


'''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


__A	:	Optional[int]       =     logging.get_logger(__name__)

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

# Base docstring
__A	:	List[Any]       =     'facebook/regnet-y-040'
__A	:	str       =     [1, 1_088, 7, 7]

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

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





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





							def 		a__   (  self		:Optional[int]  ,_UpperCamelCase		:Tuple			):
									snake_case_		:							List[Any]			       =						self.convolution(self.padding(_UpperCamelCase			)			)
									snake_case_		:							Optional[int]			       =						self.normalization(_UpperCamelCase			)
									snake_case_		:							int			       =						self.activation(_UpperCamelCase			)
									return hidden_state





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





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





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:str			):
									snake_case_		:							str			       =						shape_list(_UpperCamelCase			)[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)
									snake_case_		:							Optional[Any]			       =						tf.transpose(_UpperCamelCase  ,perm=(0, 2, 3, 1)			)
									snake_case_		:							Optional[Any]			       =						self.embedder(_UpperCamelCase			)
									return hidden_state





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





							def __init__(  self		:Dict  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int = 2  ,**_UpperCamelCase		:Tuple			):
									super().__init__(**_UpperCamelCase			)
									snake_case_		:							Tuple			       =						tf.keras.layers.ConvaD(
									    filters=_UpperCamelCase  ,kernel_size=1  ,strides=_UpperCamelCase  ,use_bias=_UpperCamelCase  ,name="""convolution"""			)
									snake_case_		:							int			       =						tf.keras.layers.BatchNormalization(epsilon=1E-5  ,momentum=0.9  ,name="""normalization"""			)





							def 		a__   (  self		:Union[str, Any]  ,_UpperCamelCase		:tf.Tensor  ,_UpperCamelCase		:bool = False			):
									return self.normalization(self.convolution(_UpperCamelCase			)  ,training=_UpperCamelCase			)





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





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





							def 		a__   (  self		:Any  ,_UpperCamelCase		:Dict			):
									# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
									snake_case_		:							Optional[int]			       =						self.pooler(_UpperCamelCase			)
									for layer_module in self.attention:
											snake_case_		:							Optional[int]			       =						layer_module(_UpperCamelCase			)
									snake_case_		:							str			       =						hidden_state * pooled
									return hidden_state





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





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





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:int			):
									snake_case_		:							Optional[Any]			       =						hidden_state
									for layer_module in self.layers:
											snake_case_		:							List[Any]			       =						layer_module(_UpperCamelCase			)
									snake_case_		:							str			       =						self.shortcut(_UpperCamelCase			)
									hidden_state += residual
									snake_case_		:							List[str]			       =						self.activation(_UpperCamelCase			)
									return hidden_state





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





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





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Dict			       =						hidden_state
									for layer_module in self.layers:
											snake_case_		:							int			       =						layer_module(_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						self.shortcut(_UpperCamelCase			)
									hidden_state += residual
									snake_case_		:							List[Any]			       =						self.activation(_UpperCamelCase			)
									return hidden_state





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





							def __init__(  self		:str  ,_UpperCamelCase		:RegNetConfig  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int = 2  ,_UpperCamelCase		:int = 2  ,**_UpperCamelCase		:Dict			):
									super().__init__(**_UpperCamelCase			)

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





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:Any			):
									for layer_module in self.layers:
											snake_case_		:							Optional[int]			       =						layer_module(_UpperCamelCase			)
									return hidden_state





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





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





							def 		a__   (  self		:Any  ,_UpperCamelCase		:tf.Tensor  ,_UpperCamelCase		:bool = False  ,_UpperCamelCase		:bool = True			):
									snake_case_		:							Union[str, Any]			       =						() if output_hidden_states else None

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

											snake_case_		:							Optional[int]			       =						stage_module(_UpperCamelCase			)

									if output_hidden_states:
											snake_case_		:							Dict			       =						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=_UpperCamelCase  ,hidden_states=_UpperCamelCase			)





@keras_serializable
class 						__UpperCamelCase	(						tf.keras.layers.Layer							):
							lowercase			:		List[str]   		=  RegNetConfig





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:Optional[int]  ,**_UpperCamelCase		:Optional[Any]			):
									super().__init__(**_UpperCamelCase			)
									snake_case_		:							int			       =						config
									snake_case_		:							Union[str, Any]			       =						TFRegNetEmbeddings(_UpperCamelCase  ,name="""embedder"""			)
									snake_case_		:							List[str]			       =						TFRegNetEncoder(_UpperCamelCase  ,name="""encoder"""			)
									snake_case_		:							Union[str, Any]			       =						tf.keras.layers.GlobalAveragePoolingaD(keepdims=_UpperCamelCase  ,name="""pooler"""			)





