



"""simple docstring"""







from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowercase__		:				Any          =	logging.get_logger(__name__)

# General docstring
lowercase__		:				Optional[int]          =	"""RegNetConfig"""

# Base docstring
lowercase__		:				Any          =	"""facebook/regnet-y-040"""
lowercase__		:				Dict          =	[1, 1_0_8_8, 7, 7]

# Image classification docstring
lowercase__		:				int          =	"""facebook/regnet-y-040"""
lowercase__		:				Tuple          =	"""tabby, tabby cat"""

lowercase__		:				Union[str, Any]          =	[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







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




      """simple docstring"""




      def __init__(      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int = 3    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = "relu"    ,			**SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        # The padding and conv has been verified in
        # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
        lowerCAmelCase_   :				Optional[Any]								= tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2       )
        lowerCAmelCase_   :				Optional[int]								= tf.keras.layers.ConvaD(
            filters=SCREAMING_SNAKE_CASE_    ,			kernel_size=SCREAMING_SNAKE_CASE_    ,			strides=SCREAMING_SNAKE_CASE_    ,			padding='VALID'    ,			groups=SCREAMING_SNAKE_CASE_    ,			use_bias=SCREAMING_SNAKE_CASE_    ,			name='convolution'    ,			)
        lowerCAmelCase_   :				List[str]								= tf.keras.layers.BatchNormalization(epsilon=1E-5    ,			momentum=0.9    ,			name='normalization'       )
        lowerCAmelCase_   :				List[Any]								= ACTaFN[activation] if activation is not None else tf.identity





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Tuple								= self.convolution(self.padding(SCREAMING_SNAKE_CASE_       )       )
        lowerCAmelCase_   :				Union[str, Any]								= self.normalization(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[int]								= self.activation(SCREAMING_SNAKE_CASE_       )
        return hidden_state







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




      """simple docstring"""




      def __init__(      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			**SCREAMING_SNAKE_CASE_				:      List[str]       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= config.num_channels
        lowerCAmelCase_   :				List[Any]								= TFRegNetConvLayer(
            out_channels=config.embedding_size    ,			kernel_size=3    ,			stride=2    ,			activation=config.hidden_act    ,			name='embedder'    ,			)





      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				Dict								= shape_list(SCREAMING_SNAKE_CASE_       )[1]
        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.'       )

        # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
        # So change the input format from `NCHW` to `NHWC`.
        # shape = (batch_size, in_height, in_width, in_channels=num_channels)
        lowerCAmelCase_   :				Optional[Any]								= tf.transpose(SCREAMING_SNAKE_CASE_    ,			perm=(0, 2, 3, 1)       )
        lowerCAmelCase_   :				List[Any]								= self.embedder(SCREAMING_SNAKE_CASE_       )
        return hidden_state







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




      """simple docstring"""




      def __init__(      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			**SCREAMING_SNAKE_CASE_				:      str       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= tf.keras.layers.ConvaD(
            filters=SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			strides=SCREAMING_SNAKE_CASE_    ,			use_bias=SCREAMING_SNAKE_CASE_    ,			name='convolution'       )
        lowerCAmelCase_   :				Optional[int]								= tf.keras.layers.BatchNormalization(epsilon=1E-5    ,			momentum=0.9    ,			name='normalization'       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      tf.Tensor    ,			SCREAMING_SNAKE_CASE_				:      bool = False       ):
        return self.normalization(self.convolution(SCREAMING_SNAKE_CASE_       )    ,			training=SCREAMING_SNAKE_CASE_       )







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




      """simple docstring"""




      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_    ,			name='pooler'       )
        lowerCAmelCase_   :				Dict								= [
            tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			activation='relu'    ,			name='attention.0'       ),
            tf.keras.layers.ConvaD(filters=SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			activation='sigmoid'    ,			name='attention.2'       ),
        ]





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
        lowerCAmelCase_   :				Tuple								= self.pooler(SCREAMING_SNAKE_CASE_       )
        for layer_module in self.attention:
          lowerCAmelCase_   :				Union[str, Any]								= layer_module(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= hidden_state * pooled
        return hidden_state







