'''simple docstring'''




from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


__snake_case  : Optional[Any]		       =    logging.get_logger(__name__)

# General docstring
__snake_case  : List[Any]		       =    'ResNetConfig'

# Base docstring
__snake_case  : Union[str, Any]		       =    'microsoft/resnet-50'
__snake_case  : Optional[int]		       =    [1, 2_048, 7, 7]

# Image classification docstring
__snake_case  : Optional[Any]		       =    'microsoft/resnet-50'
__snake_case  : List[Any]		       =    'tiger cat'

__snake_case  : int		       =    [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = 3       ,	_SCREAMING_SNAKE_CASE = 1       ,	_SCREAMING_SNAKE_CASE = "relu"       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           A_      =  kernel_size // 2
           A_      =  tf.keras.layers.ConvaD(
               _SCREAMING_SNAKE_CASE       ,	kernel_size=_SCREAMING_SNAKE_CASE       ,	strides=_SCREAMING_SNAKE_CASE       ,	padding='''valid'''       ,	use_bias=_SCREAMING_SNAKE_CASE       ,	name='''convolution'''					)
           # Use same default momentum and epsilon as PyTorch equivalent
           A_      =  tf.keras.layers.BatchNormalization(epsilon=1E-5       ,	momentum=0.9       ,	name='''normalization'''					)
           A_      =  ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	tf.Tensor:
           # Pad to match that done in the PyTorch Conv2D model
           A_      =  A_      =  (self.pad_value, self.pad_value)
           A_      =  tf.pad(_SCREAMING_SNAKE_CASE       ,	[(0, 0), height_pad, width_pad, (0, 0)]					)
           A_      =  self.conv(_SCREAMING_SNAKE_CASE					)
           return hidden_state

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False					)						->	tf.Tensor:
           A_      =  self.convolution(_SCREAMING_SNAKE_CASE					)
           A_      =  self.normalization(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           A_      =  self.activation(_SCREAMING_SNAKE_CASE					)
           return hidden_state




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           A_      =  TFResNetConvLayer(
               config.embedding_size       ,	kernel_size=7       ,	stride=2       ,	activation=config.hidden_act       ,	name='''embedder'''       ,	)
           A_      =  tf.keras.layers.MaxPoolaD(pool_size=3       ,	strides=2       ,	padding='''valid'''       ,	name='''pooler'''					)
           A_      =  config.num_channels

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False					)						->	tf.Tensor:
           A_  ,A_  ,A_  ,A_      =  shape_list(_SCREAMING_SNAKE_CASE					)
           if tf.executing_eagerly() and num_channels != self.num_channels:
               raise ValueError(
                   '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''					)
           A_      =  pixel_values
           A_      =  self.embedder(_SCREAMING_SNAKE_CASE					)
           A_      =  tf.pad(_SCREAMING_SNAKE_CASE       ,	[[0, 0], [1, 1], [1, 1], [0, 0]]					)
           A_      =  self.pooler(_SCREAMING_SNAKE_CASE					)
           return hidden_state




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = 2       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           A_      =  tf.keras.layers.ConvaD(
               _SCREAMING_SNAKE_CASE       ,	kernel_size=1       ,	strides=_SCREAMING_SNAKE_CASE       ,	use_bias=_SCREAMING_SNAKE_CASE       ,	name='''convolution'''					)
           # Use same default momentum and epsilon as PyTorch equivalent
           A_      =  tf.keras.layers.BatchNormalization(epsilon=1E-5       ,	momentum=0.9       ,	name='''normalization'''					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False					)						->	tf.Tensor:
           A_      =  x
           A_      =  self.convolution(_SCREAMING_SNAKE_CASE					)
           A_      =  self.normalization(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           return hidden_state




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = 1       ,	_SCREAMING_SNAKE_CASE = "relu"       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           A_      =  in_channels != out_channels or stride != 1
           A_      =  TFResNetConvLayer(_SCREAMING_SNAKE_CASE       ,	stride=_SCREAMING_SNAKE_CASE       ,	name='''layer.0'''					)
           A_      =  TFResNetConvLayer(_SCREAMING_SNAKE_CASE       ,	activation=_SCREAMING_SNAKE_CASE       ,	name='''layer.1'''					)
           A_      =  (
               TFResNetShortCut(_SCREAMING_SNAKE_CASE       ,	stride=_SCREAMING_SNAKE_CASE       ,	name='''shortcut'''					)
               if should_apply_shortcut
               else tf.keras.layers.Activation('''linear'''       ,	name='''shortcut'''					)
           )
           A_      =  ACTaFN[activation]

