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
  
  
  _UpperCamelCase       = logging.get_logger(__name__)
  
  # General docstring
  _UpperCamelCase       = '''ResNetConfig'''
  
  # Base docstring
  _UpperCamelCase       = '''microsoft/resnet-50'''
  _UpperCamelCase       = [1, 2048, 7, 7]
  
  # Image classification docstring
  _UpperCamelCase       = '''microsoft/resnet-50'''
  _UpperCamelCase       = '''tiger cat'''
  
  _UpperCamelCase       = [
      '''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				,						UpperCAmelCase				,						UpperCAmelCase = 3				,						UpperCAmelCase = 1				,						UpperCAmelCase = "relu"				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          __snake_case   :				List[Any]        =       kernel_size // 2
          __snake_case   :				Tuple        =       tf.keras.layers.ConvaD(
              lowercase_				,						kernel_size=lowercase_				,						strides=lowercase_				,						padding="valid"				,						use_bias=lowercase_				,						name="convolution"   )
          # Use same default momentum and epsilon as PyTorch equivalent
          __snake_case   :				Optional[int]        =       tf.keras.layers.BatchNormalization(epsilon=1E-5				,						momentum=0.9				,						name="normalization"   )
          __snake_case   :				List[Any]        =       ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[Any]        =       (self.pad_value, self.pad_value)
          __snake_case   :				Tuple        =       tf.pad(lowercase_				,						[(0, 0), height_pad, width_pad, (0, 0)]   )
          __snake_case   :				Union[str, Any]        =       self.conv(lowercase_   )
          return hidden_state
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[int]        =       self.convolution(lowercase_   )
          __snake_case   :				Dict        =       self.normalization(lowercase_				,						training=lowercase_   )
          __snake_case   :				Any        =       self.activation(lowercase_   )
          return hidden_state
  
  
  
  
  class 				_lowerCamelCase (    tf.keras.layers.Layer    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          __snake_case   :				List[Any]        =       TFResNetConvLayer(
              config.embedding_size				,						kernel_size=7				,						stride=2				,						activation=config.hidden_act				,						name="embedder"				,						)
          __snake_case   :				Any        =       tf.keras.layers.MaxPoolaD(pool_size=3				,						strides=2				,						padding="valid"				,						name="pooler"   )
          __snake_case   :				Optional[Any]        =       config.num_channels
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[Any]        =       shape_list(lowercase_   )
          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."   )
          __snake_case   :				Optional[Any]        =       pixel_values
          __snake_case   :				Union[str, Any]        =       self.embedder(lowercase_   )
          __snake_case   :				str        =       tf.pad(lowercase_				,						[[0, 0], [1, 1], [1, 1], [0, 0]]   )
          __snake_case   :				Dict        =       self.pooler(lowercase_   )
          return hidden_state
  
  
  
  
  class 				_lowerCamelCase (    tf.keras.layers.Layer    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase = 2				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          __snake_case   :				Optional[int]        =       tf.keras.layers.ConvaD(
              lowercase_				,						kernel_size=1				,						strides=lowercase_				,						use_bias=lowercase_				,						name="convolution"   )
          # Use same default momentum and epsilon as PyTorch equivalent
          __snake_case   :				Optional[Any]        =       tf.keras.layers.BatchNormalization(epsilon=1E-5				,						momentum=0.9				,						name="normalization"   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       x
          __snake_case   :				Any        =       self.convolution(lowercase_   )
          __snake_case   :				Optional[int]        =       self.normalization(lowercase_				,						training=lowercase_   )
          return hidden_state
  
  
  
  
  class 				_lowerCamelCase (    tf.keras.layers.Layer    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 1				,						UpperCAmelCase = "relu"				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          __snake_case   :				Union[str, Any]        =       in_channels != out_channels or stride != 1
          __snake_case   :				List[str]        =       TFResNetConvLayer(lowercase_				,						stride=lowercase_				,						name="layer.0"   )
          __snake_case   :				str        =       TFResNetConvLayer(lowercase_				,						activation=lowercase_				,						name="layer.1"   )
          __snake_case   :				Tuple        =       (
              TFResNetShortCut(lowercase_				,						stride=lowercase_				,						name="shortcut"   )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear"				,						name="shortcut"   )
          )
          __snake_case   :				Tuple        =       ACTaFN[activation]
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				List[str]        =       hidden_state
          __snake_case   :				Tuple        =       self.conva(lowercase_				,						training=lowercase_   )
          __snake_case   :				Optional[Any]        =       self.conva(lowercase_				,						training=lowercase_   )
          __snake_case   :				Dict        =       self.shortcut(lowercase_				,						training=lowercase_   )
          hidden_state += residual
          __snake_case   :				Dict        =       self.activation(lowercase_   )
          return hidden_state
  
