






import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    LevitForImageClassification,
					    LevitForImageClassificationWithTeacher,
					    LevitModel,
					)
					from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import LevitImageProcessor
class 						lowerCamelCase_				(      lowerCamelCase ):







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[str]        =      self.config_class(**self.inputs_dict     )
								self.parent.assertTrue(hasattr(__lowerCAmelCase       ,					'''hidden_sizes'''     )     )
								self.parent.assertTrue(hasattr(__lowerCAmelCase       ,					'''num_attention_heads'''     )     )







class 						lowerCamelCase_				:







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase=1_3       ,					__lowerCAmelCase=6_4       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=3       ,					__lowerCAmelCase=2       ,					__lowerCAmelCase=1       ,					__lowerCAmelCase=1_6       ,					__lowerCAmelCase=[1_2_8, 2_5_6, 3_8_4]       ,					__lowerCAmelCase=[4, 6, 8]       ,					__lowerCAmelCase=[2, 3, 4]       ,					__lowerCAmelCase=[1_6, 1_6, 1_6]       ,					__lowerCAmelCase=0       ,					__lowerCAmelCase=[2, 2, 2]       ,					__lowerCAmelCase=[2, 2, 2]       ,					__lowerCAmelCase=0.02       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=True       ,					__lowerCAmelCase=2       ,					):
								"""simple docstring"""
								__magic_name__		:str        =      parent
								__magic_name__		:Optional[Any]        =      batch_size
								__magic_name__		:Union[str, Any]        =      image_size
								__magic_name__		:int        =      num_channels
								__magic_name__		:int        =      kernel_size
								__magic_name__		:List[str]        =      stride
								__magic_name__		:List[str]        =      padding
								__magic_name__		:List[str]        =      hidden_sizes
								__magic_name__		:List[Any]        =      num_attention_heads
								__magic_name__		:Tuple        =      depths
								__magic_name__		:List[str]        =      key_dim
								__magic_name__		:Optional[Any]        =      drop_path_rate
								__magic_name__		:Dict        =      patch_size
								__magic_name__		:Union[str, Any]        =      attention_ratio
								__magic_name__		:Union[str, Any]        =      mlp_ratio
								__magic_name__		:Tuple        =      initializer_range
								__magic_name__		:Optional[int]        =      [
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]
								__magic_name__		:Optional[Any]        =      is_training
								__magic_name__		:Dict        =      use_labels
								__magic_name__		:Union[str, Any]        =      num_labels
								__magic_name__		:Union[str, Any]        =      initializer_range







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Any        =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

								__magic_name__		:int        =      None
								if self.use_labels:
												__magic_name__		:int        =      ids_tensor([self.batch_size]       ,					self.num_labels     )

								__magic_name__		:Dict        =      self.get_config()
								return config, pixel_values, labels







				def 			A	(						self     ):
								"""simple docstring"""
								return LevitConfig(
								    image_size=self.image_size       ,					num_channels=self.num_channels       ,					kernel_size=self.kernel_size       ,					stride=self.stride       ,					padding=self.padding       ,					patch_size=self.patch_size       ,					hidden_sizes=self.hidden_sizes       ,					num_attention_heads=self.num_attention_heads       ,					depths=self.depths       ,					key_dim=self.key_dim       ,					drop_path_rate=self.drop_path_rate       ,					mlp_ratio=self.mlp_ratio       ,					attention_ratio=self.attention_ratio       ,					initializer_range=self.initializer_range       ,					down_ops=self.down_ops       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      LevitModel(config=__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:Union[str, Any]        =      model(__lowerCAmelCase     )
								__magic_name__		:List[Any]        =      (self.image_size, self.image_size)
								__magic_name__     ,				__magic_name__		:Union[str, Any]        =      image_size[0], image_size[1]
								for _ in range(4     ):
												__magic_name__		:Optional[int]        =      floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1     )
												__magic_name__		:Dict        =      floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1     )
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,					(self.batch_size, ceil(height / 4     ) * ceil(width / 4     ), self.hidden_sizes[-1])       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.num_labels
								__magic_name__		:List[Any]        =      LevitForImageClassification(__lowerCAmelCase     )
								model.to(__lowerCAmelCase     )
								model.eval()
								__magic_name__		:List[Any]        =      model(__lowerCAmelCase       ,					labels=__lowerCAmelCase     )
								self.parent.assertEqual(result.logits.shape       ,					(self.batch_size, self.num_labels)     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:str        =      self.prepare_config_and_inputs()
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:Optional[Any]        =      config_and_inputs
								__magic_name__		:Optional[Any]        =      {'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase			,				unittest.TestCase ):
				a__					       =  (
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				a__					       =  (
				    {
				        '''feature-extraction''': LevitModel,
				        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False
				a__					       =  False







