



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  				=    logging.get_logger(__name__)

# General docstring
_lowercase  				=    '''RegNetConfig'''

# Base docstring
_lowercase  				=    '''facebook/regnet-y-040'''
_lowercase  				=    [1, 10_88, 7, 7]

# Image classification docstring
_lowercase  				=    '''facebook/regnet-y-040'''
_lowercase  				=    '''tabby, tabby cat'''

_lowercase  				=    [
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 					__A       (			tf.keras.layers.Layer   ):





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





			def 							_snake_case     (self				,     __magic_name__					):
						lowerCamelCase__			:   Any										=		self.convolution(self.padding(snake_case__					)					)
						lowerCamelCase__			:   str										=		self.normalization(snake_case__					)
						lowerCamelCase__			:   int										=		self.activation(snake_case__					)
						return hidden_state







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





			def __init__(self				,     __magic_name__				,     **__magic_name__					):
						super().__init__(**snake_case__					)
						lowerCamelCase__			:   Optional[Any]										=		config.num_channels
						lowerCamelCase__			:   List[str]										=		TFRegNetConvLayer(
						    out_channels=config.embedding_size				,     kernel_size=3				,     stride=2				,     activation=config.hidden_act				,     name="""embedder"""				,     )





			def 							_snake_case     (self				,     __magic_name__					):
						lowerCamelCase__			:   Tuple										=		shape_list(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__			:   List[Any]										=		tf.transpose(snake_case__				,     perm=(0, 2, 3, 1)					)
						lowerCamelCase__			:   Optional[int]										=		self.embedder(snake_case__					)
						return hidden_state







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





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





			def 							_snake_case     (self				,     __magic_name__				,     __magic_name__ = False					):
						return self.normalization(self.convolution(snake_case__					)				,     training=snake_case__					)







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





			def __init__(self				,     __magic_name__				,     __magic_name__				,     **__magic_name__					):
						super().__init__(**snake_case__					)
						lowerCamelCase__			:   int										=		tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__				,     name="""pooler"""					)
						lowerCamelCase__			:   List[Any]										=		[
						    tf.keras.layers.ConvaD(filters=snake_case__				,     kernel_size=1				,     activation="""relu"""				,     name="""attention.0"""					),
						    tf.keras.layers.ConvaD(filters=snake_case__				,     kernel_size=1				,     activation="""sigmoid"""				,     name="""attention.2"""					),
						]





			def 							_snake_case     (self				,     __magic_name__					):
						lowerCamelCase__			:   str										=		self.pooler(snake_case__					)
						for layer_module in self.attention:
									lowerCamelCase__			:   Optional[int]										=		layer_module(snake_case__					)
						lowerCamelCase__			:   Optional[Any]										=		hidden_state * pooled
						return hidden_state







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





			def __init__(self				,     __magic_name__				,     __magic_name__				,     __magic_name__				,     __magic_name__ = 1				,     **__magic_name__					):
						super().__init__(**snake_case__					)
						lowerCamelCase__			:   List[Any]										=		in_channels != out_channels or stride != 1
						lowerCamelCase__			:   Union[str, Any]										=		max(1				,     out_channels // config.groups_width					)
						lowerCamelCase__			:   Optional[int]										=		(
						    TFRegNetShortCut(snake_case__				,     stride=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(snake_case__				,     kernel_size=1				,     activation=config.hidden_act				,     name="""layer.0"""					),
						    TFRegNetConvLayer(
						        snake_case__				,     stride=snake_case__				,     groups=snake_case__				,     activation=config.hidden_act				,     name="""layer.1"""					),
						    TFRegNetConvLayer(snake_case__				,     kernel_size=1				,     activation=snake_case__				,     name="""layer.2"""					),
						]
						lowerCamelCase__			:   List[Any]										=		ACTaFN[config.hidden_act]





			def 							_snake_case     (self				,     __magic_name__					):
						lowerCamelCase__			:   Tuple										=		hidden_state
						for layer_module in self.layers:
									lowerCamelCase__			:   str										=		layer_module(snake_case__					)
						lowerCamelCase__			:   Optional[Any]										=		self.shortcut(snake_case__					)
						hidden_state += residual
						lowerCamelCase__			:   Optional[int]										=		self.activation(snake_case__					)
						return hidden_state







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





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





			def 							_snake_case     (self				,     __magic_name__					):
						lowerCamelCase__			:   Any										=		hidden_state
						for layer_module in self.layers:
									lowerCamelCase__			:   int										=		layer_module(snake_case__					)
						lowerCamelCase__			:   Any										=		self.shortcut(snake_case__					)
						hidden_state += residual
						lowerCamelCase__			:   Optional[Any]										=		self.activation(snake_case__					)
						return hidden_state







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





			def __init__(self				,     __magic_name__				,     __magic_name__				,     __magic_name__				,     __magic_name__ = 2				,     __magic_name__ = 2				,     **__magic_name__					):
						super().__init__(**snake_case__					)

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





			def 							_snake_case     (self				,     __magic_name__					):
						for layer_module in self.layers:
									lowerCamelCase__			:   Tuple										=		layer_module(snake_case__					)
						return hidden_state







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





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





			def 							_snake_case     (self				,     __magic_name__				,     __magic_name__ = False				,     __magic_name__ = True					):
						lowerCamelCase__			:   Union[str, Any]										=		() if output_hidden_states else None

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

									lowerCamelCase__			:   str										=		stage_module(snake_case__					)

						if output_hidden_states:
									lowerCamelCase__			:   Optional[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=snake_case__				,     hidden_states=snake_case__					)







