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


__lowerCamelCase    :				Any							     =	logging.get_logger(__name__)

# General docstring
__lowerCamelCase    :				List[Any]							     =	'''RegNetConfig'''

# Base docstring
__lowerCamelCase    :				Optional[int]							     =	'''facebook/regnet-y-040'''
__lowerCamelCase    :				Tuple							     =	[1, 1088, 7, 7]

# Image classification docstring
__lowerCamelCase    :				Any							     =	'''facebook/regnet-y-040'''
__lowerCamelCase    :				Tuple							     =	'''tabby, tabby cat'''

__lowerCamelCase    :				Optional[Any]							     =	[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







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




   def __init__(					self    :       Any ,			_lowercase    :       int ,			_lowercase    :       int = 3 ,			_lowercase    :       int = 1 ,			_lowercase    :       int = 1 ,			_lowercase    :       Optional[str] = "relu" ,			**_lowercase    :       Optional[Any] ,			):



     """simple docstring"""






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




   def 			__a		(					self    :       Any ,			_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.convolution(self.padding(_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   self.normalization(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.activation(_lowercase   )
     return hidden_state







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




   def __init__(					self    :       Union[str, Any] ,			_lowercase    :       RegNetConfig ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   config.num_channels
     SCREAMING_SNAKE_CASE__    							=   TFRegNetConvLayer(
         out_channels=config.embedding_size ,			kernel_size=3 ,			stride=2 ,			activation=config.hidden_act ,			name="""embedder""" ,			)




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   shape_list(_lowercase   )[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)
     SCREAMING_SNAKE_CASE__    							=   tf.transpose(_lowercase ,			perm=(0, 2, 3, 1)   )
     SCREAMING_SNAKE_CASE__    							=   self.embedder(_lowercase   )
     return hidden_state







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




   def __init__(					self    :       Tuple ,			_lowercase    :       int ,			_lowercase    :       int = 2 ,			**_lowercase    :       Optional[int]   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tf.keras.layers.ConvaD(
         filters=_lowercase ,			kernel_size=1 ,			strides=_lowercase ,			use_bias=_lowercase ,			name="""convolution"""   )
     SCREAMING_SNAKE_CASE__    							=   tf.keras.layers.BatchNormalization(epsilon=1E-5 ,			momentum=0.9 ,			name="""normalization"""   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       tf.Tensor ,			_lowercase    :       bool = False   ):



     """simple docstring"""






     return self.normalization(self.convolution(_lowercase   ) ,			training=_lowercase   )







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




   def __init__(					self    :       int ,			_lowercase    :       int ,			_lowercase    :       int ,			**_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase ,			name="""pooler"""   )
     SCREAMING_SNAKE_CASE__    							=   [
         tf.keras.layers.ConvaD(filters=_lowercase ,			kernel_size=1 ,			activation="""relu""" ,			name="""attention.0"""   ),
         tf.keras.layers.ConvaD(filters=_lowercase ,			kernel_size=1 ,			activation="""sigmoid""" ,			name="""attention.2"""   ),
     ]




   def 			__a		(					self    :       str ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.pooler(_lowercase   )
     for layer_module in self.attention:
       SCREAMING_SNAKE_CASE__    							=   layer_module(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   hidden_state * pooled
     return hidden_state







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




   def __init__(					self    :       List[str] ,			_lowercase    :       RegNetConfig ,			_lowercase    :       int ,			_lowercase    :       int ,			_lowercase    :       int = 1 ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   in_channels != out_channels or stride != 1
     SCREAMING_SNAKE_CASE__    							=   max(1 ,			out_channels // config.groups_width   )
     SCREAMING_SNAKE_CASE__    							=   (
         TFRegNetShortCut(_lowercase ,			stride=_lowercase ,			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.
     SCREAMING_SNAKE_CASE__    							=   [
         TFRegNetConvLayer(_lowercase ,			kernel_size=1 ,			activation=config.hidden_act ,			name="""layer.0"""   ),
         TFRegNetConvLayer(
             _lowercase ,			stride=_lowercase ,			groups=_lowercase ,			activation=config.hidden_act ,			name="""layer.1"""   ),
         TFRegNetConvLayer(_lowercase ,			kernel_size=1 ,			activation=_lowercase ,			name="""layer.2"""   ),
     ]
     SCREAMING_SNAKE_CASE__    							=   ACTaFN[config.hidden_act]




   def 			__a		(					self    :       int ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   hidden_state
     for layer_module in self.layers:
       SCREAMING_SNAKE_CASE__    							=   layer_module(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.shortcut(_lowercase   )
     hidden_state += residual
     SCREAMING_SNAKE_CASE__    							=   self.activation(_lowercase   )
     return hidden_state







