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


UpperCAmelCase_           =				logging.get_logger(__name__)

# General docstring
UpperCAmelCase_           =				'''RegNetConfig'''

# Base docstring
UpperCAmelCase_           =				'''facebook/regnet-y-040'''
UpperCAmelCase_           =				[1, 1_0_8_8, 7, 7]

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

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




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





   """simple docstring"""




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



    """simple docstring"""



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



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



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.convolution(self.padding(_lowercase				)				)
    _UpperCamelCase:  Optional[Any]    						=			self.normalization(_lowercase				)
    _UpperCamelCase:  Dict    						=			self.activation(_lowercase				)
    return hidden_state




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





   """simple docstring"""




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



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  List[str]    						=			config.num_channels
    _UpperCamelCase:  List[Any]    						=			TFRegNetConvLayer(
        out_channels=config.embedding_size		,	kernel_size=3		,	stride=2		,	activation=config.hidden_act		,	name='''embedder'''		,	)



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



    """simple docstring"""



    _UpperCamelCase:  str    						=			shape_list(_lowercase				)[1]
    if tf.executing_eagerly() and num_channels != self.num_channels:
     raise ValueError(
         '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''				)

    # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
    # So change the input format from `NCHW` to `NHWC`.
    # shape = (batch_size, in_height, in_width, in_channels=num_channels)
    _UpperCamelCase:  List[Any]    						=			tf.transpose(_lowercase		,	perm=(0, 2, 3, 1)				)
    _UpperCamelCase:  List[str]    						=			self.embedder(_lowercase				)
    return hidden_state




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





   """simple docstring"""




   def __init__(						self       :	str		,	_lowercase       :	int		,	_lowercase       :	int = 2		,	**_lowercase       :	List[Any]				):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			tf.keras.layers.ConvaD(
        filters=_lowercase		,	kernel_size=1		,	strides=_lowercase		,	use_bias=_lowercase		,	name='''convolution'''				)
    _UpperCamelCase:  Any    						=			tf.keras.layers.BatchNormalization(epsilon=1E-5		,	momentum=0.9		,	name='''normalization'''				)



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	tf.Tensor		,	_lowercase       :	bool = False				):



    """simple docstring"""



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




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





   """simple docstring"""




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



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	int		,	_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			self.pooler(_lowercase				)
    for layer_module in self.attention:
     _UpperCamelCase:  Tuple    						=			layer_module(_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			hidden_state * pooled
    return hidden_state




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





   """simple docstring"""




   def __init__(						self       :	Optional[int]		,	_lowercase       :	RegNetConfig		,	_lowercase       :	int		,	_lowercase       :	int		,	_lowercase       :	int = 1		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  List[str]    						=			in_channels != out_channels or stride != 1
    _UpperCamelCase:  Tuple    						=			max(1		,	out_channels // config.groups_width				)
    _UpperCamelCase:  Union[str, Any]    						=			(
        TFRegNetShortCut(_lowercase		,	stride=_lowercase		,	name='''shortcut'''				)
        if should_apply_shortcut
        else tf.keras.layers.Activation('''linear'''		,	name='''shortcut'''				)
    )
    # `self.layers` instead of `self.layer` because that is a reserved argument.
    _UpperCamelCase:  Optional[int]    						=			[
        TFRegNetConvLayer(_lowercase		,	kernel_size=1		,	activation=config.hidden_act		,	name='''layer.0'''				),
        TFRegNetConvLayer(
            _lowercase		,	stride=_lowercase		,	groups=_lowercase		,	activation=config.hidden_act		,	name='''layer.1'''				),
        TFRegNetConvLayer(_lowercase		,	kernel_size=1		,	activation=_lowercase		,	name='''layer.2'''				),
    ]
    _UpperCamelCase:  str    						=			ACTaFN[config.hidden_act]



   def 	lowerCAmelCase    (						self       :	int		,	_lowercase       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			hidden_state
    for layer_module in self.layers:
     _UpperCamelCase:  Optional[Any]    						=			layer_module(_lowercase				)
    _UpperCamelCase:  Tuple    						=			self.shortcut(_lowercase				)
    hidden_state += residual
    _UpperCamelCase:  List[str]    						=			self.activation(_lowercase				)
    return hidden_state




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





   """simple docstring"""




   def __init__(						self       :	int		,	_lowercase       :	RegNetConfig		,	_lowercase       :	int		,	_lowercase       :	int		,	_lowercase       :	int = 1		,	**_lowercase       :	Optional[Any]				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			hidden_state
    for layer_module in self.layers:
     _UpperCamelCase:  Tuple    						=			layer_module(_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			self.shortcut(_lowercase				)
    hidden_state += residual
    _UpperCamelCase:  Dict    						=			self.activation(_lowercase				)
    return hidden_state




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





   """simple docstring"""




   def __init__(						self       :	Any		,	_lowercase       :	RegNetConfig		,	_lowercase       :	int		,	_lowercase       :	int		,	_lowercase       :	int = 2		,	_lowercase       :	int = 2		,	**_lowercase       :	Tuple				):



    """simple docstring"""



    super().__init__(**_lowercase				)

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



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



    """simple docstring"""



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




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





   """simple docstring"""