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




      """simple docstring"""




      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			**SCREAMING_SNAKE_CASE_				:      Dict       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= in_channels != out_channels or stride != 1
        lowerCAmelCase_   :				List[str]								= max(1    ,			out_channels // config.groups_width       )
        lowerCAmelCase_   :				Union[str, Any]								= (
            TFRegNetShortCut(SCREAMING_SNAKE_CASE_    ,			stride=SCREAMING_SNAKE_CASE_    ,			name='shortcut'       )
            if should_apply_shortcut
            else tf.keras.layers.Activation('linear'    ,			name='shortcut'       )
        )
        # `self.layers` instead of `self.layer` because that is a reserved argument.
        lowerCAmelCase_   :				int								= [
            TFRegNetConvLayer(SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			activation=config.hidden_act    ,			name='layer.0'       ),
            TFRegNetConvLayer(
                SCREAMING_SNAKE_CASE_    ,			stride=SCREAMING_SNAKE_CASE_    ,			groups=SCREAMING_SNAKE_CASE_    ,			activation=config.hidden_act    ,			name='layer.1'       ),
            TFRegNetConvLayer(SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			activation=SCREAMING_SNAKE_CASE_    ,			name='layer.2'       ),
        ]
        lowerCAmelCase_   :				int								= ACTaFN[config.hidden_act]





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				Any								= hidden_state
        for layer_module in self.layers:
          lowerCAmelCase_   :				Dict								= layer_module(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= self.shortcut(SCREAMING_SNAKE_CASE_       )
        hidden_state += residual
        lowerCAmelCase_   :				Union[str, Any]								= self.activation(SCREAMING_SNAKE_CASE_       )
        return hidden_state







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




      """simple docstring"""




      def __init__(      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= in_channels != out_channels or stride != 1
        lowerCAmelCase_   :				Union[str, Any]								= max(1    ,			out_channels // config.groups_width       )
        lowerCAmelCase_   :				Union[str, Any]								= (
            TFRegNetShortCut(SCREAMING_SNAKE_CASE_    ,			stride=SCREAMING_SNAKE_CASE_    ,			name='shortcut'       )
            if should_apply_shortcut
            else tf.keras.layers.Activation('linear'    ,			name='shortcut'       )
        )
        lowerCAmelCase_   :				Optional[Any]								= [
            TFRegNetConvLayer(SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			activation=config.hidden_act    ,			name='layer.0'       ),
            TFRegNetConvLayer(
                SCREAMING_SNAKE_CASE_    ,			stride=SCREAMING_SNAKE_CASE_    ,			groups=SCREAMING_SNAKE_CASE_    ,			activation=config.hidden_act    ,			name='layer.1'       ),
            TFRegNetSELayer(SCREAMING_SNAKE_CASE_    ,			reduced_channels=int(round(in_channels / 4       )       )    ,			name='layer.2'       ),
            TFRegNetConvLayer(SCREAMING_SNAKE_CASE_    ,			kernel_size=1    ,			activation=SCREAMING_SNAKE_CASE_    ,			name='layer.3'       ),
        ]
        lowerCAmelCase_   :				List[Any]								= ACTaFN[config.hidden_act]





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				int								= hidden_state
        for layer_module in self.layers:
          lowerCAmelCase_   :				Dict								= layer_module(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= self.shortcut(SCREAMING_SNAKE_CASE_       )
        hidden_state += residual
        lowerCAmelCase_   :				Optional[Any]								= self.activation(SCREAMING_SNAKE_CASE_       )
        return hidden_state







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




      """simple docstring"""




      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			**SCREAMING_SNAKE_CASE_				:      Any       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				str								= TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
        lowerCAmelCase_   :				List[Any]								= [
            # downsampling is done in the first layer with stride of 2
            layer(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			stride=SCREAMING_SNAKE_CASE_    ,			name='layers.0'       ),
            *[layer(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			name=F"layers.{i+1}"       ) for i in range(depth - 1       )],
        ]





