






"""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		:       Any  				=		logging.get_logger(__name__)

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

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

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

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





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




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






					def   A  (				self     :						Union[str, Any]    ,						A     :						List[Any]   )  ->  Any:
							lowercase_ : Union[str, Any]							       =   self.convolution(self.padding(A   )   )
							lowercase_ : Tuple							       =   self.normalization(A   )
							lowercase_ : List[str]							       =   self.activation(A   )
							return hidden_state





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




					def __init__(				self     :						Union[str, Any]    ,						A     :						RegNetConfig    ,						**A     :						Dict   )  ->  Optional[Any]:
							super().__init__(**A   )
							lowercase_ : Any							       =   config.num_channels
							lowercase_ : List[str]							       =   TFRegNetConvLayer(
							    out_channels=config.embedding_size    ,						kernel_size=3    ,						stride=2    ,						activation=config.hidden_act    ,						name='''embedder'''    ,						)






					def   A  (				self     :						Optional[int]    ,						A     :						Dict   )  ->  Optional[Any]:
							lowercase_ : Dict							       =   shape_list(A   )[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)
							lowercase_ : List[str]							       =   tf.transpose(A    ,						perm=(0, 2, 3, 1)   )
							lowercase_ : Union[str, Any]							       =   self.embedder(A   )
							return hidden_state





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




					def __init__(				self     :						Tuple    ,						A     :						int    ,						A     :						int = 2    ,						**A     :						Optional[Any]   )  ->  Tuple:
							super().__init__(**A   )
							lowercase_ : Tuple							       =   tf.keras.layers.ConvaD(
							    filters=A    ,						kernel_size=1    ,						strides=A    ,						use_bias=A    ,						name='''convolution'''   )
							lowercase_ : Tuple							       =   tf.keras.layers.BatchNormalization(epsilon=1e-5    ,						momentum=0.9    ,						name='''normalization'''   )






					def   A  (				self     :						Optional[Any]    ,						A     :						tf.Tensor    ,						A     :						bool = False   )  ->  tf.Tensor:
							return self.normalization(self.convolution(A   )    ,						training=A   )





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




					def __init__(				self     :						Union[str, Any]    ,						A     :						int    ,						A     :						int    ,						**A     :						Union[str, Any]   )  ->  Optional[Any]:
							super().__init__(**A   )
							lowercase_ : Optional[Any]							       =   tf.keras.layers.GlobalAveragePoolingaD(keepdims=A    ,						name='''pooler'''   )
							lowercase_ : Dict							       =   [
							    tf.keras.layers.ConvaD(filters=A    ,						kernel_size=1    ,						activation='''relu'''    ,						name='''attention.0'''   ),
							    tf.keras.layers.ConvaD(filters=A    ,						kernel_size=1    ,						activation='''sigmoid'''    ,						name='''attention.2'''   ),
							]






					def   A  (				self     :						Tuple    ,						A     :						Union[str, Any]   )  ->  List[Any]:
							# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
							lowercase_ : Dict							       =   self.pooler(A   )
							for layer_module in self.attention:
									lowercase_ : List[Any]							       =   layer_module(A   )
							lowercase_ : List[str]							       =   hidden_state * pooled
							return hidden_state





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




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






					def   A  (				self     :						Tuple    ,						A     :						List[Any]   )  ->  Any:
							lowercase_ : str							       =   hidden_state
							for layer_module in self.layers:
									lowercase_ : Union[str, Any]							       =   layer_module(A   )
							lowercase_ : List[Any]							       =   self.shortcut(A   )
							hidden_state += residual
							lowercase_ : Tuple							       =   self.activation(A   )
							return hidden_state





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




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






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





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




					def __init__(				self     :						List[str]    ,						A     :						RegNetConfig    ,						A     :						int    ,						A     :						int    ,						A     :						int = 2    ,						A     :						int = 2    ,						**A     :						Optional[int]   )  ->  Tuple:
							super().__init__(**A   )

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






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





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




					def __init__(				self     :						int    ,						A     :						RegNetConfig    ,						**A     :						str   )  ->  List[str]:
							super().__init__(**A   )
							lowercase_ : 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(
							        A    ,						config.embedding_size    ,						config.hidden_sizes[0]    ,						stride=2 if config.downsample_in_first_stage else 1    ,						depth=config.depths[0]    ,						name='''stages.0'''    ,						)   )
							lowercase_ : Tuple							       =   zip(config.hidden_sizes    ,						config.hidden_sizes[1:]   )
							for i, ((in_channels, out_channels), depth) in enumerate(zip(A    ,						config.depths[1:]   )   ):
									self.stages.append(TFRegNetStage(A    ,						A    ,						A    ,						depth=A    ,						name=F'''stages.{i+1}'''   )   )






					def   A  (				self     :						Dict    ,						A     :						tf.Tensor    ,						A     :						bool = False    ,						A     :						bool = True   )  ->  TFBaseModelOutputWithNoAttention:
							lowercase_ : Dict							       =   () if output_hidden_states else None