  
  
  
  class 				_lowerCamelCase (    tf.keras.layers.Layer    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 1				,						UpperCAmelCase = "relu"				,						UpperCAmelCase = 4				,						**UpperCAmelCase				,						)       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          __snake_case   :				List[str]        =       in_channels != out_channels or stride != 1
          __snake_case   :				int        =       out_channels // reduction
          __snake_case   :				List[Any]        =       TFResNetConvLayer(lowercase_				,						kernel_size=1				,						name="layer.0"   )
          __snake_case   :				str        =       TFResNetConvLayer(lowercase_				,						stride=lowercase_				,						name="layer.1"   )
          __snake_case   :				Optional[int]        =       TFResNetConvLayer(lowercase_				,						kernel_size=1				,						activation=lowercase_				,						name="layer.2"   )
          __snake_case   :				Optional[int]        =       (
              TFResNetShortCut(lowercase_				,						stride=lowercase_				,						name="shortcut"   )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear"				,						name="shortcut"   )
          )
          __snake_case   :				Union[str, Any]        =       ACTaFN[activation]
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       hidden_state
          __snake_case   :				Dict        =       self.conva(lowercase_				,						training=lowercase_   )
          __snake_case   :				List[str]        =       self.conva(lowercase_				,						training=lowercase_   )
          __snake_case   :				str        =       self.conva(lowercase_				,						training=lowercase_   )
          __snake_case   :				Dict        =       self.shortcut(lowercase_				,						training=lowercase_   )
          hidden_state += residual
          __snake_case   :				str        =       self.activation(lowercase_   )
          return hidden_state
  
  
  
  
  class 				_lowerCamelCase (    tf.keras.layers.Layer    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 2				,						UpperCAmelCase = 2				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
  
          __snake_case   :				List[str]        =       TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer
  
          __snake_case   :				int        =       [layer(lowercase_				,						lowercase_				,						stride=lowercase_				,						activation=config.hidden_act				,						name="layers.0"   )]
          layers += [
              layer(lowercase_				,						lowercase_				,						activation=config.hidden_act				,						name=F"""layers.{i + 1}"""   )
              for i in range(depth - 1   )
          ]
          __snake_case   :				Tuple        =       layers
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          for layer in self.stage_layers:
            __snake_case   :				List[Any]        =       layer(lowercase_				,						training=lowercase_   )
          return hidden_state
  
  
  
  
  class 				_lowerCamelCase (    tf.keras.layers.Layer    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
          __snake_case   :				Any        =       [
              TFResNetStage(
                  lowercase_				,						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(lowercase_				,						lowercase_				,						lowercase_				,						depth=lowercase_				,						name=F"""stages.{i + 1}"""   )   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = False				,						UpperCAmelCase = True				,						UpperCAmelCase = False				,						)       ->   TFBaseModelOutputWithNoAttention:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       () if output_hidden_states else None
  
          for stage_module in self.stages:
            if output_hidden_states:
              __snake_case   :				Optional[int]        =       hidden_states + (hidden_state,)
  
            __snake_case   :				Dict        =       stage_module(lowercase_				,						training=lowercase_   )
  
          if output_hidden_states:
            __snake_case   :				Tuple        =       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=lowercase_				,						hidden_states=lowercase_   )
  
  
  
  
  class 				_lowerCamelCase (    __UpperCAmelCase    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      Tuple       =ResNetConfig
        UpperCAmelCase_						:      Any       ="resnet"
        UpperCAmelCase_						:      Union[str, Any]       ="pixel_values"
  
  
  
  
  
  
  
        @property
        def 						UpperCAmelCase		( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)				,						dtype=tf.floataa   )}
  
  
  
  
  _UpperCamelCase       = 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'''
  
  
  _UpperCamelCase       = 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"""
  
  
  
  
  
  
        UpperCAmelCase_						:      str       =ResNetConfig
  
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(**lowercase_   )
          __snake_case   :				Optional[int]        =       config
          __snake_case   :				int        =       TFResNetEmbeddings(lowercase_				,						name="embedder"   )
          __snake_case   :				Union[str, Any]        =       TFResNetEncoder(lowercase_				,						name="encoder"   )
          __snake_case   :				str        =       tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowercase_   )
  