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        for layer_module in self.layers:
          lowerCAmelCase_   :				Optional[int]								= layer_module(SCREAMING_SNAKE_CASE_       )
        return hidden_state







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




      """simple docstring"""




      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			**SCREAMING_SNAKE_CASE_				:      List[Any]       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= []
        # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
        self.stages.append(
            TFRegNetStage(
                SCREAMING_SNAKE_CASE_    ,			config.embedding_size    ,			config.hidden_sizes[0]    ,			stride=2 if config.downsample_in_first_stage else 1    ,			depth=config.depths[0]    ,			name='stages.0'    ,			)       )
        lowerCAmelCase_   :				Optional[Any]								= zip(config.hidden_sizes    ,			config.hidden_sizes[1:]       )
        for i, ((in_channels, out_channels), depth) in enumerate(zip(SCREAMING_SNAKE_CASE_    ,			config.depths[1:]       )       ):
          self.stages.append(TFRegNetStage(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			depth=SCREAMING_SNAKE_CASE_    ,			name=F"stages.{i+1}"       )       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      tf.Tensor    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			SCREAMING_SNAKE_CASE_				:      bool = True       ):
        lowerCAmelCase_   :				str								= () if output_hidden_states else None

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

          lowerCAmelCase_   :				int								= stage_module(SCREAMING_SNAKE_CASE_       )

        if output_hidden_states:
          lowerCAmelCase_   :				Any								= hidden_states + (hidden_state,)

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

        return TFBaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE_    ,			hidden_states=SCREAMING_SNAKE_CASE_       )







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




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     RegNetConfig



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			**SCREAMING_SNAKE_CASE_				:      Dict       ):
        super().__init__(**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= config
        lowerCAmelCase_   :				List[str]								= TFRegNetEmbeddings(SCREAMING_SNAKE_CASE_    ,			name='embedder'       )
        lowerCAmelCase_   :				str								= TFRegNetEncoder(SCREAMING_SNAKE_CASE_    ,			name='encoder'       )
        lowerCAmelCase_   :				Optional[Any]								= tf.keras.layers.GlobalAveragePoolingaD(keepdims=SCREAMING_SNAKE_CASE_    ,			name='pooler'       )





      @unpack_inputs
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      tf.Tensor    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      bool = False    ,			):
        lowerCAmelCase_   :				Optional[Any]								= (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCAmelCase_   :				Tuple								= return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase_   :				Tuple								= self.embedder(SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Union[str, Any]								= self.encoder(
            SCREAMING_SNAKE_CASE_    ,			output_hidden_states=SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Any								= encoder_outputs[0]
        lowerCAmelCase_   :				Any								= self.pooler(SCREAMING_SNAKE_CASE_       )

        # Change to NCHW output format have uniformity in the modules
        lowerCAmelCase_   :				Tuple								= tf.transpose(SCREAMING_SNAKE_CASE_    ,			perm=(0, 3, 1, 2)       )
        lowerCAmelCase_   :				Optional[int]								= tf.transpose(SCREAMING_SNAKE_CASE_    ,			perm=(0, 3, 1, 2)       )

        # Change the other hidden state outputs to NCHW as well
        if output_hidden_states:
          lowerCAmelCase_   :				Tuple								= tuple([tf.transpose(SCREAMING_SNAKE_CASE_    ,			perm=(0, 3, 1, 2)       ) for h in encoder_outputs[1]]       )

        if not return_dict:
          return (last_hidden_state, pooled_output) + encoder_outputs[1:]

        return TFBaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=SCREAMING_SNAKE_CASE_    ,			pooler_output=SCREAMING_SNAKE_CASE_    ,			hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states    ,			)







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     RegNetConfig
      _SCREAMING_SNAKE_CASE  				=     """regnet"""
      _SCREAMING_SNAKE_CASE  				=     """pixel_values"""



