





'''simple docstring'''

from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


__lowerCAmelCase							:     Any		     =logging.get_logger(__name__)


# General docstring
__lowerCAmelCase							:     Union[str, Any]		     ="MobileNetV1Config"

# Base docstring
__lowerCAmelCase							:     Union[str, Any]		     ="google/mobilenet_v1_1.0_224"
__lowerCAmelCase							:     List[Any]		     =[1, 1024, 7, 7]

# Image classification docstring
__lowerCAmelCase							:     Any		     ="google/mobilenet_v1_1.0_224"
__lowerCAmelCase							:     Any		     ="tabby, tabby cat"


__lowerCAmelCase							:     int		     =[
    "google/mobilenet_v1_1.0_224",
    "google/mobilenet_v1_0.75_192",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Dict						,	_lowerCamelCase : Any=None	):
				A__    =      {}

				if isinstance(_lowerCamelCase						,	_lowerCamelCase	):
								A__    =      model.mobilenet_va
				else:
								A__    =      model

				A__    =      "MobilenetV1/Conv2d_0/"
				A__    =      backbone.conv_stem.convolution.weight
				A__    =      backbone.conv_stem.normalization.bias
				A__    =      backbone.conv_stem.normalization.weight
				A__    =      backbone.conv_stem.normalization.running_mean
				A__    =      backbone.conv_stem.normalization.running_var

				for i in range(13	):
								A__    =      i + 1
								A__    =      i * 2

								A__    =      backbone.layer[pt_index]
								A__    =      F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
								A__    =      pointer.convolution.weight
								A__    =      pointer.normalization.bias
								A__    =      pointer.normalization.weight
								A__    =      pointer.normalization.running_mean
								A__    =      pointer.normalization.running_var

								A__    =      backbone.layer[pt_index + 1]
								A__    =      F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
								A__    =      pointer.convolution.weight
								A__    =      pointer.normalization.bias
								A__    =      pointer.normalization.weight
								A__    =      pointer.normalization.running_mean
								A__    =      pointer.normalization.running_var

				if isinstance(_lowerCamelCase						,	_lowerCamelCase	):
								A__    =      "MobilenetV1/Logits/Conv2d_1c_1x1/"
								A__    =      model.classifier.weight
								A__    =      model.classifier.bias

				return tf_to_pt_map





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Dict						,	_lowerCamelCase : List[Any]	):
				try:
								import numpy as np
								import tensorflow as tf
				except ImportError:
								logger.error(
								    "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
								    "https://www.tensorflow.org/install/ for installation instructions."	)
								raise

				# Load weights from TF model
				A__    =      tf.train.list_variables(_lowerCamelCase	)
				A__    =      {}
				for name, shape in init_vars:
								logger.info(F"Loading TF weight {name} with shape {shape}"	)
								A__    =      tf.train.load_variable(_lowerCamelCase						,	_lowerCamelCase	)
								A__    =      array

				# Build TF to PyTorch weights loading map
				A__    =      _build_tf_to_pytorch_map(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

				for name, pointer in tf_to_pt_map.items():
								logger.info(F"Importing {name}"	)
								if name not in tf_weights:
												logger.info(F"{name} not in tf pre-trained weights, skipping"	)
												continue

								A__    =      tf_weights[name]

								if "depthwise_weights" in name:
												logger.info("Transposing depthwise"	)
												A__    =      np.transpose(_lowerCamelCase						,	(2, 3, 0, 1)	)
								elif "weights" in name:
												logger.info("Transposing"	)
												if len(pointer.shape	) == 2:  # copying into linear layer
																A__    =      array.squeeze().transpose()
												else:
																A__    =      np.transpose(_lowerCamelCase						,	(3, 2, 0, 1)	)

								if pointer.shape != array.shape:
												raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"	)

								logger.info(F"Initialize PyTorch weight {name} {array.shape}"	)
								A__    =      torch.from_numpy(_lowerCamelCase	)

								tf_weights.pop(_lowerCamelCase						,	_lowerCamelCase	)
								tf_weights.pop(name + "/RMSProp"						,	_lowerCamelCase	)
								tf_weights.pop(name + "/RMSProp_1"						,	_lowerCamelCase	)
								tf_weights.pop(name + "/ExponentialMovingAverage"						,	_lowerCamelCase	)

				logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys()	)}"	)
				return model





def 						UpperCamelCase    (							_lowerCamelCase : torch.Tensor						,	_lowerCamelCase : nn.Convad	):
				A__,     A__    =      features.shape[-2:]
				A__,     A__    =      conv_layer.stride
				A__,     A__    =      conv_layer.kernel_size

				if in_height % stride_height == 0:
								A__    =      max(kernel_height - stride_height						,	0	)
				else:
								A__    =      max(kernel_height - (in_height % stride_height)						,	0	)

				if in_width % stride_width == 0:
								A__    =      max(kernel_width - stride_width						,	0	)
				else:
								A__    =      max(kernel_width - (in_width % stride_width)						,	0	)

