



"""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_			   =					logging.get_logger(__name__)

# General docstring
a_			   =					'''RegNetConfig'''

# Base docstring
a_			   =					'''facebook/regnet-y-040'''
a_			   =					[1, 1088, 7, 7]

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

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







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







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					self.convolution(self.padding(lowercase__       )       )
											snake_case_					:		Tuple 					=					self.normalization(lowercase__       )
											snake_case_					:		int 					=					self.activation(lowercase__       )
											return hidden_state







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







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Optional[int] 					=					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)
											snake_case_					:		Union[str, Any] 					=					tf.transpose(lowercase__   ,		perm=(0, 2, 3, 1)       )
											snake_case_					:		Optional[Any] 					=					self.embedder(lowercase__       )
											return hidden_state







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







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = False       ):
											return self.normalization(self.convolution(lowercase__       )   ,		training=lowercase__       )







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







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		**lowercase__       ):
											super().__init__(**lowercase__       )
											snake_case_					:		int 					=					tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowercase__   ,		name="""pooler"""       )
											snake_case_					:		str 					=					[
											    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 						__UpperCamelCase							(self   ,		lowercase__       ):
											# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
											snake_case_					:		int 					=					self.pooler(lowercase__       )
											for layer_module in self.attention:
																snake_case_					:		Union[str, Any] 					=					layer_module(lowercase__       )
											snake_case_					:		List[Any] 					=					hidden_state * pooled
											return hidden_state







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







						"""simple docstring"""







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







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		Tuple 					=					hidden_state
											for layer_module in self.layers:
																snake_case_					:		Tuple 					=					layer_module(lowercase__       )
											snake_case_					:		List[Any] 					=					self.shortcut(lowercase__       )
											hidden_state += residual
											snake_case_					:		List[Any] 					=					self.activation(lowercase__       )
											return hidden_state







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







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = 1   ,		**lowercase__       ):
											super().__init__(**lowercase__       )
											snake_case_					:		str 					=					in_channels != out_channels or stride != 1
											snake_case_					:		List[Any] 					=					max(1   ,		out_channels // config.groups_width       )
											snake_case_					:		Optional[Any] 					=					(
											    TFRegNetShortCut(lowercase__   ,		stride=lowercase__   ,		name="""shortcut"""       )
											    if should_apply_shortcut
											    else tf.keras.layers.Activation("""linear"""   ,		name="""shortcut"""       )
											)
											snake_case_					:		Tuple 					=					[
											    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"""       ),
											]
											snake_case_					:		Optional[Any] 					=					ACTaFN[config.hidden_act]







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







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







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__ = 2   ,		lowercase__ = 2   ,		**lowercase__       ):
											super().__init__(**lowercase__       )

											snake_case_					:		Any 					=					TFRegNetXLayer if config.layer_type == """x""" else TFRegNetYLayer
											snake_case_					:		List[str] 					=					[
											    # 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 						__UpperCamelCase							(self   ,		lowercase__       ):
											for layer_module in self.layers:
																snake_case_					:		str 					=					layer_module(lowercase__       )
											return hidden_state







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







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		**lowercase__       ):
											super().__init__(**lowercase__       )
											snake_case_					:		List[str] 					=					[]
											# 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"""   ,		)       )
											snake_case_					:		Optional[int] 					=					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 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = False   ,		lowercase__ = True       ):
											snake_case_					:		List[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_					:		List[str] 					=					stage_module(lowercase__       )

											if output_hidden_states:
																snake_case_					:		List[Any] 					=					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    __lowercase     (					tf.keras.layers.Layer):







						"""simple docstring"""







						_A		:							Dict							       =		RegNetConfig







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







						@unpack_inputs
						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = False   ,		):
											snake_case_					:		str 					=					(
											    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[int] 					=					self.embedder(lowercase__   ,		training=lowercase__       )

											snake_case_					:		List[Any] 					=					self.encoder(
											    lowercase__   ,		output_hidden_states=lowercase__   ,		return_dict=lowercase__   ,		training=lowercase__       )

											snake_case_					:		Optional[Any] 					=					encoder_outputs[0]
											snake_case_					:		Optional[Any] 					=					self.pooler(lowercase__       )

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

											# Change the other hidden state outputs to NCHW as well
											if output_hidden_states:
																snake_case_					:		Any 					=					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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Any							       =		RegNetConfig
						_A		:							List[Any]							       =		"""regnet"""
						_A		:							Tuple							       =		"""pixel_values"""







						@property
						def 						__UpperCamelCase							(self       ):
											return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)   ,		dtype=tf.floataa       )}
a_			   =					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_			   =					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    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		*lowercase__   ,		**lowercase__       ):
											super().__init__(lowercase__   ,		*lowercase__   ,		**lowercase__       )
											snake_case_					:		Tuple 					=					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 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__=False   ,		):
											snake_case_					:		List[str] 					=					(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											snake_case_					:		int 					=					return_dict if return_dict is not None else self.config.use_return_dict

											snake_case_					:		Dict 					=					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.
    """					,    _UpperCAmelCase					,    )
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase):







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		*lowercase__   ,		**lowercase__       ):
											super().__init__(lowercase__   ,		*lowercase__   ,		**lowercase__       )
											snake_case_					:		Optional[int] 					=					config.num_labels
											snake_case_					:		List[str] 					=					TFRegNetMainLayer(lowercase__   ,		name="""regnet"""       )
											# classification head
											snake_case_					:		Dict 					=					[
											    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 						__UpperCamelCase							(self   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__=False   ,		):
											snake_case_					:		Optional[Any] 					=					(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											snake_case_					:		Union[str, Any] 					=					return_dict if return_dict is not None else self.config.use_return_dict

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

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

											snake_case_					:		Tuple 					=					self.classifier[0](lowercase__       )
											snake_case_					:		Optional[int] 					=					self.classifier[1](lowercase__       )

											snake_case_					:		Any 					=					None if labels is None else self.hf_compute_loss(labels=lowercase__   ,		logits=lowercase__       )

											if not return_dict:
																snake_case_					:		Optional[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       )









"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			   =					logging.get_logger(__name__)

a_			   =					{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[int]							       =		"""dpr"""







						def __init__(self   ,		lowercase__=3_05_22   ,		lowercase__=7_68   ,		lowercase__=12   ,		lowercase__=12   ,		lowercase__=30_72   ,		lowercase__="gelu"   ,		lowercase__=0.1   ,		lowercase__=0.1   ,		lowercase__=5_12   ,		lowercase__=2   ,		lowercase__=0.02   ,		lowercase__=1e-12   ,		lowercase__=0   ,		lowercase__="absolute"   ,		lowercase__ = 0   ,		**lowercase__   ,		):
											super().__init__(pad_token_id=lowercase__   ,		**lowercase__       )

											snake_case_					:		List[Any] 					=					vocab_size
											snake_case_					:		List[str] 					=					hidden_size
											snake_case_					:		Tuple 					=					num_hidden_layers
											snake_case_					:		List[Any] 					=					num_attention_heads
											snake_case_					:		int 					=					hidden_act
											snake_case_					:		Dict 					=					intermediate_size
											snake_case_					:		int 					=					hidden_dropout_prob
											snake_case_					:		Any 					=					attention_probs_dropout_prob
											snake_case_					:		str 					=					max_position_embeddings
											snake_case_					:		List[str] 					=					type_vocab_size
											snake_case_					:		List[str] 					=					initializer_range
											snake_case_					:		Optional[int] 					=					layer_norm_eps
											snake_case_					:		Union[str, Any] 					=					projection_dim
											snake_case_					:		str 					=					position_embedding_type




