code: 
  
  
  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
  
  
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  
  # General docstring
  lowerCAmelCase__   	=				'ResNetConfig'
  
  # Base docstring
  lowerCAmelCase__   	=				'microsoft/resnet-50'
  lowerCAmelCase__   	=				[1, 20_48, 7, 7]
  
  # Image classification docstring
  lowerCAmelCase__   	=				'microsoft/resnet-50'
  lowerCAmelCase__   	=				'tiger cat'
  
  lowerCAmelCase__   	=				[
      'microsoft/resnet-50',
      # See all resnet models at https://huggingface.co/models?filter=resnet
  ]
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase = 3				,		__lowerCamelCase = 1				,		__lowerCamelCase = "relu"				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
              _A   : Tuple       					=				kernel_size // 2
              _A   : List[str]       					=				tf.keras.layers.ConvaD(
                  __lowerCamelCase				,		kernel_size=__lowerCamelCase				,		strides=__lowerCamelCase				,		padding="valid"				,		use_bias=__lowerCamelCase				,		name="convolution")
              # Use same default momentum and epsilon as PyTorch equivalent
              _A   : Dict       					=				tf.keras.layers.BatchNormalization(epsilon=1e-5				,		momentum=0.9				,		name="normalization")
              _A   : List[Any]       					=				ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear")
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       tf.Tensor:
              # Pad to match that done in the PyTorch Conv2D model
              _A   : Dict       					=				(self.pad_value, self.pad_value)
              _A   : List[Any]       					=				tf.pad(__lowerCamelCase				,		[(0, 0), height_pad, width_pad, (0, 0)])
              _A   : int       					=				self.conv(__lowerCamelCase)
              return hidden_state
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       tf.Tensor:
              _A   : Optional[int]       					=				self.convolution(__lowerCamelCase)
              _A   : List[Any]       					=				self.normalization(__lowerCamelCase				,		training=__lowerCamelCase)
              _A   : Optional[int]       					=				self.activation(__lowerCamelCase)
              return hidden_state
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
              _A   : Optional[int]       					=				TFResNetConvLayer(
                  config.embedding_size				,		kernel_size=7				,		stride=2				,		activation=config.hidden_act				,		name="embedder"				,		)
              _A   : Optional[Any]       					=				tf.keras.layers.MaxPoolaD(pool_size=3				,		strides=2				,		padding="valid"				,		name="pooler")
              _A   : int       					=				config.num_channels
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       tf.Tensor:
              _A					,    _A					,    _A					,    _A   : Union[str, Any]       					=				shape_list(__lowerCamelCase)
              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   : Optional[int]       					=				pixel_values
              _A   : str       					=				self.embedder(__lowerCamelCase)
              _A   : int       					=				tf.pad(__lowerCamelCase				,		[[0, 0], [1, 1], [1, 1], [0, 0]])
              _A   : List[str]       					=				self.pooler(__lowerCamelCase)
              return hidden_state
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase = 2				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
              _A   : Tuple       					=				tf.keras.layers.ConvaD(
                  __lowerCamelCase				,		kernel_size=1				,		strides=__lowerCamelCase				,		use_bias=__lowerCamelCase				,		name="convolution")
              # Use same default momentum and epsilon as PyTorch equivalent
              _A   : int       					=				tf.keras.layers.BatchNormalization(epsilon=1e-5				,		momentum=0.9				,		name="normalization")
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       tf.Tensor:
              _A   : Any       					=				x
              _A   : List[Any]       					=				self.convolution(__lowerCamelCase)
              _A   : List[str]       					=				self.normalization(__lowerCamelCase				,		training=__lowerCamelCase)
              return hidden_state
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = 1				,		__lowerCamelCase = "relu"				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
              _A   : int       					=				in_channels != out_channels or stride != 1
              _A   : List[Any]       					=				TFResNetConvLayer(__lowerCamelCase				,		stride=__lowerCamelCase				,		name="layer.0")
              _A   : str       					=				TFResNetConvLayer(__lowerCamelCase				,		activation=__lowerCamelCase				,		name="layer.1")
              _A   : Optional[Any]       					=				(
                  TFResNetShortCut(__lowerCamelCase				,		stride=__lowerCamelCase				,		name="shortcut")
                  if should_apply_shortcut
                  else tf.keras.layers.Activation("linear"				,		name="shortcut")
              )
              _A   : int       					=				ACTaFN[activation]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       tf.Tensor:
              _A   : str       					=				hidden_state
              _A   : Union[str, Any]       					=				self.conva(__lowerCamelCase				,		training=__lowerCamelCase)
              _A   : int       					=				self.conva(__lowerCamelCase				,		training=__lowerCamelCase)
              _A   : Any       					=				self.shortcut(__lowerCamelCase				,		training=__lowerCamelCase)
              hidden_state += residual
              _A   : Union[str, Any]       					=				self.activation(__lowerCamelCase)
              return hidden_state
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = 1				,		__lowerCamelCase = "relu"				,		__lowerCamelCase = 4				,		**__lowerCamelCase				,		)    ->       None:
              super().__init__(**__lowerCamelCase)
              _A   : Any       					=				in_channels != out_channels or stride != 1
              _A   : Any       					=				out_channels // reduction
              _A   : int       					=				TFResNetConvLayer(__lowerCamelCase				,		kernel_size=1				,		name="layer.0")
              _A   : str       					=				TFResNetConvLayer(__lowerCamelCase				,		stride=__lowerCamelCase				,		name="layer.1")
              _A   : Union[str, Any]       					=				TFResNetConvLayer(__lowerCamelCase				,		kernel_size=1				,		activation=__lowerCamelCase				,		name="layer.2")
              _A   : Optional[Any]       					=				(
                  TFResNetShortCut(__lowerCamelCase				,		stride=__lowerCamelCase				,		name="shortcut")
                  if should_apply_shortcut
                  else tf.keras.layers.Activation("linear"				,		name="shortcut")
              )
              _A   : Union[str, Any]       					=				ACTaFN[activation]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       tf.Tensor:
              _A   : int       					=				hidden_state
              _A   : Tuple       					=				self.conva(__lowerCamelCase				,		training=__lowerCamelCase)
              _A   : Dict       					=				self.conva(__lowerCamelCase				,		training=__lowerCamelCase)
              _A   : List[Any]       					=				self.conva(__lowerCamelCase				,		training=__lowerCamelCase)
              _A   : Union[str, Any]       					=				self.shortcut(__lowerCamelCase				,		training=__lowerCamelCase)
              hidden_state += residual
              _A   : Tuple       					=				self.activation(__lowerCamelCase)
              return hidden_state
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase = 2				,		__lowerCamelCase = 2				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
  
