:
												raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""						)

									# Follow DDPMScheduler custom timesteps logic
									if timesteps is not None:
												for i in range(1      ,     len(__lowercase						)						):
															if timesteps[i] >= timesteps[i - 1]:
																		raise ValueError("""`timesteps` must be in descending order."""						)

												if timesteps[0] >= self.config.num_train_timesteps:
															raise ValueError(
															    F'''`timesteps` must start before `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps}.'''						)

												a__     :		List[str]		 =						np.array(__lowercase      ,     dtype=np.intaa						)
												a__     :		str		 =						True
									else:
												if num_inference_steps > self.config.num_train_timesteps:
															raise ValueError(
															    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
															    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
															    F''' maximal {self.config.num_train_timesteps} timesteps.'''						)

												a__     :		List[str]		 =						num_inference_steps

												a__     :		str		 =						self.config.num_train_timesteps // self.num_inference_steps
												a__     :		int		 =						(np.arange(0      ,     __lowercase						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
												a__     :		Union[str, Any]		 =						False

									# Map timesteps to Karras sigmas directly for multistep sampling
									# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
									a__     :		int		 =						self.config.num_train_timesteps
									a__     :		Optional[Any]		 =						timesteps[::-1].copy()
									a__     :		Optional[Any]		 =						ramp / (num_train_timesteps - 1)
									a__     :		Union[str, Any]		 =						self._convert_to_karras(__lowercase						)
									a__     :		Any		 =						self.sigma_to_t(__lowercase						)

									a__     :		Dict		 =						np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
									a__     :		List[str]		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

									if str(__lowercase						).startswith("""mps"""						):
												# mps does not support float64
												a__     :		int		 =						torch.from_numpy(__lowercase						).to(__lowercase      ,     dtype=torch.floataa						)
									else:
												a__     :		int		 =						torch.from_numpy(__lowercase						).to(device=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		float		 =						self.config.sigma_min
									a__     :		float		 =						self.config.sigma_max

									a__     :		Optional[int]		 =						self.config.rho
									a__     :		List[str]		 =						sigma_min ** (1 / rho)
									a__     :		Any		 =						sigma_max ** (1 / rho)
									a__     :		Any		 =						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		int		 =						self.config.sigma_data

									a__     :		List[Any]		 =						sigma_data**2 / (sigma**2 + sigma_data**2)
									a__     :		Optional[Any]		 =						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int:
									"""simple docstring"""




									a__     :		str		 =						self.config.sigma_min
									a__     :		Dict		 =						self.config.sigma_data

									a__     :		Dict		 =						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
									a__     :		List[Any]		 =						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
									return c_skip, c_out

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     __lowercase = None      ,     __lowercase = True      ,     )      ->   Union[CMStochasticIterativeSchedulerOutput, Tuple]:
									"""simple docstring"""




									if (
									    isinstance(__lowercase      ,     __lowercase						)
									    or isinstance(__lowercase      ,     torch.IntTensor						)
									    or isinstance(__lowercase      ,     torch.LongTensor						)
									):
												raise ValueError(
												    (
												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
												        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
												        """ one of the `scheduler.timesteps` as a timestep."""
												    )      ,     )

									if not self.is_scale_input_called:
												logger.warning(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""						)

									if isinstance(__lowercase      ,     torch.Tensor						):
												a__     :		Optional[int]		 =						timestep.to(self.timesteps.device						)

									a__     :		Tuple		 =						self.config.sigma_min
									a__     :		Tuple		 =						self.config.sigma_max

									a__     :		Union[str, Any]		 =						self.index_for_timestep(__lowercase						)

									# sigma_next corresponds to next_t in original implementation
									a__     :		List[Any]		 =						self.sigmas[step_index]
									if step_index + 1 < self.config.num_train_timesteps:
												a__     :		int		 =						self.sigmas[step_index + 1]
									else:
												# Set sigma_next to sigma_min
												a__     :		Dict		 =						self.sigmas[-1]

									# Get scalings for boundary conditions
									a__ ,		a__     :		Any		 =						self.get_scalings_for_boundary_condition(__lowercase						)

									# 1. Denoise model output using boundary conditions
									a__     :		Optional[int]		 =						c_out * model_output + c_skip * sample
									if self.config.clip_denoised:
												a__     :		Dict		 =						denoised.clamp(-1      ,     1						)

									# 2. Sample z ~ N(0, s_noise^2 * I)
									# Noise is not used for onestep sampling.
									if len(self.timesteps						) > 1:
												a__     :		Optional[Any]		 =						randn_tensor(
												    model_output.shape      ,     dtype=model_output.dtype      ,     device=model_output.device      ,     generator=__lowercase						)
									else:
												a__     :		Any		 =						torch.zeros_like(__lowercase						)
									a__     :		Union[str, Any]		 =						noise * self.config.s_noise

									a__     :		Union[str, Any]		 =						sigma_next.clamp(min=__lowercase      ,     max=__lowercase						)

									# 3. Return noisy sample
									# tau = sigma_hat, eps = sigma_min
									a__     :		Union[str, Any]		 =						denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

									if not return_dict:
												return (prev_sample,)

									return CMStochasticIterativeSchedulerOutput(prev_sample=__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase      ,     )      ->   torch.FloatTensor:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						self.sigmas.to(device=original_samples.device      ,     dtype=original_samples.dtype						)
									if original_samples.device.type == "mps" and torch.is_floating_point(__lowercase						):
												# mps does not support float64
												a__     :		List[str]		 =						self.timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
												a__     :		Any		 =						timesteps.to(original_samples.device      ,     dtype=torch.floataa						)
									else:
												a__     :		str		 =						self.timesteps.to(original_samples.device						)
												a__     :		List[str]		 =						timesteps.to(original_samples.device						)

									a__     :		Optional[Any]		 =						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

									a__     :		Tuple		 =						sigmas[step_indices].flatten()
									while len(sigma.shape						) < len(original_samples.shape						):
												a__     :		List[Any]		 =						sigma.unsqueeze(-1						)

									a__     :		Optional[int]		 =						original_samples + noise * sigma
									return noisy_samples

						def __len__(     self						)      ->   int:
									"""simple docstring"""




									return self.config.num_train_timesteps