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False					)						->	tf.Tensor:
           A_      =  hidden_state
           A_      =  self.conva(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           A_      =  self.conva(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           A_      =  self.shortcut(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           hidden_state += residual
           A_      =  self.activation(_SCREAMING_SNAKE_CASE					)
           return hidden_state




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = 1       ,	_SCREAMING_SNAKE_CASE = "relu"       ,	_SCREAMING_SNAKE_CASE = 4       ,	**_SCREAMING_SNAKE_CASE       ,	)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           A_      =  in_channels != out_channels or stride != 1
           A_      =  out_channels // reduction
           A_      =  TFResNetConvLayer(_SCREAMING_SNAKE_CASE       ,	kernel_size=1       ,	name='''layer.0'''					)
           A_      =  TFResNetConvLayer(_SCREAMING_SNAKE_CASE       ,	stride=_SCREAMING_SNAKE_CASE       ,	name='''layer.1'''					)
           A_      =  TFResNetConvLayer(_SCREAMING_SNAKE_CASE       ,	kernel_size=1       ,	activation=_SCREAMING_SNAKE_CASE       ,	name='''layer.2'''					)
           A_      =  (
               TFResNetShortCut(_SCREAMING_SNAKE_CASE       ,	stride=_SCREAMING_SNAKE_CASE       ,	name='''shortcut'''					)
               if should_apply_shortcut
               else tf.keras.layers.Activation('''linear'''       ,	name='''shortcut'''					)
           )
           A_      =  ACTaFN[activation]

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False					)						->	tf.Tensor:
           A_      =  hidden_state
           A_      =  self.conva(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           A_      =  self.conva(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           A_      =  self.conva(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           A_      =  self.shortcut(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           hidden_state += residual
           A_      =  self.activation(_SCREAMING_SNAKE_CASE					)
           return hidden_state




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = 2       ,	_SCREAMING_SNAKE_CASE = 2       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)

           A_      =  TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

           A_      =  [layer(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	stride=_SCREAMING_SNAKE_CASE       ,	activation=config.hidden_act       ,	name='''layers.0'''					)]
           layers += [
               layer(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	activation=config.hidden_act       ,	name=F'''layers.{i + 1}'''					)
               for i in range(depth - 1					)
           ]
           A_      =  layers

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False					)						->	tf.Tensor:
           for layer in self.stage_layers:
               A_      =  layer(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)
           return hidden_state




class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
           A_      =  [
               TFResNetStage(
                   _SCREAMING_SNAKE_CASE       ,	config.embedding_size       ,	config.hidden_sizes[0]       ,	stride=2 if config.downsample_in_first_stage else 1       ,	depth=config.depths[0]       ,	name='''stages.0'''       ,	)
           ]
           for i, (in_channels, out_channels, depth) in enumerate(
               zip(config.hidden_sizes       ,	config.hidden_sizes[1:]       ,	config.depths[1:]					)					):
               self.stages.append(TFResNetStage(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	depth=_SCREAMING_SNAKE_CASE       ,	name=F'''stages.{i + 1}'''					)					)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = True       ,	_SCREAMING_SNAKE_CASE = False       ,	)						->	TFBaseModelOutputWithNoAttention:
           A_      =  () if output_hidden_states else None

           for stage_module in self.stages:
               if output_hidden_states:
                   A_      =  hidden_states + (hidden_state,)

               A_      =  stage_module(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)

           if output_hidden_states:
               A_      =  hidden_states + (hidden_state,)

           if not return_dict:
               return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

           return TFBaseModelOutputWithNoAttention(last_hidden_state=_SCREAMING_SNAKE_CASE       ,	hidden_states=_SCREAMING_SNAKE_CASE					)




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   List[Any]  					=							ResNetConfig
       __lowercase   :   Optional[Any]  					=							'resnet'
       __lowercase   :   List[Any]  					=							'pixel_values'

       @property
       def    __A			( self					)						->	Union[str, Any]:
           return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)       ,	dtype=tf.floataa					)}
__snake_case  : Tuple		       =    R'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


__snake_case  : str		       =    R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'




@keras_serializable
class       __UpperCAmelCase							(							tf.keras.layers.Layer	):





       '''simple docstring'''