				A__    =      pad_along_width // 2
				A__    =      pad_along_width - pad_left
				A__    =      pad_along_height // 2
				A__    =      pad_along_height - pad_top

				A__    =      (pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(_lowerCamelCase						,	_lowerCamelCase						,	"constant"						,	0.0	)






class        UpperCAmelCase     (	nn.Module    ):
	def __init__(    self				:List[str]						,	lowercase_				:MobileNetVaConfig						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:int						,	lowercase_				:Optional[int] = 1						,	lowercase_				:Optional[int] = 1						,	lowercase_				:bool = False						,	lowercase_				:Optional[bool] = True						,	lowercase_				:Optional[bool or str] = True						,	)->							None:
					super().__init__()
					A__    =      config

					if in_channels % groups != 0:
									raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups."							)
					if out_channels % groups != 0:
									raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups."							)

					A__    =      0 if config.tf_padding else int((kernel_size - 1) / 2							)

					A__    =      nn.Convad(
					    in_channels=lowercase_						,	out_channels=lowercase_						,	kernel_size=lowercase_						,	stride=lowercase_						,	padding=lowercase_						,	groups=lowercase_						,	bias=lowercase_						,	padding_mode="zeros"						,	)

					if use_normalization:
									A__    =      nn.BatchNormad(
									    num_features=lowercase_						,	eps=config.layer_norm_eps						,	momentum=0.9_9_9_7						,	affine=lowercase_						,	track_running_stats=lowercase_						,	)
					else:
									A__    =      None

					if use_activation:
									if isinstance(lowercase_						,	lowercase_							):
													A__    =      ACTaFN[use_activation]
									elif isinstance(config.hidden_act						,	lowercase_							):
													A__    =      ACTaFN[config.hidden_act]
									else:
													A__    =      config.hidden_act
					else:
									A__    =      None



	def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:torch.Tensor							)->							torch.Tensor:
					if self.config.tf_padding:
									A__    =      apply_tf_padding(lowercase_						,	self.convolution							)
					A__    =      self.convolution(lowercase_							)
					if self.normalization is not None:
									A__    =      self.normalization(lowercase_							)
					if self.activation is not None:
									A__    =      self.activation(lowercase_							)
					return features






class        UpperCAmelCase     (	UpperCamelCase__    ):
	__lowercase       		=    MobileNetVaConfig
	__lowercase       		=    load_tf_weights_in_mobilenet_va
	__lowercase       		=    """mobilenet_v1"""
	__lowercase       		=    """pixel_values"""
	__lowercase       		=    False
	def 					UpperCAmelCase_							(    self				:Optional[int]						,	lowercase_				:Union[nn.Linear, nn.Convad]							)->							None:
					if isinstance(lowercase_						,	(nn.Linear, nn.Convad)							):
									module.weight.data.normal_(mean=0.0						,	std=self.config.initializer_range							)
									if module.bias is not None:
													module.bias.data.zero_()
					elif isinstance(lowercase_						,	nn.BatchNormad							):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0							)

__lowerCAmelCase							:     Dict		     =R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

__lowerCAmelCase							:     Optional[Any]		     =R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\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"






@add_start_docstrings(
    """The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."""      ,	UpperCamelCase__      ,	)
class        UpperCAmelCase     (	UpperCamelCase__    ):
	def __init__(    self				:Union[str, Any]						,	lowercase_				:MobileNetVaConfig						,	lowercase_				:bool = True							)->							int:
					super().__init__(lowercase_							)
					A__    =      config

					A__    =      32
					A__    =      max(int(depth * config.depth_multiplier							)						,	config.min_depth							)

					A__    =      MobileNetVaConvLayer(
					    lowercase_						,	in_channels=config.num_channels						,	out_channels=lowercase_						,	kernel_size=3						,	stride=2						,	)