              _A   : Union[str, Any]       					=				TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer
  
              _A   : Optional[int]       					=				[layer(__lowerCamelCase				,		__lowerCamelCase				,		stride=__lowerCamelCase				,		activation=config.hidden_act				,		name="layers.0")]
              layers += [
                  layer(__lowerCamelCase				,		__lowerCamelCase				,		activation=config.hidden_act				,		name=F"layers.{i + 1}")
                  for i in range(depth - 1)
              ]
              _A   : Optional[Any]       					=				layers
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False)    ->       tf.Tensor:
              for layer in self.stage_layers:
                     _A   : Dict       					=				layer(__lowerCamelCase				,		training=__lowerCamelCase)
              return hidden_state
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
              # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
              _A   : Dict       					=				[
                  TFResNetStage(
                      __lowerCamelCase				,		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(__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		depth=__lowerCamelCase				,		name=F"stages.{i + 1}"))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = False				,		__lowerCamelCase = True				,		__lowerCamelCase = False				,		)    ->       TFBaseModelOutputWithNoAttention:
              _A   : List[str]       					=				() if output_hidden_states else None
  
              for stage_module in self.stages:
                     if output_hidden_states:
                            _A   : str       					=				hidden_states + (hidden_state,)
  
                     _A   : int       					=				stage_module(__lowerCamelCase				,		training=__lowerCamelCase)
  
              if output_hidden_states:
                     _A   : List[str]       					=				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=__lowerCamelCase				,		hidden_states=__lowerCamelCase)
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				ResNetConfig
       __SCREAMING_SNAKE_CASE						    =				"resnet"
       __SCREAMING_SNAKE_CASE						    =				"pixel_values"
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       int:
              return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)				,		dtype=tf.floataa)}
  
  
  
  
  lowerCAmelCase__   	=				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'
  
  
  lowerCAmelCase__   	=				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   lowerCAmelCase__ (       tf.keras.layers.Layer):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				ResNetConfig
  
  
       def __init__(		self				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       None:
              super().__init__(**__lowerCamelCase)
              _A   : Any       					=				config
              _A   : Any       					=				TFResNetEmbeddings(__lowerCamelCase				,		name="embedder")
              _A   : Any       					=				TFResNetEncoder(__lowerCamelCase				,		name="encoder")
              _A   : str       					=				tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowerCamelCase)
  
  
       @unpack_inputs
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		)    ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
              _A   : int       					=				(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              _A   : Union[str, Any]       					=				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   : List[Any]       					=				tf.transpose(__lowerCamelCase				,		perm=[0, 2, 3, 1])
              _A   : List[Any]       					=				self.embedder(__lowerCamelCase				,		training=__lowerCamelCase)
  
