



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


_lowerCAmelCase	:   Any    =   logging.get_logger(__name__)

# General docstring
_lowerCAmelCase	:   Union[str, Any]    =   """RegNetConfig"""

# Base docstring
_lowerCAmelCase	:   str    =   """facebook/regnet-y-040"""
_lowerCAmelCase	:   Dict    =   [1, 1_0_8_8, 7, 7]

# Image classification docstring
_lowerCAmelCase	:   int    =   """facebook/regnet-y-040"""
_lowerCAmelCase	:   Optional[int]    =   """tabby, tabby cat"""

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


class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,a_ = 3    ,a_ = 1    ,a_ = 1    ,a_ = "relu"    ,**a_    ,):







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



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.convolution(self.padding(a_						)						)
						lowerCAmelCase__		  =  self.normalization(a_						)
						lowerCAmelCase__		  =  self.activation(a_						)
						return hidden_state



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  config.num_channels
						lowerCAmelCase__		  =  TFRegNetConvLayer(
						    out_channels=config.embedding_size    ,kernel_size=3    ,stride=2    ,activation=config.hidden_act    ,name='embedder'    ,)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  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)
						lowerCAmelCase__		  =  tf.transpose(a_    ,perm=(0, 2, 3, 1)						)
						lowerCAmelCase__		  =  self.embedder(a_						)
						return hidden_state



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,a_ = 2    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  tf.keras.layers.ConvaD(
						    filters=a_    ,kernel_size=1    ,strides=a_    ,use_bias=a_    ,name='convolution'						)
						lowerCAmelCase__		  =  tf.keras.layers.BatchNormalization(epsilon=1e-5    ,momentum=0.9    ,name='normalization'						)



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = False						):







						"""simple docstring"""
						return self.normalization(self.convolution(a_						)    ,training=a_						)



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,a_    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_    ,name='pooler'						)
						lowerCAmelCase__		  =  [
						    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 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
						lowerCAmelCase__		  =  self.pooler(a_						)
						for layer_module in self.attention:
										lowerCAmelCase__		  =  layer_module(a_						)
						lowerCAmelCase__		  =  hidden_state * pooled
						return hidden_state



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_ = 1    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  in_channels != out_channels or stride != 1
						lowerCAmelCase__		  =  max(1    ,out_channels // config.groups_width						)
						lowerCAmelCase__		  =  (
						    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.
						lowerCAmelCase__		  =  [
						    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'						),
						]
						lowerCAmelCase__		  =  ACTaFN[config.hidden_act]



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  hidden_state
						for layer_module in self.layers:
										lowerCAmelCase__		  =  layer_module(a_						)
						lowerCAmelCase__		  =  self.shortcut(a_						)
						hidden_state += residual
						lowerCAmelCase__		  =  self.activation(a_						)
						return hidden_state



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_ = 1    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  in_channels != out_channels or stride != 1
						lowerCAmelCase__		  =  max(1    ,out_channels // config.groups_width						)
						lowerCAmelCase__		  =  (
						    TFRegNetShortCut(a_    ,stride=a_    ,name='shortcut'						)
						    if should_apply_shortcut
						    else tf.keras.layers.Activation('linear'    ,name='shortcut'						)
						)
						lowerCAmelCase__		  =  [
						    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'						),
						]
						lowerCAmelCase__		  =  ACTaFN[config.hidden_act]



		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  hidden_state
						for layer_module in self.layers:
										lowerCAmelCase__		  =  layer_module(a_						)
						lowerCAmelCase__		  =  self.shortcut(a_						)
						hidden_state += residual
						lowerCAmelCase__		  =  self.activation(a_						)
						return hidden_state



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,a_    ,a_    ,a_ = 2    ,a_ = 2    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)

						lowerCAmelCase__		  =  TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
						lowerCAmelCase__		  =  [
						    # 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 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						for layer_module in self.layers:
										lowerCAmelCase__		  =  layer_module(a_						)
						return hidden_state



class 	__snake_case   ( tf.keras.layers.Layer ):
		def __init__(							self    ,a_    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  []
						# 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'    ,)						)
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = False    ,a_ = True						):