					A__    =      [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

					A__    =      nn.ModuleList()
					for i in range(13							):
									A__    =      out_channels

									if strides[i] == 2 or i == 0:
													depth *= 2
													A__    =      max(int(depth * config.depth_multiplier							)						,	config.min_depth							)

									self.layer.append(
									    MobileNetVaConvLayer(
									        lowercase_						,	in_channels=lowercase_						,	out_channels=lowercase_						,	kernel_size=3						,	stride=strides[i]						,	groups=lowercase_						,	)							)

									self.layer.append(
									    MobileNetVaConvLayer(
									        lowercase_						,	in_channels=lowercase_						,	out_channels=lowercase_						,	kernel_size=1						,	)							)

					A__    =      nn.AdaptiveAvgPoolad((1, 1)							) if add_pooling_layer else None

					# Initialize weights and apply final processing
					self.post_init()
	def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int							)->							Optional[int]:
					raise NotImplementedError



	@add_start_docstrings_to_model_forward(lowercase_							)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC						,	output_type=lowercase_						,	config_class=_CONFIG_FOR_DOC						,	modality="vision"						,	expected_output=_EXPECTED_OUTPUT_SHAPE						,	)
	def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[torch.Tensor] = None						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Optional[bool] = None						,	)->							Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
					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

					if pixel_values is None:
									raise ValueError("You have to specify pixel_values"							)

					A__    =      self.conv_stem(lowercase_							)

					A__    =      () if output_hidden_states else None

					for i, layer_module in enumerate(self.layer							):
									A__    =      layer_module(lowercase_							)

									if output_hidden_states:
													A__    =      all_hidden_states + (hidden_states,)

					A__    =      hidden_states

					if self.pooler is not None:
									A__    =      torch.flatten(self.pooler(lowercase_							)						,	start_dim=1							)
					else:
									A__    =      None

					if not return_dict:
									return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None							)

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=lowercase_						,	pooler_output=lowercase_						,	hidden_states=lowercase_						,	)






@add_start_docstrings(
    """
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      ,	UpperCamelCase__      ,	)
class        UpperCAmelCase     (	UpperCamelCase__    ):
	def __init__(    self				:str						,	lowercase_				:MobileNetVaConfig							)->							None:
					super().__init__(lowercase_							)

					A__    =      config.num_labels
					A__    =      MobileNetVaModel(lowercase_							)

					A__    =      self.mobilenet_va.layer[-1].convolution.out_channels

					# Classifier head
					A__    =      nn.Dropout(config.classifier_dropout_prob						,	inplace=lowercase_							)
					A__    =      nn.Linear(lowercase_						,	config.num_labels							) if config.num_labels > 0 else nn.Identity()

					# Initialize weights and apply final processing
					self.post_init()



	@add_start_docstrings_to_model_forward(lowercase_							)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT						,	output_type=lowercase_						,	config_class=_CONFIG_FOR_DOC						,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,	)
	def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[torch.Tensor] = None						,	lowercase_				:Optional[bool] = None						,	lowercase_				:Optional[torch.Tensor] = None						,	lowercase_				:Optional[bool] = None						,	)->							Union[tuple, ImageClassifierOutputWithNoAttention]:
					A__    =      return_dict if return_dict is not None else self.config.use_return_dict

					A__    =      self.mobilenet_va(lowercase_						,	output_hidden_states=lowercase_						,	return_dict=lowercase_							)

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

					A__    =      self.classifier(self.dropout(lowercase_							)							)

					A__    =      None
					if labels is not None:
									if self.config.problem_type is None:
													if self.num_labels == 1:
																	A__    =      "regression"
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																	A__    =      "single_label_classification"
													else:
																	A__    =      "multi_label_classification"

									if self.config.problem_type == "regression":
													A__    =      MSELoss()
													if self.num_labels == 1:
																	A__    =      loss_fct(logits.squeeze()						,	labels.squeeze()							)
													else:
																	A__    =      loss_fct(lowercase_						,	lowercase_							)
									elif self.config.problem_type == "single_label_classification":
													A__    =      CrossEntropyLoss()
													A__    =      loss_fct(logits.view(-1						,	self.num_labels							)						,	labels.view(-1							)							)
									elif self.config.problem_type == "multi_label_classification":
													A__    =      BCEWithLogitsLoss()
													A__    =      loss_fct(lowercase_						,	lowercase_							)

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

					return ImageClassifierOutputWithNoAttention(
					    loss=lowercase_						,	logits=lowercase_						,	hidden_states=outputs.hidden_states						,	)










'''simple docstring'''