  
  
  
  
  
  
        @unpack_inputs
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = False				,						)       ->   Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          __snake_case   :				str        =       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)
          __snake_case   :				List[Any]        =       tf.transpose(lowercase_				,						perm=[0, 2, 3, 1]   )
          __snake_case   :				Optional[Any]        =       self.embedder(lowercase_				,						training=lowercase_   )
  
          __snake_case   :				int        =       self.encoder(
              lowercase_				,						output_hidden_states=lowercase_				,						return_dict=lowercase_				,						training=lowercase_   )
  
          __snake_case   :				Union[str, Any]        =       encoder_outputs[0]
  
          __snake_case   :				str        =       self.pooler(lowercase_   )
  
          # Transpose all the outputs to the NCHW format
          # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
          __snake_case   :				Optional[Any]        =       tf.transpose(lowercase_				,						(0, 3, 1, 2)   )
          __snake_case   :				Tuple        =       tf.transpose(lowercase_				,						(0, 3, 1, 2)   )
          __snake_case   :				Dict        =       ()
          for hidden_state in encoder_outputs[1:]:
            __snake_case   :				Any        =       hidden_states + tuple(tf.transpose(lowercase_				,						(0, 3, 1, 2)   ) for h in hidden_state   )
  
          if not return_dict:
            return (last_hidden_state, pooled_output) + hidden_states
  
          __snake_case   :				Tuple        =       hidden_states if output_hidden_states else None
  
          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=lowercase_				,						pooler_output=lowercase_				,						hidden_states=lowercase_				,						)
  
  
  
  
  @add_start_docstrings(
      "The bare ResNet model outputting raw features without any specific head on top." ,   __UpperCAmelCase ,   )
  class 				_lowerCamelCase (    __UpperCAmelCase    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(lowercase_				,						**lowercase_   )
          __snake_case   :				str        =       TFResNetMainLayer(config=lowercase_				,						name="resnet"   )
  
  
  
  
  
  
  
        @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				,						)
        @unpack_inputs
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = False				,						)       ->   Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          __snake_case   :				List[Any]        =       return_dict if return_dict is not None else self.config.use_return_dict
  
          __snake_case   :				Union[str, Any]        =       self.resnet(
              pixel_values=lowercase_				,						output_hidden_states=lowercase_				,						return_dict=lowercase_				,						training=lowercase_				,						)
          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 				_lowerCamelCase (    __UpperCAmelCase ,   __UpperCAmelCase    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						**UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__(lowercase_				,						**lowercase_   )
          __snake_case   :				Optional[int]        =       config.num_labels
          __snake_case   :				Optional[Any]        =       TFResNetMainLayer(lowercase_				,						name="resnet"   )
          # classification head
          __snake_case   :				Dict        =       (
              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 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   tf.Tensor:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       tf.keras.layers.Flatten()(lowercase_   )
          __snake_case   :				int        =       self.classifier_layer(lowercase_   )
          return logits
  
  
  
  
  
  
  
        @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				,						)
        @unpack_inputs
        def 						UpperCAmelCase		( self				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = None				,						UpperCAmelCase = False				,						)       ->   Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       return_dict if return_dict is not None else self.config.use_return_dict
  
          __snake_case   :				Tuple        =       self.resnet(
              lowercase_				,						output_hidden_states=lowercase_				,						return_dict=lowercase_				,						training=lowercase_   )
  
          __snake_case   :				Tuple        =       outputs.pooler_output if return_dict else outputs[1]
  
          __snake_case   :				List[str]        =       self.classifier(lowercase_   )
  
          __snake_case   :				Tuple        =       None if labels is None else self.hf_compute_loss(lowercase_				,						lowercase_   )
  
          if not return_dict:
            __snake_case   :				Optional[int]        =       (logits,) + outputs[2:]
            return (loss,) + output if loss is not None else output
  
          return TFImageClassifierOutputWithNoAttention(loss=lowercase_				,						logits=lowercase_				,						hidden_states=outputs.hidden_states   )
  
  
style_context: 
  