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



    """simple docstring"""



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



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



    """simple docstring"""



    _UpperCamelCase:  int    						=			() if output_hidden_states else None

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

     _UpperCamelCase:  Optional[int]    						=			stage_module(_lowercase				)

    if output_hidden_states:
     _UpperCamelCase:  str    						=			hidden_states + (hidden_state,)

    if not return_dict:
     return tuple(v for v in [hidden_state, hidden_states] if v is not None				)

    return TFBaseModelOutputWithNoAttention(last_hidden_state=_lowercase		,	hidden_states=_lowercase				)




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





   """simple docstring"""




   lowerCAmelCase   :     Any	      = RegNetConfig



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



    """simple docstring"""



    super().__init__(**_lowercase				)
    _UpperCamelCase:  List[Any]    						=			config
    _UpperCamelCase:  List[Any]    						=			TFRegNetEmbeddings(_lowercase		,	name='''embedder'''				)
    _UpperCamelCase:  Dict    						=			TFRegNetEncoder(_lowercase		,	name='''encoder'''				)
    _UpperCamelCase:  Dict    						=			tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase		,	name='''pooler'''				)



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



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    _UpperCamelCase:  Optional[int]    						=			return_dict if return_dict is not None else self.config.use_return_dict

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

    _UpperCamelCase:  Union[str, Any]    						=			self.encoder(
        _lowercase		,	output_hidden_states=_lowercase		,	return_dict=_lowercase		,	training=_lowercase				)

    _UpperCamelCase:  Optional[int]    						=			encoder_outputs[0]
    _UpperCamelCase:  Dict    						=			self.pooler(_lowercase				)

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

    # Change the other hidden state outputs to NCHW as well
    if output_hidden_states:
     _UpperCamelCase:  Tuple    						=			tuple([tf.transpose(_lowercase		,	perm=(0, 3, 1, 2)				) for h in encoder_outputs[1]]				)

    if not return_dict:
     return (last_hidden_state, pooled_output) + encoder_outputs[1:]

    return TFBaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=_lowercase		,	pooler_output=_lowercase		,	hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states		,	)




class 			__magic_name__	(		__a     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = RegNetConfig
   lowerCAmelCase   :     int	      = '''regnet'''
   lowerCAmelCase   :     List[str]	      = '''pixel_values'''



   @property
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



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




UpperCAmelCase_           =				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.
'''

UpperCAmelCase_           =				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.'''   ,						__a   ,						)
class 			__magic_name__	(		__a     ):





   """simple docstring"""




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



    """simple docstring"""



    super().__init__(_lowercase		,	*_lowercase		,	**_lowercase				)
    _UpperCamelCase:  Optional[int]    						=			TFRegNetMainLayer(_lowercase		,	name='''regnet'''				)



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



    """simple docstring"""



    _UpperCamelCase:  int    						=			(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    _UpperCamelCase:  Dict    						=			return_dict if return_dict is not None else self.config.use_return_dict

    _UpperCamelCase:  List[str]    						=			self.regnet(
        pixel_values=_lowercase		,	output_hidden_states=_lowercase		,	return_dict=_lowercase		,	training=_lowercase		,	)
    if not return_dict:
     return (outputs[0],) + outputs[1:]

    return TFBaseModelOutputWithPoolingAndNoAttention(
        last_hidden_state=outputs.last_hidden_state		,	pooler_output=outputs.pooler_output		,	hidden_states=outputs.hidden_states		,	)




@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''   ,						__a   ,						)
class 			__magic_name__	(		__a   ,						__a     ):





   """simple docstring"""




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



    """simple docstring"""



    super().__init__(_lowercase		,	*_lowercase		,	**_lowercase				)
    _UpperCamelCase:  Dict    						=			config.num_labels
    _UpperCamelCase:  Union[str, Any]    						=			TFRegNetMainLayer(_lowercase		,	name='''regnet'''				)
    # classification head
    _UpperCamelCase:  List[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(_lowercase				)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT		,	output_type=_lowercase		,	config_class=_CONFIG_FOR_DOC		,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,	)
   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	tf.Tensor = None		,	_lowercase       :	tf.Tensor = None		,	_lowercase       :	bool = None		,	_lowercase       :	bool = None		,	_lowercase       :	Union[str, Any]=False		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			(
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    _UpperCamelCase:  Dict    						=			return_dict if return_dict is not None else self.config.use_return_dict

    _UpperCamelCase:  Optional[int]    						=			self.regnet(
        _lowercase		,	output_hidden_states=_lowercase		,	return_dict=_lowercase		,	training=_lowercase				)

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

    _UpperCamelCase:  Dict    						=			self.classifier[0](_lowercase				)
    _UpperCamelCase:  int    						=			self.classifier[1](_lowercase				)

    _UpperCamelCase:  Optional[int]    						=			None if labels is None else self.hf_compute_loss(labels=_lowercase		,	logits=_lowercase				)

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

    return TFSequenceClassifierOutput(loss=_lowercase		,	logits=_lowercase		,	hidden_states=outputs.hidden_states				)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_           =				{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''EncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''TFEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_encoder_decoder import EncoderDecoderConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_encoder_decoder import EncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)