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCAmelCase     (	UpperCamelCase__    ):
 __lowercase       		=    (PNDMScheduler,)
 __lowercase       		=    (("""num_inference_steps""", 50),)
 def 					UpperCAmelCase_							(    self				:List[str]						,	**lowercase_				:Optional[int]							)->							List[str]:
     A__    =      {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
     }

     config.update(**lowercase_							)
     return config
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:Optional[int]=0						,	**lowercase_				:Any							)->							int:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config(**lowercase_							)
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)
         # copy over dummy past residuals
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             new_scheduler.set_timesteps(lowercase_							)
             # copy over dummy past residuals
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:Optional[Any]=0						,	**lowercase_				:List[str]							)->							str:
     A__    =      dict(self.forward_default_kwargs							)
     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)
     A__    =      self.dummy_sample
     A__    =      0.1 * sample
     A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)
         scheduler.set_timesteps(lowercase_							)

         # copy over dummy past residuals (must be after setting timesteps)
         A__    =      dummy_past_residuals[:]

         with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(lowercase_							)
             A__    =      scheduler_class.from_pretrained(lowercase_							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(lowercase_							)

             # copy over dummy past residual (must be after setting timesteps)
             A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_							).prev_sample

         assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"
 def 					UpperCAmelCase_							(    self				:Optional[int]						,	**lowercase_				:Dict							)->							Optional[int]:
     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(**lowercase_							)
     A__    =      scheduler_class(**lowercase_							)

     A__    =      10
     A__    =      self.dummy_model()
     A__    =      self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_							)

     for i, t in enumerate(scheduler.prk_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     for i, t in enumerate(scheduler.plms_timesteps							):
         A__    =      model(lowercase_						,	lowercase_							)
         A__    =      scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_							).prev_sample

     return sample
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      dict(self.forward_default_kwargs							)

     A__    =      kwargs.pop("num_inference_steps"						,	lowercase_							)

     for scheduler_class in self.scheduler_classes:
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         A__    =      self.dummy_sample
         A__    =      0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_						,	"set_timesteps"							):
             scheduler.set_timesteps(lowercase_							)
         elif num_inference_steps is not None and not hasattr(lowercase_						,	"set_timesteps"							):
             A__    =      num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         A__    =      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
         A__    =      dummy_past_residuals[:]

         A__    =      scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

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

         A__    =      scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_							).prev_sample
         A__    =      scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_							).prev_sample

         self.assertEqual(output_a.shape						,	sample.shape							)
         self.assertEqual(output_a.shape						,	output_a.shape							)
 def 					UpperCAmelCase_							(    self				:Any							)->							Dict:
     for timesteps in [1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_							)

     A__    =      self.scheduler_classes[0]
     A__    =      self.get_scheduler_config(steps_offset=1							)
     A__    =      scheduler_class(**lowercase_							)
     scheduler.set_timesteps(10							)
     assert torch.equal(
         scheduler.timesteps						,	torch.LongTensor(
             [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]							)						,	)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Any:
     for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1]						,	[0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Tuple:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Tuple:
     for t in [1, 5, 10]:
         self.check_over_forward(time_step=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Optional[int]:
     for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 1_00]							):
         self.check_over_forward(num_inference_steps=lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     # earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
     A__    =      27

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

         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.set_timesteps(lowercase_							)

         # before power of 3 fix, would error on first step, so we only need to do two
         for i, t in enumerate(scheduler.prk_timesteps[:2]							):
             A__    =      scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_							).prev_sample
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     with self.assertRaises(lowercase_							):
         A__    =      self.scheduler_classes[0]
         A__    =      self.get_scheduler_config()
         A__    =      scheduler_class(**lowercase_							)

         scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample							).prev_sample
 def 					UpperCAmelCase_							(    self				:int							)->							str:
     A__    =      self.full_loop()
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_9_8.1_3_1_8							) < 1E-2
     assert abs(result_mean.item() - 0.2_5_8_0							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.full_loop(prediction_type="v_prediction"							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 6_7.3_9_8_6							) < 1E-2
     assert abs(result_mean.item() - 0.0_8_7_8							) < 1E-3
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 2_3_0.0_3_9_9							) < 1E-2
     assert abs(result_mean.item() - 0.2_9_9_5							) < 1E-3



 def 					UpperCAmelCase_							(    self				:Dict							)->							Any:
     # We specify different beta, so that the first alpha is 0.99
     A__    =      self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.0_1							)
     A__    =      torch.sum(torch.abs(lowercase_							)							)
     A__    =      torch.mean(torch.abs(lowercase_							)							)

     assert abs(result_sum.item() - 1_8_6.9_4_8_2							) < 1E-2
     assert abs(result_mean.item() - 0.2_4_3_4							) < 1E-3



