


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							:   str						=						logging.get_logger(__name__)

# General docstring
_lowercase							:   List[Any]						=						"""RegNetConfig"""

# Base docstring
_lowercase							:   Any						=						"""facebook/regnet-y-040"""
_lowercase							:   List[Any]						=						[1, 1088, 7, 7]

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

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






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

				"""simple docstring"""







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

				def   _UpperCAmelCase       (    self					, a__ )						->					List[Any]:
					A             =					self.convolution(self.padding(lowerCamelCase_ ) )
					A             =					self.normalization(lowerCamelCase_ )
					A             =					self.activation(lowerCamelCase_ )
					return hidden_state






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

				"""simple docstring"""







				def __init__(    self					, a__					, **a__ )						->					Union[str, Any]:
					super().__init__(**lowerCamelCase_ )
					A             =					config.num_channels
					A             =					TFRegNetConvLayer(
					    out_channels=config.embedding_size					, kernel_size=3					, stride=2					, activation=config.hidden_act					, name="""embedder"""					, )

				def   _UpperCAmelCase       (    self					, a__ )						->					Any:
					A             =					shape_list(lowerCamelCase_ )[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)
					A             =					tf.transpose(lowerCamelCase_					, perm=(0, 2, 3, 1) )
					A             =					self.embedder(lowerCamelCase_ )
					return hidden_state






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

				"""simple docstring"""







				def __init__(    self					, a__					, a__ = 2					, **a__ )						->					int:
					super().__init__(**lowerCamelCase_ )
					A             =					tf.keras.layers.ConvaD(
					    filters=lowerCamelCase_					, kernel_size=1					, strides=lowerCamelCase_					, use_bias=lowerCamelCase_					, name="""convolution""" )
					A             =					tf.keras.layers.BatchNormalization(epsilon=1e-5					, momentum=0.9					, name="""normalization""" )

				def   _UpperCAmelCase       (    self					, a__					, a__ = False )						->					Any:
					return self.normalization(self.convolution(lowerCamelCase_ )					, training=lowerCamelCase_ )






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

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, **a__ )						->					Dict:
					super().__init__(**lowerCamelCase_ )
					A             =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase_					, name="""pooler""" )
					A             =					[
					    tf.keras.layers.ConvaD(filters=lowerCamelCase_					, kernel_size=1					, activation="""relu"""					, name="""attention.0""" ),
					    tf.keras.layers.ConvaD(filters=lowerCamelCase_					, kernel_size=1					, activation="""sigmoid"""					, name="""attention.2""" ),
					]

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
					A             =					self.pooler(lowerCamelCase_ )
					for layer_module in self.attention:
						A             =					layer_module(lowerCamelCase_ )
					A             =					hidden_state * pooled
					return hidden_state






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

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__ = 1					, **a__ )						->					str:
					super().__init__(**lowerCamelCase_ )
					A             =					in_channels != out_channels or stride != 1
					A             =					max(1					, out_channels // config.groups_width )
					A             =					(
					    TFRegNetShortCut(lowerCamelCase_					, stride=lowerCamelCase_					, 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.
					A             =					[
					    TFRegNetConvLayer(lowerCamelCase_					, kernel_size=1					, activation=config.hidden_act					, name="""layer.0""" ),
					    TFRegNetConvLayer(
					        lowerCamelCase_					, stride=lowerCamelCase_					, groups=lowerCamelCase_					, activation=config.hidden_act					, name="""layer.1""" ),
					    TFRegNetConvLayer(lowerCamelCase_					, kernel_size=1					, activation=lowerCamelCase_					, name="""layer.2""" ),
					]
					A             =					ACTaFN[config.hidden_act]

				def   _UpperCAmelCase       (    self					, a__ )						->					Optional[Any]:
					A             =					hidden_state
					for layer_module in self.layers:
						A             =					layer_module(lowerCamelCase_ )
					A             =					self.shortcut(lowerCamelCase_ )
					hidden_state += residual
					A             =					self.activation(lowerCamelCase_ )
					return hidden_state






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

				"""simple docstring"""







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

				def   _UpperCAmelCase       (    self					, a__ )						->					Tuple:
					A             =					hidden_state
					for layer_module in self.layers:
						A             =					layer_module(lowerCamelCase_ )
					A             =					self.shortcut(lowerCamelCase_ )
					hidden_state += residual
					A             =					self.activation(lowerCamelCase_ )
					return hidden_state






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

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__ = 2					, a__ = 2					, **a__ )						->					Optional[Any]:
					super().__init__(**lowerCamelCase_ )

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

				def   _UpperCAmelCase       (    self					, a__ )						->					List[Any]:
					for layer_module in self.layers:
						A             =					layer_module(lowerCamelCase_ )
					return hidden_state






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

				"""simple docstring"""







				def __init__(    self					, a__					, **a__ )						->					Dict:
					super().__init__(**lowerCamelCase_ )
					A             =					[]
					# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
					self.stages.append(
					    TFRegNetStage(
					        lowerCamelCase_					, config.embedding_size					, config.hidden_sizes[0]					, stride=2 if config.downsample_in_first_stage else 1					, depth=config.depths[0]					, name="""stages.0"""					, ) )
					A             =					zip(config.hidden_sizes					, config.hidden_sizes[1:] )
					for i, ((in_channels, out_channels), depth) in enumerate(zip(lowerCamelCase_					, config.depths[1:] ) ):
						self.stages.append(TFRegNetStage(lowerCamelCase_					, lowerCamelCase_					, lowerCamelCase_					, depth=lowerCamelCase_					, name=f'stages.{i+1}' ) )

				def   _UpperCAmelCase       (    self					, a__					, a__ = False					, a__ = True )						->					Dict:
					A             =					() if output_hidden_states else None