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)    ,			dtype=tf.floataa       )}




lowercase__		:				Dict          =	R"""
    Parameters:
    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.
        config ([`RegNetConfig`]): 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.
"""

lowercase__		:				Any          =	R"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__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.
"""







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




      """simple docstring"""




      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			*SCREAMING_SNAKE_CASE_				:      int    ,			**SCREAMING_SNAKE_CASE_				:      Any       ):
        super().__init__(SCREAMING_SNAKE_CASE_    ,			*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= TFRegNetMainLayer(SCREAMING_SNAKE_CASE_    ,			name='regnet'       )





      @unpack_inputs
      @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_       )
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC    ,			output_type=SCREAMING_SNAKE_CASE_    ,			config_class=_CONFIG_FOR_DOC    ,			modality='vision'    ,			expected_output=_EXPECTED_OUTPUT_SHAPE    ,			)
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      tf.Tensor    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[bool] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=False    ,			):
        lowerCAmelCase_   :				Optional[Any]								= (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCAmelCase_   :				Union[str, Any]								= return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase_   :				Dict								= self.regnet(
            pixel_values=SCREAMING_SNAKE_CASE_    ,			output_hidden_states=SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_    ,			)
        if not return_dict:
          return (outputs[0],) + outputs[1:]

        return TFBaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=outputs.last_hidden_state    ,			pooler_output=outputs.pooler_output    ,			hidden_states=outputs.hidden_states    ,			)







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




      """simple docstring"""




      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      RegNetConfig    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        super().__init__(SCREAMING_SNAKE_CASE_    ,			*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[Any]								= config.num_labels
        lowerCAmelCase_   :				List[str]								= TFRegNetMainLayer(SCREAMING_SNAKE_CASE_    ,			name='regnet'       )
        # classification head
        lowerCAmelCase_   :				int								= [
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(config.num_labels    ,			name='classifier.1'       ) if config.num_labels > 0 else tf.identity,
        ]





      @unpack_inputs
      @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE_       )
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT    ,			output_type=SCREAMING_SNAKE_CASE_    ,			config_class=_CONFIG_FOR_DOC    ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,			)
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      tf.Tensor = None    ,			SCREAMING_SNAKE_CASE_				:      tf.Tensor = None    ,			SCREAMING_SNAKE_CASE_				:      bool = None    ,			SCREAMING_SNAKE_CASE_				:      bool = None    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			):
        lowerCAmelCase_   :				Optional[int]								= (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowerCAmelCase_   :				Union[str, Any]								= return_dict if return_dict is not None else self.config.use_return_dict

        lowerCAmelCase_   :				Optional[int]								= self.regnet(
            SCREAMING_SNAKE_CASE_    ,			output_hidden_states=SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Union[str, Any]								= outputs.pooler_output if return_dict else outputs[1]

        lowerCAmelCase_   :				Optional[int]								= self.classifier[0](SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[str]								= self.classifier[1](SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				Any								= None if labels is None else self.hf_compute_loss(labels=SCREAMING_SNAKE_CASE_    ,			logits=SCREAMING_SNAKE_CASE_       )

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

        return TFSequenceClassifierOutput(loss=SCREAMING_SNAKE_CASE_    ,			logits=SCREAMING_SNAKE_CASE_    ,			hidden_states=outputs.hidden_states       )










"""simple docstring"""







from typing import Union

import fire
import torch
from tqdm import tqdm

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				str = "cpu"    ,   lowerCAmelCase__      :				Union[str, None] = None						)						->   None:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= torch.load(lowerCAmelCase__    ,   map_location=lowerCAmelCase__						)
  for k, v in tqdm(state_dict.items()						):
    if not isinstance(lowerCAmelCase__    ,   torch.Tensor						):
      raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'						)
    lowerCAmelCase_   :				str								= v.half()
  if save_path is None:  # overwrite src_path
    lowerCAmelCase_   :				Dict								= src_path
  torch.save(lowerCAmelCase__    ,   lowerCAmelCase__						)


if __name__ == "__main__":
     fire.Fire(convert)