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      LevitModelTester(self     )
								__magic_name__		:Any        =      ConfigTester(self       ,					config_class=__lowerCAmelCase       ,					has_text_modality=__lowerCAmelCase       ,					hidden_size=3_7     )







				def 			A	(						self     ):
								"""simple docstring"""
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()







				def 			A	(						self     ):
								"""simple docstring"""
								return







				@unittest.skip(reason='''Levit does not use inputs_embeds'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				@unittest.skip(reason='''Levit does not support input and output embeddings'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				@unittest.skip(reason='''Levit does not output attentions'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:str        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:Any        =      model_class(__lowerCAmelCase     )
												__magic_name__		:int        =      inspect.signature(model.forward     )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												__magic_name__		:List[str]        =      [*signature.parameters.keys()]

												__magic_name__		:int        =      ['''pixel_values''']
												self.assertListEqual(arg_names[:1]       ,					__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								def check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
												__magic_name__		:Optional[int]        =      model_class(__lowerCAmelCase     )
												model.to(__lowerCAmelCase     )
												model.eval()

												with torch.no_grad():
																__magic_name__		:Dict        =      model(**self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase     )     )

												__magic_name__		:str        =      outputs.hidden_states

												__magic_name__		:Optional[Any]        =      len(self.model_tester.depths     ) + 1
												self.assertEqual(len(__lowerCAmelCase     )       ,					__lowerCAmelCase     )

												__magic_name__		:str        =      (self.model_tester.image_size, self.model_tester.image_size)
												__magic_name__     ,				__magic_name__		:Tuple        =      image_size[0], image_size[1]
												for _ in range(4     ):
																__magic_name__		:str        =      floor(
																    (
																        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1     )
																__magic_name__		:Optional[Any]        =      floor(
																    (
																        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																        / self.model_tester.stride
																    )
																    + 1     )
												# verify the first hidden states (first block)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]     )       ,					[
												        height * width,
												        self.model_tester.hidden_sizes[0],
												    ]       ,					)

								__magic_name__     ,				__magic_name__		:Union[str, Any]        =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												__magic_name__		:int        =      True
												check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												__magic_name__		:List[Any]        =      True

												check_hidden_states_output(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
				def 			A	(						self     ):
								"""simple docstring"""
								pass







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=False     ):
								"""simple docstring"""
								__magic_name__		:Dict        =      super()._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase       ,					return_labels=__lowerCAmelCase     )

								if return_labels:
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																del inputs_dict["labels"]

								return inputs_dict







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase     )







				def 			A	(						self     ):
								"""simple docstring"""
								if not self.model_tester.is_training:
												return

								__magic_name__     ,				__magic_name__		:Tuple        =      self.model_tester.prepare_config_and_inputs_for_common()
								__magic_name__		:List[Any]        =      True

								for model_class in self.all_model_classes:
												# LevitForImageClassificationWithTeacher supports inference-only
												if (
												    model_class in get_values(__lowerCAmelCase     )
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
																continue
												__magic_name__		:str        =      model_class(__lowerCAmelCase     )
												model.to(__lowerCAmelCase     )
												model.train()
												__magic_name__		:Optional[int]        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase       ,					return_labels=__lowerCAmelCase     )
												__magic_name__		:Dict        =      model(**__lowerCAmelCase     ).loss
												loss.backward()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Dict        =      self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
												return

