





"""simple docstring"""


# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class   lowercase_					(  lowercase_				):

       def __init__(      self ,       __SCREAMING_SNAKE_CASE ,       __SCREAMING_SNAKE_CASE  ):






        '''simple docstring'''

        super().__init__()
        self.register_modules(unet=snake_case__ ,       scheduler=snake_case__  )

       @torch.no_grad()
       def __call__(      self ,       __SCREAMING_SNAKE_CASE = 1 ,       __SCREAMING_SNAKE_CASE = None ,       __SCREAMING_SNAKE_CASE = 50 ,       __SCREAMING_SNAKE_CASE = "pil" ,       __SCREAMING_SNAKE_CASE = True ,       **__SCREAMING_SNAKE_CASE ,       ):






        '''simple docstring'''

        snake_case__     				=					torch.randn(
            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,       generator=snake_case__ ,       )
        snake_case__     				=					image.to(self.device  )

        # set step values
        self.scheduler.set_timesteps(snake_case__  )

        for t in self.progress_bar(self.scheduler.timesteps  ):
         # 1. predict noise model_output
         snake_case__     				=					self.unet(snake_case__ ,       snake_case__  ).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         snake_case__     				=					self.scheduler.step(snake_case__ ,       snake_case__ ,       snake_case__  ).prev_sample

        snake_case__     				=					(image / 2 + 0.5).clamp(0 ,       1  )
        snake_case__     				=					image.cpu().permute(0 ,       2 ,       3 ,       1  ).numpy()
        if output_type == "pil":
         snake_case__     				=					self.numpy_to_pil(snake_case__  )

        if not return_dict:
         return (image,), "This is a local test"

        return ImagePipelineOutput(images=snake_case__  ), "This is a local test"









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, 2048, 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        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int = 3 ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						int						)	->		None:


       """simple docstring"""



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

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						_lowercase     			=						(self.pad_value, self.pad_value)
       _lowercase     			=						tf.pad(snake_case__ ,							[(0, 0), height_pad, width_pad, (0, 0)]						)
       _lowercase     			=						self.conv(snake_case__						)
       return hidden_state

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						TFResNetConvLayer(
           config.embedding_size ,							kernel_size=7 ,							stride=2 ,							activation=config.hidden_act ,							name='embedder' ,							)
       _lowercase     			=						tf.keras.layers.MaxPoolaD(pool_size=3 ,							strides=2 ,							padding='valid' ,							name='pooler'						)
       _lowercase     			=						config.num_channels

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



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






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







   """simple docstring"""



   def __init__(     self:						List[str] ,							snake_case__:						int ,							snake_case__:						int = 2 ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=1 ,							strides=snake_case__ ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						x
       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							activation=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							snake_case__:						int = 4 ,							**snake_case__:						Optional[Any] ,							)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						out_channels // reduction
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							activation=snake_case__ ,							name='layer.2'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						ResNetConfig ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 2 ,							snake_case__:						int = 2 ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)

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

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

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       for layer in self.stage_layers:
           _lowercase     			=						layer(snake_case__ ,							training=snake_case__						)
       return hidden_state






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







   """simple docstring"""



   def __init__(     self:						Optional[Any] ,							snake_case__:						ResNetConfig ,							**snake_case__:						Union[str, Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       _lowercase     			=						[
           TFResNetStage(
               snake_case__ ,							config.embedding_size ,							config.hidden_sizes[0] ,							stride=2 if config.downsample_in_first_stage else 1 ,							depth=config.depths[0] ,							name='stages.0' ,							)
       ]
       for i, (in_channels, out_channels, depth) in enumerate(
           zip(config.hidden_sizes ,							config.hidden_sizes[1:] ,							config.depths[1:]						)						):
           self.stages.append(TFResNetStage(snake_case__ ,							snake_case__ ,							snake_case__ ,							depth=snake_case__ ,							name=f'''stages.{i + 1}'''						)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False ,							snake_case__:						bool = True ,							snake_case__:						bool = False ,							)	->		TFBaseModelOutputWithNoAttention:


       """simple docstring"""



       _lowercase     			=						() if output_hidden_states else None

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

           _lowercase     			=						stage_module(snake_case__ ,							training=snake_case__						)

       if output_hidden_states:
           _lowercase     			=						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=snake_case__ ,							hidden_states=snake_case__						)






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig
   __lowerCAmelCase  =    """resnet"""
   __lowerCAmelCase  =    """pixel_values"""

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Dict:


       """simple docstring"""



       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) ,							dtype=tf.floataa						)}






lowerCamelCase					    =	r'''
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''


lowerCamelCase					    =	r'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''

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







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig

   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Dict						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						config
       _lowercase     			=						TFResNetEmbeddings(snake_case__ ,							name='embedder'						)
       _lowercase     			=						TFResNetEncoder(snake_case__ ,							name='encoder'						)
       _lowercase     			=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__						)

   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



       _lowercase     			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowercase     			=						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)
       _lowercase     			=						tf.transpose(snake_case__ ,							perm=[0, 2, 3, 1]						)
       _lowercase     			=						self.embedder(snake_case__ ,							training=snake_case__						)

       _lowercase     			=						self.encoder(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

       _lowercase     			=						encoder_outputs[0]

       _lowercase     			=						self.pooler(snake_case__						)

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

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

       _lowercase     			=						hidden_states if output_hidden_states else None

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






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						TFResNetMainLayer(config=snake_case__ ,							name='resnet'						)

   @add_start_docstrings_to_model_forward(snake_case__						)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC ,							output_type=snake_case__ ,							config_class=_CONFIG_FOR_DOC ,							modality='vision' ,							expected_output=_EXPECTED_OUTPUT_SHAPE ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



       _lowercase     			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           pixel_values=snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__ ,							)
       return resnet_outputs






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ,      lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Any						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						config.num_labels
       _lowercase     			=						TFResNetMainLayer(snake_case__ ,							name='resnet'						)
       # classification head
       _lowercase     			=						(
           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       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						tf.keras.layers.Flatten()(snake_case__						)
       _lowercase     			=						self.classifier_layer(snake_case__						)
       return logits

   @add_start_docstrings_to_model_forward(snake_case__						)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT ,							output_type=snake_case__ ,							config_class=_CONFIG_FOR_DOC ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor = None ,							snake_case__:						tf.Tensor = None ,							snake_case__:						bool = None ,							snake_case__:						bool = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:


       """simple docstring"""



       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

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

       _lowercase     			=						self.classifier(snake_case__						)

       _lowercase     			=						None if labels is None else self.hf_compute_loss(snake_case__ ,							snake_case__						)

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

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