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

									lowercase_ : int							       =   stage_module(A   )

							if output_hidden_states:
									lowercase_ : Tuple							       =   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=A    ,						hidden_states=A   )





@keras_serializable
class 					_UpperCAmelCase       ( tf.keras.layers.Layer							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   RegNetConfig




					def __init__(				self     :						Dict    ,						A     :						int    ,						**A     :						Dict   )  ->  Optional[Any]:
							super().__init__(**A   )
							lowercase_ : Optional[Any]							       =   config
							lowercase_ : Any							       =   TFRegNetEmbeddings(A    ,						name='''embedder'''   )
							lowercase_ : str							       =   TFRegNetEncoder(A    ,						name='''encoder'''   )
							lowercase_ : Dict							       =   tf.keras.layers.GlobalAveragePoolingaD(keepdims=A    ,						name='''pooler'''   )






					@unpack_inputs
					def   A  (				self     :						Union[str, Any]    ,						A     :						tf.Tensor    ,						A     :						Optional[bool] = None    ,						A     :						Optional[bool] = None    ,						A     :						bool = False    ,						)  ->  TFBaseModelOutputWithPoolingAndNoAttention:
							lowercase_ : str							       =   (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowercase_ : Dict							       =   return_dict if return_dict is not None else self.config.use_return_dict

							lowercase_ : Optional[int]							       =   self.embedder(A    ,						training=A   )

							lowercase_ : int							       =   self.encoder(
							    A    ,						output_hidden_states=A    ,						return_dict=A    ,						training=A   )

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

							# Change to NCHW output format have uniformity in the modules
							lowercase_ : List[Any]							       =   tf.transpose(A    ,						perm=(0, 3, 1, 2)   )
							lowercase_ : Union[str, Any]							       =   tf.transpose(A    ,						perm=(0, 3, 1, 2)   )

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





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   RegNetConfig
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   "regnet"
					SCREAMING_SNAKE_CASE_		:				str							    =   "pixel_values"




					@property
					def   A  (				self     :						Tuple   )  ->  List[Any]:
							return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)    ,						dtype=tf.floataa   )}
__A		:       Optional[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.
'''

__A		:       int  				=		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 					_UpperCAmelCase       ( _A							):




					def __init__(				self     :						Tuple    ,						A     :						RegNetConfig    ,						*A     :						Union[str, Any]    ,						**A     :						List[str]   )  ->  Optional[Any]:
							super().__init__(A    ,						*A    ,						**A   )
							lowercase_ : Any							       =   TFRegNetMainLayer(A    ,						name='''regnet'''   )






					@unpack_inputs
					@add_start_docstrings_to_model_forward(A   )
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC    ,						output_type=A    ,						config_class=_CONFIG_FOR_DOC    ,						modality='''vision'''    ,						expected_output=_EXPECTED_OUTPUT_SHAPE    ,						)
					def   A  (				self     :						Tuple    ,						A     :						tf.Tensor    ,						A     :						Optional[bool] = None    ,						A     :						Optional[bool] = None    ,						A     :						Dict=False    ,						)  ->  Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:
							lowercase_ : List[Any]							       =   (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowercase_ : Dict							       =   return_dict if return_dict is not None else self.config.use_return_dict

							lowercase_ : List[str]							       =   self.regnet(
							    pixel_values=A    ,						output_hidden_states=A    ,						return_dict=A    ,						training=A    ,						)
							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    "   ,						_A   ,						)
class 					_UpperCAmelCase       ( _A   ,						_A							):




					def __init__(				self     :						Union[str, Any]    ,						A     :						RegNetConfig    ,						*A     :						Optional[Any]    ,						**A     :						Union[str, Any]   )  ->  int:
							super().__init__(A    ,						*A    ,						**A   )
							lowercase_ : Optional[Any]							       =   config.num_labels
							lowercase_ : List[str]							       =   TFRegNetMainLayer(A    ,						name='''regnet'''   )
							# classification head
							lowercase_ : 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(A   )
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,						output_type=A    ,						config_class=_CONFIG_FOR_DOC    ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,						)
					def   A  (				self     :						Optional[Any]    ,						A     :						tf.Tensor = None    ,						A     :						tf.Tensor = None    ,						A     :						bool = None    ,						A     :						bool = None    ,						A     :						Union[str, Any]=False    ,						)  ->  Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
							lowercase_ : Any							       =   (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowercase_ : List[str]							       =   return_dict if return_dict is not None else self.config.use_return_dict

							lowercase_ : Any							       =   self.regnet(
							    A    ,						output_hidden_states=A    ,						return_dict=A    ,						training=A   )

							lowercase_ : List[Any]							       =   outputs.pooler_output if return_dict else outputs[1]

							lowercase_ : Any							       =   self.classifier[0](A   )
							lowercase_ : Dict							       =   self.classifier[1](A   )

							lowercase_ : Union[str, Any]							       =   None if labels is None else self.hf_compute_loss(labels=A    ,						logits=A   )

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

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











"""simple docstring"""



from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
						from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