              _A   : str       					=				self.encoder(
                  __lowerCamelCase				,		output_hidden_states=__lowerCamelCase				,		return_dict=__lowerCamelCase				,		training=__lowerCamelCase)
  
              _A   : str       					=				encoder_outputs[0]
  
              _A   : List[Any]       					=				self.pooler(__lowerCamelCase)
  
              # Transpose all the outputs to the NCHW format
              # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
              _A   : Union[str, Any]       					=				tf.transpose(__lowerCamelCase				,		(0, 3, 1, 2))
              _A   : Union[str, Any]       					=				tf.transpose(__lowerCamelCase				,		(0, 3, 1, 2))
              _A   : int       					=				()
              for hidden_state in encoder_outputs[1:]:
                     _A   : Optional[int]       					=				hidden_states + tuple(tf.transpose(__lowerCamelCase				,		(0, 3, 1, 2)) for h in hidden_state)
  
              if not return_dict:
                     return (last_hidden_state, pooled_output) + hidden_states
  
              _A   : Dict       					=				hidden_states if output_hidden_states else None
  
              return TFBaseModelOutputWithPoolingAndNoAttention(
                  last_hidden_state=__lowerCamelCase				,		pooler_output=__lowerCamelCase				,		hidden_states=__lowerCamelCase				,		)
  
  
  
  
  
  
  
  @add_start_docstrings(
      "The bare ResNet model outputting raw features without any specific head on top."  ,					a  ,					)
  class   lowerCAmelCase__ (       a):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       None:
              super().__init__(__lowerCamelCase				,		**__lowerCamelCase)
              _A   : List[str]       					=				TFResNetMainLayer(config=__lowerCamelCase				,		name="resnet")
  
  
       @add_start_docstrings_to_model_forward(__lowerCamelCase)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC				,		output_type=__lowerCamelCase				,		config_class=_CONFIG_FOR_DOC				,		modality="vision"				,		expected_output=_EXPECTED_OUTPUT_SHAPE				,		)
       @unpack_inputs
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		)    ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
              _A   : List[Any]       					=				(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              _A   : List[str]       					=				return_dict if return_dict is not None else self.config.use_return_dict
  
              _A   : Dict       					=				self.resnet(
                  pixel_values=__lowerCamelCase				,		output_hidden_states=__lowerCamelCase				,		return_dict=__lowerCamelCase				,		training=__lowerCamelCase				,		)
              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    "  ,					a  ,					)
  class   lowerCAmelCase__ (       a  ,					a):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		**__lowerCamelCase)    ->       None:
              super().__init__(__lowerCamelCase				,		**__lowerCamelCase)
              _A   : Optional[int]       					=				config.num_labels
              _A   : Any       					=				TFResNetMainLayer(__lowerCamelCase				,		name="resnet")
              # classification head
              _A   : str       					=				(
                  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 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       tf.Tensor:
              _A   : Any       					=				tf.keras.layers.Flatten()(__lowerCamelCase)
              _A   : int       					=				self.classifier_layer(__lowerCamelCase)
              return logits
  
  
       @add_start_docstrings_to_model_forward(__lowerCamelCase)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT				,		output_type=__lowerCamelCase				,		config_class=_CONFIG_FOR_DOC				,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,		)
       @unpack_inputs
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = None				,		__lowerCamelCase = False				,		)    ->       Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
              _A   : str       					=				return_dict if return_dict is not None else self.config.use_return_dict
  
              _A   : Union[str, Any]       					=				self.resnet(
                  __lowerCamelCase				,		output_hidden_states=__lowerCamelCase				,		return_dict=__lowerCamelCase				,		training=__lowerCamelCase)
  
              _A   : str       					=				outputs.pooler_output if return_dict else outputs[1]
  
              _A   : Tuple       					=				self.classifier(__lowerCamelCase)
  
              _A   : str       					=				None if labels is None else self.hf_compute_loss(__lowerCamelCase				,		__lowerCamelCase)
  
              if not return_dict:
                     _A   : Union[str, Any]       					=				(logits,) + outputs[2:]
                     return (loss,) + output if loss is not None else output
  
              return TFImageClassifierOutputWithNoAttention(loss=__lowerCamelCase				,		logits=__lowerCamelCase				,		hidden_states=outputs.hidden_states)
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
                if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				argparse.ArgumentParser()
  
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
          type=str,
          required=False,
          help='Path to the checkpoint to convert.',
      )
  
      parser.add_argument(
          '--config_file',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
          type=str,
          required=False,
          help='The config json file corresponding to the architecture.',
      )
  
      parser.add_argument(
          '--dump_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
          type=str,
          required=False,
          help='Path to the output model.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  