							@unpack_inputs
							def 		a__   (  self		:Tuple  ,_UpperCamelCase		:tf.Tensor  ,_UpperCamelCase		:Optional[bool] = None  ,_UpperCamelCase		:Optional[bool] = None  ,_UpperCamelCase		:bool = False  ,):
									snake_case_		:							Optional[int]			       =						(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									snake_case_		:							Optional[int]			       =						return_dict if return_dict is not None else self.config.use_return_dict

									snake_case_		:							str			       =						self.embedder(_UpperCamelCase  ,training=_UpperCamelCase			)

									snake_case_		:							int			       =						self.encoder(
									    _UpperCamelCase  ,output_hidden_states=_UpperCamelCase  ,return_dict=_UpperCamelCase  ,training=_UpperCamelCase			)

									snake_case_		:							Optional[int]			       =						encoder_outputs[0]
									snake_case_		:							Union[str, Any]			       =						self.pooler(_UpperCamelCase			)

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

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





class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  RegNetConfig
							lowercase			:		Optional[Any]   		=  'regnet'
							lowercase			:		Tuple   		=  'pixel_values'





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





__A	:	Dict       =     r'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

__A	:	Optional[Any]       =     r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'						,  lowercase__						,  )
class 						__UpperCamelCase	(						lowercase__							):





							def __init__(  self		:str  ,_UpperCamelCase		:RegNetConfig  ,*_UpperCamelCase		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									super().__init__(_UpperCamelCase  ,*_UpperCamelCase  ,**_UpperCamelCase			)
									snake_case_		:							List[Any]			       =						TFRegNetMainLayer(_UpperCamelCase  ,name="""regnet"""			)





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

									snake_case_		:							Optional[Any]			       =						self.regnet(
									    pixel_values=_UpperCamelCase  ,output_hidden_states=_UpperCamelCase  ,return_dict=_UpperCamelCase  ,training=_UpperCamelCase  ,)
									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    '						,  lowercase__						,  )
class 						__UpperCamelCase	(						lowercase__						,  lowercase__							):





							def __init__(  self		:Dict  ,_UpperCamelCase		:RegNetConfig  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:List[str]			):
									super().__init__(_UpperCamelCase  ,*_UpperCamelCase  ,**_UpperCamelCase			)
									snake_case_		:							List[Any]			       =						config.num_labels
									snake_case_		:							int			       =						TFRegNetMainLayer(_UpperCamelCase  ,name="""regnet"""			)
									# classification head
									snake_case_		:							Any			       =						[
									    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(_UpperCamelCase			)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT  ,output_type=_UpperCamelCase  ,config_class=_CONFIG_FOR_DOC  ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,)
							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:tf.Tensor = None  ,_UpperCamelCase		:tf.Tensor = None  ,_UpperCamelCase		:bool = None  ,_UpperCamelCase		:bool = None  ,_UpperCamelCase		:Dict=False  ,):
									snake_case_		:							Optional[Any]			       =						(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									snake_case_		:							Any			       =						return_dict if return_dict is not None else self.config.use_return_dict

									snake_case_		:							List[Any]			       =						self.regnet(
									    _UpperCamelCase  ,output_hidden_states=_UpperCamelCase  ,return_dict=_UpperCamelCase  ,training=_UpperCamelCase			)

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

									snake_case_		:							Optional[int]			       =						self.classifier[0](_UpperCamelCase			)
									snake_case_		:							Union[str, Any]			       =						self.classifier[1](_UpperCamelCase			)

									snake_case_		:							int			       =						None if labels is None else self.hf_compute_loss(labels=_UpperCamelCase  ,logits=_UpperCamelCase			)

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

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



'''simple docstring'''






import random
from typing import Any





def      UpperCAmelCase      (		lowerCamelCase_  :list ):







		'''simple docstring'''
		for _ in range(len(lowerCamelCase_ ) ):
				snake_case_		:							Union[str, Any]			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_		:							Any			       =						random.randint(0				,      len(lowerCamelCase_ ) - 1 )
				snake_case_       ,      snake_case_		:							List[str]			       =						data[b], data[a]
		return data


if __name__ == "__main__":
							__A	:	Optional[int]     =     [0, 1, 2, 3, 4, 5, 6, 7]
							__A	:	Optional[int]     =     ['python', 'says', 'hello', '!']
							print('Fisher-Yates Shuffle:')
							print('List', integers, strings)
							print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))