       __lowercase   :   str  					=							ResNetConfig

       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(**_SCREAMING_SNAKE_CASE					)
           A_      =  config
           A_      =  TFResNetEmbeddings(_SCREAMING_SNAKE_CASE       ,	name='''embedder'''					)
           A_      =  TFResNetEncoder(_SCREAMING_SNAKE_CASE       ,	name='''encoder'''					)
           A_      =  tf.keras.layers.GlobalAveragePoolingaD(keepdims=_SCREAMING_SNAKE_CASE					)

       @unpack_inputs
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = False       ,	)						->	Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
           A_      =  (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           A_      =  return_dict if return_dict is not None else self.config.use_return_dict

           # TF 2.0 image layers can't use NCHW format when running on CPU.
           # We transpose to NHWC format and then transpose back after the full forward pass.
           # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
           A_      =  tf.transpose(_SCREAMING_SNAKE_CASE       ,	perm=[0, 2, 3, 1]					)
           A_      =  self.embedder(_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)

           A_      =  self.encoder(
               _SCREAMING_SNAKE_CASE       ,	output_hidden_states=_SCREAMING_SNAKE_CASE       ,	return_dict=_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)

           A_      =  encoder_outputs[0]

           A_      =  self.pooler(_SCREAMING_SNAKE_CASE					)

           # Transpose all the outputs to the NCHW format
           # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
           A_      =  tf.transpose(_SCREAMING_SNAKE_CASE       ,	(0, 3, 1, 2)					)
           A_      =  tf.transpose(_SCREAMING_SNAKE_CASE       ,	(0, 3, 1, 2)					)
           A_      =  ()
           for hidden_state in encoder_outputs[1:]:
               A_      =  hidden_states + tuple(tf.transpose(_SCREAMING_SNAKE_CASE       ,	(0, 3, 1, 2)					) for h in hidden_state					)

           if not return_dict:
               return (last_hidden_state, pooled_output) + hidden_states

           A_      =  hidden_states if output_hidden_states else None

           return TFBaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=_SCREAMING_SNAKE_CASE       ,	pooler_output=_SCREAMING_SNAKE_CASE       ,	hidden_states=_SCREAMING_SNAKE_CASE       ,	)




@add_start_docstrings(
    'The bare ResNet model outputting raw features without any specific head on top.'		, _UpperCamelCase		, )
class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  TFResNetMainLayer(config=_SCREAMING_SNAKE_CASE       ,	name='''resnet'''					)

       @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE					)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC       ,	output_type=_SCREAMING_SNAKE_CASE       ,	config_class=_CONFIG_FOR_DOC       ,	modality='''vision'''       ,	expected_output=_EXPECTED_OUTPUT_SHAPE       ,	)
       @unpack_inputs
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = False       ,	)						->	Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
           A_      =  (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           A_      =  return_dict if return_dict is not None else self.config.use_return_dict

           A_      =  self.resnet(
               pixel_values=_SCREAMING_SNAKE_CASE       ,	output_hidden_states=_SCREAMING_SNAKE_CASE       ,	return_dict=_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE       ,	)
           return resnet_outputs




@add_start_docstrings(
    '\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '		, _UpperCamelCase		, )
class       __UpperCAmelCase							(							_UpperCamelCase		, _UpperCamelCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)						->	None:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           A_      =  config.num_labels
           A_      =  TFResNetMainLayer(_SCREAMING_SNAKE_CASE       ,	name='''resnet'''					)
           # classification head
           A_      =  (
               tf.keras.layers.Dense(config.num_labels       ,	name='''classifier.1'''					)
               if config.num_labels > 0
               else tf.keras.layers.Activation('''linear'''       ,	name='''classifier.1'''					)
           )

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE					)						->	tf.Tensor:
           A_      =  tf.keras.layers.Flatten()(_SCREAMING_SNAKE_CASE					)
           A_      =  self.classifier_layer(_SCREAMING_SNAKE_CASE					)
           return logits

       @add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE					)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT       ,	output_type=_SCREAMING_SNAKE_CASE       ,	config_class=_CONFIG_FOR_DOC       ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,	)
       @unpack_inputs
       def    __A			( self       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = None       ,	_SCREAMING_SNAKE_CASE = False       ,	)						->	Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
           A_      =  return_dict if return_dict is not None else self.config.use_return_dict