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

						A             =					stage_module(lowerCamelCase_ )

					if output_hidden_states:
						A             =					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=lowerCamelCase_					, hidden_states=lowerCamelCase_ )






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

				"""simple docstring"""







				lowerCAmelCase					=			RegNetConfig

				def __init__(    self					, a__					, **a__ )						->					Optional[int]:
					super().__init__(**lowerCamelCase_ )
					A             =					config
					A             =					TFRegNetEmbeddings(lowerCamelCase_					, name="""embedder""" )
					A             =					TFRegNetEncoder(lowerCamelCase_					, name="""encoder""" )
					A             =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase_					, name="""pooler""" )

				@unpack_inputs
				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = None					, a__ = False					, )						->					Optional[int]:
					A             =					(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					A             =					return_dict if return_dict is not None else self.config.use_return_dict

					A             =					self.embedder(lowerCamelCase_					, training=lowerCamelCase_ )

					A             =					self.encoder(
					    lowerCamelCase_					, output_hidden_states=lowerCamelCase_					, return_dict=lowerCamelCase_					, training=lowerCamelCase_ )

					A             =					encoder_outputs[0]
					A             =					self.pooler(lowerCamelCase_ )

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

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






class    _UpperCamelCase     (       _UpperCamelCase					):

				"""simple docstring"""







				lowerCAmelCase					=			RegNetConfig
				lowerCAmelCase					=			'regnet'
				lowerCAmelCase					=			'pixel_values'

				@property
				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)					, dtype=tf.floataa )}
_lowercase							:   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.
"""

_lowercase							:   Dict						=						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    _UpperCamelCase     (       _UpperCamelCase					):

				"""simple docstring"""







				def __init__(    self					, a__					, *a__					, **a__ )						->					Optional[int]:
					super().__init__(lowerCamelCase_					, *lowerCamelCase_					, **lowerCamelCase_ )
					A             =					TFRegNetMainLayer(lowerCamelCase_					, name="""regnet""" )

				@unpack_inputs
				@add_start_docstrings_to_model_forward(lowerCamelCase_ )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC					, output_type=lowerCamelCase_					, config_class=_CONFIG_FOR_DOC					, modality="""vision"""					, expected_output=_EXPECTED_OUTPUT_SHAPE					, )
				def   _UpperCAmelCase       (    self					, a__					, a__ = None					, a__ = None					, a__=False					, )						->					Optional[Any]:
					A             =					(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					A             =					return_dict if return_dict is not None else self.config.use_return_dict

					A             =					self.regnet(
					    pixel_values=lowerCamelCase_					, output_hidden_states=lowerCamelCase_					, return_dict=lowerCamelCase_					, training=lowerCamelCase_					, )
					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    _UpperCamelCase     (       _UpperCamelCase     ,		_UpperCamelCase					):

				"""simple docstring"""







				def __init__(    self					, a__					, *a__					, **a__ )						->					List[Any]:
					super().__init__(lowerCamelCase_					, *lowerCamelCase_					, **lowerCamelCase_ )
					A             =					config.num_labels
					A             =					TFRegNetMainLayer(lowerCamelCase_					, name="""regnet""" )
					# classification head
					A             =					[
					    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(lowerCamelCase_ )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT					, output_type=lowerCamelCase_					, config_class=_CONFIG_FOR_DOC					, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					, )
				def   _UpperCAmelCase       (    self					, a__ = None					, a__ = None					, a__ = None					, a__ = None					, a__=False					, )						->					List[Any]:
					A             =					(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					A             =					return_dict if return_dict is not None else self.config.use_return_dict

					A             =					self.regnet(
					    lowerCamelCase_					, output_hidden_states=lowerCamelCase_					, return_dict=lowerCamelCase_					, training=lowerCamelCase_ )

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

					A             =					self.classifier[0](lowerCamelCase_ )
					A             =					self.classifier[1](lowerCamelCase_ )

					A             =					None if labels is None else self.hf_compute_loss(labels=lowerCamelCase_					, logits=lowerCamelCase_ )

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

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










import enum
import shutil
import sys


__UpperCamelCase,	__UpperCamelCase:	Optional[int] = shutil.get_terminal_size()

__UpperCamelCase:	Optional[Any] = {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}


class       __lowerCAmelCase					(       enum.Enum      ):


	'''simple docstring'''





	_A													=				0
	_A													=				1


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]   ,    _lowercase     : Union[str, Any]=""			)   ->							int:




		'''simple docstring'''
		sys.stdout.write(str(_lowercase			) + end			)
		sys.stdout.flush()


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[Any]   ,    _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]=""			)   ->							str:




		'''simple docstring'''
		forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""   ,    _lowercase			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		forceWrite('\r'			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : str			)   ->							List[Any]:




		'''simple docstring'''
		forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""			)


def       SCREAMING_SNAKE_CASE__  (       )   ->							Dict:




		'''simple docstring'''
		forceWrite(' ' * TERMINAL_WIDTH			)
		reset_cursor()





def       SCREAMING_SNAKE_CASE__  (       )   ->							List[str]:




		'''simple docstring'''
		reset_cursor()
		forceWrite('-' * TERMINAL_WIDTH			)