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




   def __init__(					self    :       str ,			_lowercase    :       RegNetConfig ,			_lowercase    :       int ,			_lowercase    :       int ,			_lowercase    :       int = 1 ,			**_lowercase    :       int   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   in_channels != out_channels or stride != 1
     SCREAMING_SNAKE_CASE__    							=   max(1 ,			out_channels // config.groups_width   )
     SCREAMING_SNAKE_CASE__    							=   (
         TFRegNetShortCut(_lowercase ,			stride=_lowercase ,			name="""shortcut"""   )
         if should_apply_shortcut
         else tf.keras.layers.Activation("""linear""" ,			name="""shortcut"""   )
     )
     SCREAMING_SNAKE_CASE__    							=   [
         TFRegNetConvLayer(_lowercase ,			kernel_size=1 ,			activation=config.hidden_act ,			name="""layer.0"""   ),
         TFRegNetConvLayer(
             _lowercase ,			stride=_lowercase ,			groups=_lowercase ,			activation=config.hidden_act ,			name="""layer.1"""   ),
         TFRegNetSELayer(_lowercase ,			reduced_channels=int(round(in_channels / 4   )   ) ,			name="""layer.2"""   ),
         TFRegNetConvLayer(_lowercase ,			kernel_size=1 ,			activation=_lowercase ,			name="""layer.3"""   ),
     ]
     SCREAMING_SNAKE_CASE__    							=   ACTaFN[config.hidden_act]




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   hidden_state
     for layer_module in self.layers:
       SCREAMING_SNAKE_CASE__    							=   layer_module(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.shortcut(_lowercase   )
     hidden_state += residual
     SCREAMING_SNAKE_CASE__    							=   self.activation(_lowercase   )
     return hidden_state







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




   def __init__(					self    :       Optional[int] ,			_lowercase    :       RegNetConfig ,			_lowercase    :       int ,			_lowercase    :       int ,			_lowercase    :       int = 2 ,			_lowercase    :       int = 2 ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






     super().__init__(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
     SCREAMING_SNAKE_CASE__    							=   [
         # downsampling is done in the first layer with stride of 2
         layer(_lowercase ,			_lowercase ,			_lowercase ,			stride=_lowercase ,			name="""layers.0"""   ),
         *[layer(_lowercase ,			_lowercase ,			_lowercase ,			name=f"""layers.{i+1}"""   ) for i in range(depth - 1   )],
     ]




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Tuple   ):



     """simple docstring"""






     for layer_module in self.layers:
       SCREAMING_SNAKE_CASE__    							=   layer_module(_lowercase   )
     return hidden_state







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




   def __init__(					self    :       int ,			_lowercase    :       RegNetConfig ,			**_lowercase    :       int   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   []
     # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
     self.stages.append(
         TFRegNetStage(
             _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""" ,			)   )
     SCREAMING_SNAKE_CASE__    							=   zip(config.hidden_sizes ,			config.hidden_sizes[1:]   )
     for i, ((in_channels, out_channels), depth) in enumerate(zip(_lowercase ,			config.depths[1:]   )   ):
       self.stages.append(TFRegNetStage(_lowercase ,			_lowercase ,			_lowercase ,			depth=_lowercase ,			name=f"""stages.{i+1}"""   )   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       tf.Tensor ,			_lowercase    :       bool = False ,			_lowercase    :       bool = True   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   () if output_hidden_states else None

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

       SCREAMING_SNAKE_CASE__    							=   stage_module(_lowercase   )

     if output_hidden_states:
       SCREAMING_SNAKE_CASE__    							=   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   )







@keras_serializable
class __snake_case       (		tf.keras.layers.Layer	):
   lowerCAmelCase_														=							RegNetConfig




   def __init__(					self    :       Dict ,			_lowercase    :       List[str] ,			**_lowercase    :       Union[str, Any]   ):



     """simple docstring"""






     super().__init__(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   config
     SCREAMING_SNAKE_CASE__    							=   TFRegNetEmbeddings(_lowercase ,			name="""embedder"""   )
     SCREAMING_SNAKE_CASE__    							=   TFRegNetEncoder(_lowercase ,			name="""encoder"""   )
     SCREAMING_SNAKE_CASE__    							=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase ,			name="""pooler"""   )




   @unpack_inputs
   def 			__a		(					self    :       int ,			_lowercase    :       tf.Tensor ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       bool = False ,			):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__    							=   self.embedder(_lowercase ,			training=_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   encoder_outputs[0]
     SCREAMING_SNAKE_CASE__    							=   self.pooler(_lowercase   )

     # Change to NCHW output format have uniformity in the modules
     SCREAMING_SNAKE_CASE__    							=   tf.transpose(_lowercase ,			perm=(0, 3, 1, 2)   )
     SCREAMING_SNAKE_CASE__    							=   tf.transpose(_lowercase ,			perm=(0, 3, 1, 2)   )

     # Change the other hidden state outputs to NCHW as well
     if output_hidden_states:
       SCREAMING_SNAKE_CASE__    							=   tuple([tf.transpose(_lowercase ,			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=_lowercase ,			pooler_output=_lowercase ,			hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states ,			)







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							RegNetConfig
   lowerCAmelCase_														=							"regnet"
   lowerCAmelCase_														=							"pixel_values"




   @property
   def 			__a		(					self    :       str   ):



     """simple docstring"""






     return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24) ,			dtype=tf.floataa   )}





__lowerCamelCase    :				Optional[int]							     =	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.
'''

__lowerCamelCase    :				Optional[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."     ,	lowerCamelCase_     ,	)
class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Dict ,			_lowercase    :       RegNetConfig ,			*_lowercase    :       List[Any] ,			**_lowercase    :       Optional[int]   ):



     """simple docstring"""






     super().__init__(_lowercase ,			*_lowercase ,			**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   TFRegNetMainLayer(_lowercase ,			name="""regnet"""   )




   @unpack_inputs
   @add_start_docstrings_to_model_forward(_lowercase   )
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC ,			output_type=_lowercase ,			config_class=_CONFIG_FOR_DOC ,			modality="""vision""" ,			expected_output=_EXPECTED_OUTPUT_SHAPE ,			)
   def 			__a		(					self    :       str ,			_lowercase    :       tf.Tensor ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       List[Any]=False ,			):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__    							=   self.regnet(
         pixel_values=_lowercase ,			output_hidden_states=_lowercase ,			return_dict=_lowercase ,			training=_lowercase ,			)
     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(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "     ,	lowerCamelCase_     ,	)
class __snake_case       (		lowerCamelCase_     ,	lowerCamelCase_	):




   def __init__(					self    :       Tuple ,			_lowercase    :       RegNetConfig ,			*_lowercase    :       List[str] ,			**_lowercase    :       Tuple   ):



     """simple docstring"""






     super().__init__(_lowercase ,			*_lowercase ,			**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   config.num_labels
     SCREAMING_SNAKE_CASE__    							=   TFRegNetMainLayer(_lowercase ,			name="""regnet"""   )
     # classification head
     SCREAMING_SNAKE_CASE__    							=   [
         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(_lowercase   )
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT ,			output_type=_lowercase ,			config_class=_CONFIG_FOR_DOC ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,			)
   def 			__a		(					self    :       int ,			_lowercase    :       tf.Tensor = None ,			_lowercase    :       tf.Tensor = None ,			_lowercase    :       bool = None ,			_lowercase    :       bool = None ,			_lowercase    :       Optional[int]=False ,			):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__    							=   self.regnet(
         _lowercase ,			output_hidden_states=_lowercase ,			return_dict=_lowercase ,			training=_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   self.classifier[0](_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.classifier[1](_lowercase   )

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

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

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

from __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list			)							->      float:







  """simple docstring"""





  if not nums:
    raise ValueError("""List is empty"""			)
  return sum(__UpperCamelCase			) / len(__UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