						"""simple docstring"""
						lowerCAmelCase__		  =  () if output_hidden_states else None

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

										lowerCAmelCase__		  =  stage_module(a_						)

						if output_hidden_states:
										lowerCAmelCase__		  =  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 	__snake_case   ( tf.keras.layers.Layer ):
		SCREAMING_SNAKE_CASE__								=	RegNetConfig
		def __init__(							self    ,a_    ,**a_						):







						"""simple docstring"""
						super().__init__(**a_						)
						lowerCAmelCase__		  =  config
						lowerCAmelCase__		  =  TFRegNetEmbeddings(a_    ,name='embedder'						)
						lowerCAmelCase__		  =  TFRegNetEncoder(a_    ,name='encoder'						)
						lowerCAmelCase__		  =  tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_    ,name='pooler'						)



		@unpack_inputs
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None    ,a_ = None    ,a_ = False    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCAmelCase__		  =  return_dict if return_dict is not None else self.config.use_return_dict

						lowerCAmelCase__		  =  self.embedder(a_    ,training=a_						)

						lowerCAmelCase__		  =  self.encoder(
						    a_    ,output_hidden_states=a_    ,return_dict=a_    ,training=a_						)

						lowerCAmelCase__		  =  encoder_outputs[0]
						lowerCAmelCase__		  =  self.pooler(a_						)

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

						# Change the other hidden state outputs to NCHW as well
						if output_hidden_states:
										lowerCAmelCase__		  =  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 	__snake_case   ( __snake_case ):
		SCREAMING_SNAKE_CASE__								=	RegNetConfig
		SCREAMING_SNAKE_CASE__								=	'regnet'
		SCREAMING_SNAKE_CASE__								=	'pixel_values'
		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







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



_lowerCAmelCase	:   List[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.
"""

_lowerCAmelCase	:   str    =   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.'							, __snake_case							, )
class 	__snake_case   ( __snake_case ):
		def __init__(							self    ,a_    ,*a_    ,**a_						):







						"""simple docstring"""
						super().__init__(a_    ,*a_    ,**a_						)
						lowerCAmelCase__		  =  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 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_ = None    ,a_ = None    ,a_=False    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCAmelCase__		  =  return_dict if return_dict is not None else self.config.use_return_dict

						lowerCAmelCase__		  =  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    '							, __snake_case							, )
class 	__snake_case   ( __snake_case							, __snake_case ):
		def __init__(							self    ,a_    ,*a_    ,**a_						):







						"""simple docstring"""
						super().__init__(a_    ,*a_    ,**a_						)
						lowerCAmelCase__		  =  config.num_labels
						lowerCAmelCase__		  =  TFRegNetMainLayer(a_    ,name='regnet'						)
						# classification head
						lowerCAmelCase__		  =  [
						    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 						SCREAMING_SNAKE_CASE_					(							self    ,a_ = None    ,a_ = None    ,a_ = None    ,a_ = None    ,a_=False    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						lowerCAmelCase__		  =  return_dict if return_dict is not None else self.config.use_return_dict

						lowerCAmelCase__		  =  self.regnet(
						    a_    ,output_hidden_states=a_    ,return_dict=a_    ,training=a_						)

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

						lowerCAmelCase__		  =  self.classifier[0](a_						)
						lowerCAmelCase__		  =  self.classifier[1](a_						)

						lowerCAmelCase__		  =  None if labels is None else self.hf_compute_loss(labels=a_    ,logits=a_						)

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

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


import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCamelCase_						:     List[str]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):






	def __init__(self				: int      ,	*snake_case				: Optional[Any]      ,	**snake_case				: Optional[Any]    ) ->			None:
							warnings.warn(
							    "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
							    " use FlavaImageProcessor instead."      ,	snake_case      ,	)
							super().__init__(*snake_case      ,	**snake_case    )