								__magic_name__		:Optional[Any]        =      False
								__magic_name__		:Dict        =      True

								for model_class in self.all_model_classes:
												if model_class in get_values(__lowerCAmelCase     ) or not model_class.supports_gradient_checkpointing:
																continue
												# LevitForImageClassificationWithTeacher supports inference-only
												if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																continue
												__magic_name__		:List[str]        =      model_class(__lowerCAmelCase     )
												model.gradient_checkpointing_enable()
												model.to(__lowerCAmelCase     )
												model.train()
												__magic_name__		:str        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase       ,					return_labels=__lowerCAmelCase     )
												__magic_name__		:int        =      model(**__lowerCAmelCase     ).loss
												loss.backward()







				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:Tuple        =      self.model_tester.prepare_config_and_inputs_for_common()

								__magic_name__		:Any        =      [
								    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
								    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
								    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
								]

								for model_class in self.all_model_classes:
												if (
												    model_class
												    not in [
												        *get_values(__lowerCAmelCase     ),
												    ]
												    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
												):
																continue

												for problem_type in problem_types:
																with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''     ):
																				__magic_name__		:Tuple        =      problem_type['''title''']
																				__magic_name__		:int        =      problem_type['''num_labels''']

																				__magic_name__		:Union[str, Any]        =      model_class(__lowerCAmelCase     )
																				model.to(__lowerCAmelCase     )
																				model.train()

																				__magic_name__		:str        =      self._prepare_for_class(__lowerCAmelCase       ,					__lowerCAmelCase       ,					return_labels=__lowerCAmelCase     )

																				if problem_type["num_labels"] > 1:
																								__magic_name__		:List[str]        =      inputs['''labels'''].unsqueeze(1     ).repeat(1       ,					problem_type['''num_labels''']     )

																				__magic_name__		:Dict        =      inputs['''labels'''].to(problem_type['''dtype''']     )

																				# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																				# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																				# they have the same size." which is a symptom something in wrong for the regression problem.
																				# See https://github.com/huggingface/transformers/issues/11780
																				with warnings.catch_warnings(record=__lowerCAmelCase     ) as warning_list:
																								__magic_name__		:str        =      model(**__lowerCAmelCase     ).loss
																				for w in warning_list:
																								if "Using a target size that is different to the input size" in str(w.message     ):
																												raise ValueError(
																												    F'''Something is going wrong in the regression problem: intercepted {w.message}'''     )

																				loss.backward()







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												__magic_name__		:str        =      LevitModel.from_pretrained(__lowerCAmelCase     )
												self.assertIsNotNone(__lowerCAmelCase     )







def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:str        =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image

@require_torch
@require_vision
class 						lowerCamelCase_				(      unittest.TestCase ):







				@cached_property
				def 			A	(						self     ):
								"""simple docstring"""
								return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )







				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								__magic_name__		:int        =      LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(
								    __lowerCAmelCase     )

								__magic_name__		:Dict        =      self.default_image_processor
								__magic_name__		:int        =      prepare_img()
								__magic_name__		:Union[str, Any]        =      image_processor(images=__lowerCAmelCase       ,					return_tensors='''pt'''     ).to(__lowerCAmelCase     )

								# forward pass
								with torch.no_grad():
												__magic_name__		:Optional[Any]        =      model(**__lowerCAmelCase     )

								# verify the logits
								__magic_name__		:Optional[Any]        =      torch.Size((1, 1_0_0_0)     )
								self.assertEqual(outputs.logits.shape       ,					__lowerCAmelCase     )

								__magic_name__		:str        =      torch.tensor([1.0448, -0.3745, -1.8317]     ).to(__lowerCAmelCase     )

								self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,					__lowerCAmelCase       ,					atol=1E-4     )     )














# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 						lowerCamelCase_				:
				a__					       =  42

				# setable values
				a__					       =  42
				a__					       =  42
				a__					       =  None







				@classmethod
				def 			A	(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return cls(common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase     )