           A_      =  self.resnet(
               _SCREAMING_SNAKE_CASE       ,	output_hidden_states=_SCREAMING_SNAKE_CASE       ,	return_dict=_SCREAMING_SNAKE_CASE       ,	training=_SCREAMING_SNAKE_CASE					)

           A_      =  outputs.pooler_output if return_dict else outputs[1]

           A_      =  self.classifier(_SCREAMING_SNAKE_CASE					)

           A_      =  None if labels is None else self.hf_compute_loss(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

           if not return_dict:
               A_      =  (logits,) + outputs[2:]
               return (loss,) + output if loss is not None else output

           return TFImageClassifierOutputWithNoAttention(loss=_SCREAMING_SNAKE_CASE       ,	logits=_SCREAMING_SNAKE_CASE       ,	hidden_states=outputs.hidden_states					)







'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   str  					=							(ScoreSdeVeScheduler,)
       __lowercase   :   List[str]  					=							()

       @property
       def    __A			( self					)						->	List[str]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  torch.rand((batch_size, num_channels, height, width)					)

           return sample

       @property
       def    __A			( self					)						->	Optional[Any]:
           A_      =  4
           A_      =  3
           A_      =  8
           A_      =  8

           A_      =  batch_size * num_channels * height * width
           A_      =  torch.arange(_SCREAMING_SNAKE_CASE					)
           A_      =  sample.reshape(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
           A_      =  sample / num_elems
           A_      =  sample.permute(3       ,	0       ,	1       ,	2					)

           return sample

       def    __A			( self					)						->	Union[str, Any]:
           def model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	*_SCREAMING_SNAKE_CASE					):
               return sample * t / (t + 1)

           return model

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Tuple:
           A_      =  {
               '''num_train_timesteps''': 2000,
               '''snr''': 0.15,
               '''sigma_min''': 0.01,
               '''sigma_max''': 1348,
               '''sampling_eps''': 1E-5,
           }

           config.update(**_SCREAMING_SNAKE_CASE					)
           return config

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE=0       ,	**_SCREAMING_SNAKE_CASE					)						->	Any:
           A_      =  dict(self.forward_default_kwargs					)
           kwargs.update(_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_pred(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler outputs are not identical"

               A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  new_scheduler.step_correct(
                   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               assert torch.sum(torch.abs(output - new_output					)					) < 1E-5, "Scheduler correction are not identical"

       def    __A			( self					)						->	Any:
           for timesteps in [10, 100, 1000]:
               self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Dict:
           for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,	[1, 100, 1000]					):
               self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE       ,	sigma_max=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Optional[int]:
           for t in [0.1, 0.5, 0.75]:
               self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	int:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  self.scheduler_classes[0]
           A_      =  self.get_scheduler_config()
           A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

           A_      =  3

           A_      =  self.dummy_model()
           A_      =  self.dummy_sample_deter

           scheduler.set_sigmas(_SCREAMING_SNAKE_CASE					)
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
           A_      =  torch.manual_seed(0					)

           for i, t in enumerate(scheduler.timesteps					):
               A_      =  scheduler.sigmas[i]

               for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                       A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)
                   A_      =  scheduler.step_correct(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               with torch.no_grad():
                   A_      =  model(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	generator=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
               A_  ,A_      =  output.prev_sample, output.prev_sample_mean

           A_      =  torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
           A_      =  torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

           assert np.isclose(result_sum.item()       ,	14_372_758_528.0					)
           assert np.isclose(result_mean.item()       ,	18_714_530.0					)

       def    __A			( self					)						->	List[Any]:
           A_      =  dict(self.forward_default_kwargs					)

           A_      =  kwargs.pop('''num_inference_steps'''       ,	_SCREAMING_SNAKE_CASE					)

           for scheduler_class in self.scheduler_classes:
               A_      =  self.get_scheduler_config()
               A_      =  scheduler_class(**_SCREAMING_SNAKE_CASE					)

               A_      =  self.dummy_sample
               A_      =  0.1 * sample

               if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
               elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,	'''set_timesteps'''					):
                   A_      =  num_inference_steps

               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	0       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample
               A_      =  scheduler.step_pred(_SCREAMING_SNAKE_CASE       ,	1       ,	_SCREAMING_SNAKE_CASE       ,	generator=torch.manual_seed(0					)       ,	**_SCREAMING_SNAKE_CASE					).prev_sample

               self.assertEqual(output_a.shape       ,	sample.shape					)
               self.assertEqual(output_a.shape       ,	output_a.shape					)