@keras_serializable
class 					__A       (			tf.keras.layers.Layer   ):
			UpperCamelCase       :Optional[int]	 =      RegNetConfig





			def __init__(self				,     __magic_name__				,     **__magic_name__					):
						super().__init__(**snake_case__					)
						lowerCamelCase__			:   Optional[Any]										=		config
						lowerCamelCase__			:   Dict										=		TFRegNetEmbeddings(snake_case__				,     name="""embedder"""					)
						lowerCamelCase__			:   List[str]										=		TFRegNetEncoder(snake_case__				,     name="""encoder"""					)
						lowerCamelCase__			:   Any										=		tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__				,     name="""pooler"""					)





			@unpack_inputs
			def 							_snake_case     (self				,     __magic_name__				,     __magic_name__ = None				,     __magic_name__ = None				,     __magic_name__ = False				,     ):
						lowerCamelCase__			:   Union[str, Any]										=		(
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCamelCase__			:   List[str]										=		return_dict if return_dict is not None else self.config.use_return_dict

						lowerCamelCase__			:   str										=		self.embedder(snake_case__				,     training=snake_case__					)

						lowerCamelCase__			:   Optional[Any]										=		self.encoder(
						    snake_case__				,     output_hidden_states=snake_case__				,     return_dict=snake_case__				,     training=snake_case__					)

						lowerCamelCase__			:   Any										=		encoder_outputs[0]
						lowerCamelCase__			:   List[Any]										=		self.pooler(snake_case__					)

						# Change to NCHW output format have uniformity in the modules
						lowerCamelCase__			:   str										=		tf.transpose(snake_case__				,     perm=(0, 3, 1, 2)					)
						lowerCamelCase__			:   str										=		tf.transpose(snake_case__				,     perm=(0, 3, 1, 2)					)

						# Change the other hidden state outputs to NCHW as well
						if output_hidden_states:
									lowerCamelCase__			:   List[str]										=		tuple([tf.transpose(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=snake_case__				,     pooler_output=snake_case__				,     hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states				,     )







class 					__A       (			lowerCamelCase__   ):
			UpperCamelCase       :List[str]	 =      RegNetConfig
			UpperCamelCase       :Optional[Any]	 =      """regnet"""
			UpperCamelCase       :str	 =      """pixel_values"""





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







_lowercase  				=    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  				=    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 					__A       (			lowerCamelCase__   ):





			def __init__(self				,     __magic_name__				,     *__magic_name__				,     **__magic_name__					):
						super().__init__(snake_case__				,     *snake_case__				,     **snake_case__					)
						lowerCamelCase__			:   int										=		TFRegNetMainLayer(snake_case__				,     name="""regnet"""					)





			@unpack_inputs
			@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				,     )
			def 							_snake_case     (self				,     __magic_name__				,     __magic_name__ = None				,     __magic_name__ = None				,     __magic_name__=False				,     ):
						lowerCamelCase__			:   Optional[int]										=		(
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCamelCase__			:   Dict										=		return_dict if return_dict is not None else self.config.use_return_dict

						lowerCamelCase__			:   List[str]										=		self.regnet(
						    pixel_values=snake_case__				,     output_hidden_states=snake_case__				,     return_dict=snake_case__				,     training=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.
    '''       ,			lowerCamelCase__       ,			)
class 					__A       (			lowerCamelCase__       ,			lowerCamelCase__   ):





			def __init__(self				,     __magic_name__				,     *__magic_name__				,     **__magic_name__					):
						super().__init__(snake_case__				,     *snake_case__				,     **snake_case__					)
						lowerCamelCase__			:   Union[str, Any]										=		config.num_labels
						lowerCamelCase__			:   int										=		TFRegNetMainLayer(snake_case__				,     name="""regnet"""					)
						# classification head
						lowerCamelCase__			:   Optional[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(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				,     )
			def 							_snake_case     (self				,     __magic_name__ = None				,     __magic_name__ = None				,     __magic_name__ = None				,     __magic_name__ = None				,     __magic_name__=False				,     ):
						lowerCamelCase__			:   Optional[Any]										=		(
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCamelCase__			:   str										=		return_dict if return_dict is not None else self.config.use_return_dict

						lowerCamelCase__			:   str										=		self.regnet(
						    snake_case__				,     output_hidden_states=snake_case__				,     return_dict=snake_case__				,     training=snake_case__					)

						lowerCamelCase__			:   str										=		outputs.pooler_output if return_dict else outputs[1]

						lowerCamelCase__			:   Optional[Any]										=		self.classifier[0](snake_case__					)
						lowerCamelCase__			:   str										=		self.classifier[1](snake_case__					)

						lowerCamelCase__			:   Dict										=		None if labels is None else self.hf_compute_loss(labels=snake_case__				,     logits=snake_case__					)

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

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










UpperCAmelCase_							=				{"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
UpperCAmelCase_							=				["""a""", """b""", """c""", """d""", """e"""]





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> Union[str, Any]:

							"""simple docstring"""



							lowercase_	:   Tuple  				=     start
							# add current to visited
							visited.append(lowercase				)
							lowercase_	:   Any  				=     edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					lowercase_	:   List[str]  				=     topological_sort(lowercase				,       lowercase				,       lowercase				)
    # if all neighbors visited add current to sort
							sort.append(lowercase				)
							# if all vertices haven't been visited select a new one to visit
							if len(lowercase				) != len(lowercase				):
														for vertice in vertices:
																					if vertice not in visited:
																												lowercase_	:   Optional[Any]  				=     topological_sort(lowercase				,       lowercase				,       lowercase				)
    # return sort
							return sort


if __name__ == "__main__":
							UpperCAmelCase_							=				topological_sort("""a""", [], [])
							print(sort)