@dataclass
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  42







class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  [e.name for e in FlaxKarrasDiffusionSchedulers]

				a__					       =  42







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return True







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase = 1_0_0_0       ,					__lowerCAmelCase = 0.0001       ,					__lowerCAmelCase = 0.02       ,					__lowerCAmelCase = "linear"       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "fixed_small"       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = "epsilon"       ,					__lowerCAmelCase = jnp.floataa       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      dtype







				def 			A	(						self       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								if common is None:
												__magic_name__		:Dict        =      CommonSchedulerState.create(self     )

								# standard deviation of the initial noise distribution
								__magic_name__		:Optional[Any]        =      jnp.array(1.0       ,					dtype=self.dtype     )

								__magic_name__		:str        =      jnp.arange(0       ,					self.config.num_train_timesteps     ).round()[::-1]

								return DDPMSchedulerState.create(
								    common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								return sample







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = ()     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.config.num_train_timesteps // num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# rounding to avoid issues when num_inference_step is power of 3
								__magic_name__		:List[Any]        =      (jnp.arange(0       ,					__lowerCAmelCase     ) * step_ratio).round()[::-1]

								return state.replace(
								    num_inference_steps=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      state.common.alphas_cumprod[t]
								__magic_name__		:Optional[Any]        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								__magic_name__		:Tuple        =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
												__magic_name__		:Optional[Any]        =      self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small":
												__magic_name__		:Optional[Any]        =      jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )
								# for rl-diffuser https://arxiv.org/abs/2205.09991
								elif variance_type == "fixed_small_log":
												__magic_name__		:Dict        =      jnp.log(jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )     )
								elif variance_type == "fixed_large":
												__magic_name__		:Tuple        =      state.common.betas[t]
								elif variance_type == "fixed_large_log":
												# Glide max_log
												__magic_name__		:Optional[Any]        =      jnp.log(state.common.betas[t]     )
								elif variance_type == "learned":
												return predicted_variance
								elif variance_type == "learned_range":
												__magic_name__		:Union[str, Any]        =      variance
												__magic_name__		:List[str]        =      state.common.betas[t]
												__magic_name__		:Any        =      (predicted_variance + 1) / 2
												__magic_name__		:str        =      frac * max_log + (1 - frac) * min_log

								return variance







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      timestep

								if key is None:
												__magic_name__		:Union[str, Any]        =      jax.random.PRNGKey(0     )

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
												__magic_name__     ,				__magic_name__		:Dict        =      jnp.split(__lowerCAmelCase       ,					sample.shape[1]       ,					axis=1     )
								else:
												__magic_name__		:Optional[int]        =      None

								# 1. compute alphas, betas
								__magic_name__		:Any        =      state.common.alphas_cumprod[t]
								__magic_name__		:int        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )
								__magic_name__		:Optional[int]        =      1 - alpha_prod_t
								__magic_name__		:Union[str, Any]        =      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 self.config.prediction_type == "epsilon":
												__magic_name__		:List[Any]        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
												__magic_name__		:Tuple        =      model_output
								elif self.config.prediction_type == "v_prediction":
												__magic_name__		:Dict        =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								else:
												raise ValueError(
												    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
												    ''' for the FlaxDDPMScheduler.'''     )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												__magic_name__		:Union[str, Any]        =      jnp.clip(__lowerCAmelCase       ,					-1       ,					1     )

								# 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
								__magic_name__		:Union[str, Any]        =      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								__magic_name__		:Union[str, Any]        =      state.common.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
								__magic_name__		:Union[str, Any]        =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								def random_variance():
												__magic_name__		:Tuple        =      jax.random.split(__lowerCAmelCase       ,					num=1     )
												__magic_name__		:Dict        =      jax.random.normal(__lowerCAmelCase       ,					shape=model_output.shape       ,					dtype=self.dtype     )
												return (self._get_variance(__lowerCAmelCase       ,					__lowerCAmelCase       ,					predicted_variance=__lowerCAmelCase     ) ** 0.5) * noise

								__magic_name__		:List[str]        =      jnp.where(t > 0       ,					random_variance()       ,					jnp.zeros(model_output.shape       ,					dtype=self.dtype     )     )

								__magic_name__		:int        =      pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase       ,					state=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return add_noise_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return get_velocity_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps






