


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__  :Union[str, Any]             =							logging.get_logger(__name__)

# General docstring
lowerCAmelCase__  :int             =							'''RegNetConfig'''

# Base docstring
lowerCAmelCase__  :List[Any]             =							'''facebook/regnet-y-040'''
lowerCAmelCase__  :List[str]             =							[1, 1_0_8_8, 7, 7]

# Image classification docstring
lowerCAmelCase__  :Union[str, Any]             =							'''facebook/regnet-y-040'''
lowerCAmelCase__  :Optional[int]             =							'''tabby, tabby cat'''

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

class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 3      ,   _SCREAMING_SNAKE_CASE = 1      ,   _SCREAMING_SNAKE_CASE = 1      ,   _SCREAMING_SNAKE_CASE = "relu"      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									# The padding and conv has been verified in
									# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
									_UpperCAmelCase					=      tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2						)
									_UpperCAmelCase					=      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'      ,   )
									_UpperCAmelCase					=      tf.keras.layers.BatchNormalization(epsilon=1e-5      ,   momentum=0.9      ,   name='normalization'						)
									_UpperCAmelCase					=      ACTaFN[activation] if activation is not None else tf.identity






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      self.convolution(self.padding(_SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase					=      self.normalization(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.activation(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      config.num_channels
									_UpperCAmelCase					=      TFRegNetConvLayer(
									    out_channels=config.embedding_size      ,   kernel_size=3      ,   stride=2      ,   activation=config.hidden_act      ,   name='embedder'      ,   )






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      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)
									_UpperCAmelCase					=      tf.transpose(_SCREAMING_SNAKE_CASE      ,   perm=(0, 2, 3, 1)						)
									_UpperCAmelCase					=      self.embedder(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 2      ,   **_SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      tf.keras.layers.ConvaD(
									    filters=_SCREAMING_SNAKE_CASE      ,   kernel_size=1      ,   strides=_SCREAMING_SNAKE_CASE      ,   use_bias=_SCREAMING_SNAKE_CASE      ,   name='convolution'						)
									_UpperCAmelCase					=      tf.keras.layers.BatchNormalization(epsilon=1e-5      ,   momentum=0.9      ,   name='normalization'						)






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = False						)			->	tf.Tensor:




									"""simple docstring"""





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



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=_SCREAMING_SNAKE_CASE      ,   name='pooler'						)
									_UpperCAmelCase					=      [
									    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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.pooler(_SCREAMING_SNAKE_CASE						)
									for layer_module in self.attention:
																_UpperCAmelCase					=      layer_module(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      hidden_state * pooled
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 1      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      in_channels != out_channels or stride != 1
									_UpperCAmelCase					=      max(1      ,   out_channels // config.groups_width						)
									_UpperCAmelCase					=      (
									    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.
									_UpperCAmelCase					=      [
									    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'						),
									]
									_UpperCAmelCase					=      ACTaFN[config.hidden_act]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





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



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 1      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      in_channels != out_channels or stride != 1
									_UpperCAmelCase					=      max(1      ,   out_channels // config.groups_width						)
									_UpperCAmelCase					=      (
									    TFRegNetShortCut(_SCREAMING_SNAKE_CASE      ,   stride=_SCREAMING_SNAKE_CASE      ,   name='shortcut'						)
									    if should_apply_shortcut
									    else tf.keras.layers.Activation('linear'      ,   name='shortcut'						)
									)
									_UpperCAmelCase					=      [
									    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'						),
									]
									_UpperCAmelCase					=      ACTaFN[config.hidden_act]






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





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



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = 2      ,   _SCREAMING_SNAKE_CASE = 2      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      TFRegNetXLayer if config.layer_type == 'x' else TFRegNetYLayer
									_UpperCAmelCase					=      [
									    # 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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									for layer_module in self.layers:
																_UpperCAmelCase					=      layer_module(_SCREAMING_SNAKE_CASE						)
									return hidden_state



class 		__a				(    tf.keras.layers.Layer      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      []
									# 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'      ,   )						)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = True						)			->	TFBaseModelOutputWithNoAttention:




									"""simple docstring"""





									_UpperCAmelCase					=      () if output_hidden_states else None

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

																_UpperCAmelCase					=      stage_module(_SCREAMING_SNAKE_CASE						)

									if output_hidden_states:
																_UpperCAmelCase					=      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 		__a				(    tf.keras.layers.Layer      ):
		_a     :  Optional[Any]           =     RegNetConfig
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      config
									_UpperCAmelCase					=      TFRegNetEmbeddings(_SCREAMING_SNAKE_CASE      ,   name='embedder'						)
									_UpperCAmelCase					=      TFRegNetEncoder(_SCREAMING_SNAKE_CASE      ,   name='encoder'						)
									_UpperCAmelCase					=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=_SCREAMING_SNAKE_CASE      ,   name='pooler'						)






		@unpack_inputs
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   )			->	TFBaseModelOutputWithPoolingAndNoAttention:




									"""simple docstring"""





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

									_UpperCAmelCase					=      self.embedder(_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.encoder(
									    _SCREAMING_SNAKE_CASE      ,   output_hidden_states=_SCREAMING_SNAKE_CASE      ,   return_dict=_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      encoder_outputs[0]
									_UpperCAmelCase					=      self.pooler(_SCREAMING_SNAKE_CASE						)

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

									# Change the other hidden state outputs to NCHW as well
									if output_hidden_states:
																_UpperCAmelCase					=      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 		__a				(    UpperCAmelCase      ):
		_a     :  List[Any]           =     RegNetConfig
		_a     :  List[Any]           =     'regnet'
		_a     :  Optional[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						)}



lowerCAmelCase__  :Any             =							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__  :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.'   ,				UpperCAmelCase   ,				)
class 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=False      ,   )			->	Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      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(
    '\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    '   ,				UpperCAmelCase   ,				)
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	str:




									"""simple docstring"""





									super().__init__(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      config.num_labels
									_UpperCAmelCase					=      TFRegNetMainLayer(_SCREAMING_SNAKE_CASE      ,   name='regnet'						)
									# classification head
									_UpperCAmelCase					=      [
									    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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE=False      ,   )			->	Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      self.regnet(
									    _SCREAMING_SNAKE_CASE      ,   output_hidden_states=_SCREAMING_SNAKE_CASE      ,   return_dict=_SCREAMING_SNAKE_CASE      ,   training=_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      self.classifier[0](_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.classifier[1](_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      None if labels is None else self.hf_compute_loss(labels=_SCREAMING_SNAKE_CASE      ,   logits=_SCREAMING_SNAKE_CASE						)

									if not return_dict:
																_UpperCAmelCase					=      (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						)




def 	lowerCAmelCase__			(      a__:		int	)  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      generate_pascal_triangle(a__	)
							for row_idx in range(a__	):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1	):
																					print(end=' '	)
														# Print row values
														for col_idx in range(row_idx + 1	):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]      ,  end=' '	)
																					else:
																												print(triangle[row_idx][col_idx]      ,  end=''	)
														print()

def 	lowerCAmelCase__			(      a__:		int	)  ->		list[list[int]]:


							'''simple docstring'''





							if not isinstance(a__      ,  a__	):
														raise TypeError('The input value of \'num_rows\' should be \'int\''	)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'	)

							_UpperCAmelCase					=      []
							for current_row_idx in range(a__	):
														_UpperCAmelCase					=      populate_current_row(a__      ,  a__	)
														triangle.append(a__	)
							return triangle

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		int	)  ->		list[int]:


							'''simple docstring'''





							_UpperCAmelCase					=      [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							_UpperCAmelCase		,	_UpperCAmelCase					=      1, 1
							for current_col_idx in range(1      ,  a__	):
														calculate_current_element(
														    a__      ,  a__      ,  a__      ,  a__	)
							return current_row

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		list[int]      ,  a__:		int      ,  a__:		int      ,  )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      triangle[current_row_idx - 1][current_col_idx - 1]
							_UpperCAmelCase					=      triangle[current_row_idx - 1][current_col_idx]
							_UpperCAmelCase					=      above_to_left_elt + above_to_right_elt

def 	lowerCAmelCase__			(      a__:		int	)  ->		list[list[int]]:


							'''simple docstring'''





							if not isinstance(a__      ,  a__	):
														raise TypeError('The input value of \'num_rows\' should be \'int\''	)

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'	)

							_UpperCAmelCase					=      [[1]]

							for row_index in range(1      ,  a__	):
														_UpperCAmelCase					=      [0] + result[-1] + [0]
														_UpperCAmelCase					=      row_index + 1
														# Calculate the number of distinct elements in a row
														_UpperCAmelCase					=      sum(divmod(a__      ,  2	)	)
														_UpperCAmelCase					=      [
														    temp_row[i - 1] + temp_row[i] for i in range(1      ,  distinct_elements + 1	)
														]
														_UpperCAmelCase					=      row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														_UpperCAmelCase					=      row_first_half + row_second_half
														result.append(a__	)

							return result

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(a__:		Callable      ,  a__:		int	) -> None:
														_UpperCAmelCase					=      F'''{func.__name__}({value})'''
														_UpperCAmelCase					=      timeit(F'''__main__.{call}'''      ,  setup='import __main__'	)
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F'''{call:38} -- {timing:.4f} seconds'''	)

							for value in range(1_5	):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(a__      ,  a__	)
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()