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  
  
  class 				_lowerCamelCase (    a    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   Optional[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          super().__init__()
  
          self.register_modules(vae=UpperCAmelCase				,						text_encoder=UpperCAmelCase				,						tokenizer=UpperCAmelCase				,						unet=UpperCAmelCase				,						scheduler=UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          with torch.no_grad():
            __snake_case   :				Tuple        =       self.vae.encode(tfms.ToTensor()(UpperCAmelCase   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
            __snake_case   :				Optional[int]        =       0.18_215 * latent.latent_dist.sample()
          return latent
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Optional[Any]        =       (1 / 0.18_215) * latent
          with torch.no_grad():
            __snake_case   :				Any        =       self.vae.decode(UpperCAmelCase   ).sample
          __snake_case   :				Optional[int]        =       (img / 2 + 0.5).clamp(0				,						1   )
          __snake_case   :				Tuple        =       img.detach().cpu().permute(0				,						2				,						3				,						1   ).numpy()
          __snake_case   :				Dict        =       (img * 255).round().astype("uint8"   )
          return Image.fromarray(img[0]   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				int        =       self.tokenizer(
              UpperCAmelCase				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
  
          __snake_case   :				List[Any]        =       self.text_encoder(text_input.input_ids.to(self.device   )   )[0]
  
          __snake_case   :				Union[str, Any]        =       self.tokenizer(
              ""				,						padding="max_length"				,						max_length=self.tokenizer.model_max_length				,						truncation=UpperCAmelCase				,						return_tensors="pt"				,						)
  
          __snake_case   :				int        =       self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
  
          return torch.cat([uncond_embedding, text_embedding]   )
  
  
  
  
  
  
  
        def __call__( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = 0.3				,						UpperCAmelCase = 0.6				,						UpperCAmelCase = 0.5				,						UpperCAmelCase = 42				,						UpperCAmelCase = 50				,						UpperCAmelCase = 7.5				,						)       ->   Image.Image:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Tuple        =       steps - int(kmin * steps   )
          __snake_case   :				Optional[int]        =       steps - int(kmax * steps   )
  
          __snake_case   :				Tuple        =       self.prep_text(UpperCAmelCase   )
  
          self.scheduler.set_timesteps(UpperCAmelCase   )
  
          __snake_case    ,   __snake_case   :				Optional[Any]        =       img.size
          __snake_case   :				List[Any]        =       self.encode(UpperCAmelCase   )
  
          torch.manual_seed(UpperCAmelCase   )
          __snake_case   :				str        =       torch.randn(
              (1, self.unet.config.in_channels, height // 8, width // 8)				,						).to(self.device   )
  
          __snake_case   :				int        =       self.scheduler.add_noise(
              UpperCAmelCase				,						UpperCAmelCase				,						timesteps=self.scheduler.timesteps[tmax]				,						)
  
          __snake_case   :				Optional[Any]        =       torch.cat([latents] * 2   )
  
          __snake_case   :				Union[str, Any]        =       self.scheduler.scale_model_input(UpperCAmelCase				,						self.scheduler.timesteps[tmax]   )
  
          with torch.no_grad():
            __snake_case   :				int        =       self.unet(
                UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						encoder_hidden_states=UpperCAmelCase				,						).sample
  
          __snake_case    ,   __snake_case   :				Union[str, Any]        =       pred.chunk(2   )
          __snake_case   :				Optional[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
          __snake_case   :				int        =       self.scheduler.step(UpperCAmelCase				,						self.scheduler.timesteps[tmax]				,						UpperCAmelCase   ).prev_sample
  
          for i, t in enumerate(tqdm(self.scheduler.timesteps   )   ):
            if i > tmax:
              if i < tmin:  # layout generation phase
                __snake_case   :				int        =       self.scheduler.add_noise(
                    UpperCAmelCase				,						UpperCAmelCase				,						timesteps=UpperCAmelCase				,						)
  
                __snake_case   :				str        =       (mix_factor * latents) + (
                    1 - mix_factor
                ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                __snake_case   :				List[Any]        =       torch.cat([input] * 2   )
  
              else:  # content generation phase
                __snake_case   :				str        =       torch.cat([latents] * 2   )
  
              __snake_case   :				Tuple        =       self.scheduler.scale_model_input(UpperCAmelCase				,						UpperCAmelCase   )
  
              with torch.no_grad():
                __snake_case   :				List[str]        =       self.unet(
                    UpperCAmelCase				,						UpperCAmelCase				,						encoder_hidden_states=UpperCAmelCase				,						).sample
  
              __snake_case    ,   __snake_case   :				str        =       pred.chunk(2   )
              __snake_case   :				List[Any]        =       pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
              __snake_case   :				List[Any]        =       self.scheduler.step(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   ).prev_sample
  
          return self.decode(UpperCAmelCase   )
  
  