


'''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


__snake_case							      =     logging.get_logger(__name__)

# General docstring
__snake_case							      =     '''RegNetConfig'''

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

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

__snake_case							      =     [
    '''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    ,   UpperCamelCase_    ,   UpperCamelCase_ = 3    ,   UpperCamelCase_ = 1    ,   UpperCamelCase_ = 1    ,   UpperCamelCase_ = "relu"    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.convolution(self.padding(UpperCamelCase_							)							)
							UpperCamelCase__   :int												= self.normalization(UpperCamelCase_							)
							UpperCamelCase__   :Tuple												= self.activation(UpperCamelCase_							)
							return hidden_state



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__   :Dict												= config.num_channels
							UpperCamelCase__   :Union[str, Any]												= TFRegNetConvLayer(
							    out_channels=config.embedding_size    ,   kernel_size=3    ,   stride=2    ,   activation=config.hidden_act    ,   name='''embedder'''    ,   )




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= shape_list(UpperCamelCase_							)[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__   :int												= tf.transpose(UpperCamelCase_    ,   perm=(0, 2, 3, 1)							)
							UpperCamelCase__   :Dict												= self.embedder(UpperCamelCase_							)
							return hidden_state



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = 2    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = False							):

							'''simple docstring'''


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



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__   :Tuple												= tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase_    ,   name='''pooler'''							)
							UpperCamelCase__   :str												= [
							    tf.keras.layers.ConvaD(filters=UpperCamelCase_    ,   kernel_size=1    ,   activation='''relu'''    ,   name='''attention.0'''							),
							    tf.keras.layers.ConvaD(filters=UpperCamelCase_    ,   kernel_size=1    ,   activation='''sigmoid'''    ,   name='''attention.2'''							),
							]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= self.pooler(UpperCamelCase_							)
							for layer_module in self.attention:
												UpperCamelCase__   :Tuple												= layer_module(UpperCamelCase_							)
							UpperCamelCase__   :Tuple												= hidden_state * pooled
							return hidden_state



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = 1    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__   :Union[str, Any]												= in_channels != out_channels or stride != 1
							UpperCamelCase__   :Any												= max(1    ,   out_channels // config.groups_width							)
							UpperCamelCase__   :Optional[Any]												= (
							    TFRegNetShortCut(UpperCamelCase_    ,   stride=UpperCamelCase_    ,   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[Any]												= [
							    TFRegNetConvLayer(UpperCamelCase_    ,   kernel_size=1    ,   activation=config.hidden_act    ,   name='''layer.0'''							),
							    TFRegNetConvLayer(
							        UpperCamelCase_    ,   stride=UpperCamelCase_    ,   groups=UpperCamelCase_    ,   activation=config.hidden_act    ,   name='''layer.1'''							),
							    TFRegNetConvLayer(UpperCamelCase_    ,   kernel_size=1    ,   activation=UpperCamelCase_    ,   name='''layer.2'''							),
							]
							UpperCamelCase__   :List[Any]												= ACTaFN[config.hidden_act]




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= hidden_state
							for layer_module in self.layers:
												UpperCamelCase__   :int												= layer_module(UpperCamelCase_							)
							UpperCamelCase__   :Union[str, Any]												= self.shortcut(UpperCamelCase_							)
							hidden_state += residual
							UpperCamelCase__   :str												= self.activation(UpperCamelCase_							)
							return hidden_state



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = 1    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :List[Any]												= hidden_state
							for layer_module in self.layers:
												UpperCamelCase__   :Optional[Any]												= layer_module(UpperCamelCase_							)
							UpperCamelCase__   :Optional[Any]												= self.shortcut(UpperCamelCase_							)
							hidden_state += residual
							UpperCamelCase__   :Dict												= self.activation(UpperCamelCase_							)
							return hidden_state



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_ = 2    ,   UpperCamelCase_ = 2    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_							):

							'''simple docstring'''


							for layer_module in self.layers:
												UpperCamelCase__   :List[str]												= layer_module(UpperCamelCase_							)
							return hidden_state



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

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__   :int												= []
							# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
							self.stages.append(
							    TFRegNetStage(
							        UpperCamelCase_    ,   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__   :int												= zip(config.hidden_sizes    ,   config.hidden_sizes[1:]							)
							for i, ((in_channels, out_channels), depth) in enumerate(zip(UpperCamelCase_    ,   config.depths[1:]							)							):
												self.stages.append(TFRegNetStage(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   depth=UpperCamelCase_    ,   name=F'''stages.{i+1}'''							)							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = True							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= () if output_hidden_states else None

							for stage_module in self.stages:
												if output_hidden_states:
																	UpperCamelCase__   :List[Any]												= hidden_states + (hidden_state,)

												UpperCamelCase__   :Optional[Any]												= stage_module(UpperCamelCase_							)

							if output_hidden_states:
												UpperCamelCase__   :Dict												= 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=UpperCamelCase_    ,   hidden_states=UpperCamelCase_							)



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

		"""simple docstring"""

		_a     =				RegNetConfig




		def __init__(		self    ,   UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(**UpperCamelCase_							)
							UpperCamelCase__   :Any												= config
							UpperCamelCase__   :Dict												= TFRegNetEmbeddings(UpperCamelCase_    ,   name='''embedder'''							)
							UpperCamelCase__   :Any												= TFRegNetEncoder(UpperCamelCase_    ,   name='''encoder'''							)
							UpperCamelCase__   :Union[str, Any]												= tf.keras.layers.GlobalAveragePoolingaD(keepdims=UpperCamelCase_    ,   name='''pooler'''							)




		@unpack_inputs
		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False    ,   ):

							'''simple docstring'''


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

							UpperCamelCase__   :List[str]												= self.embedder(UpperCamelCase_    ,   training=UpperCamelCase_							)

							UpperCamelCase__   :Optional[int]												= self.encoder(
							    UpperCamelCase_    ,   output_hidden_states=UpperCamelCase_    ,   return_dict=UpperCamelCase_    ,   training=UpperCamelCase_							)

							UpperCamelCase__   :Union[str, Any]												= encoder_outputs[0]
							UpperCamelCase__   :str												= self.pooler(UpperCamelCase_							)

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

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



class    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				RegNetConfig
		_a     =				'regnet'
		_a     =				'pixel_values'




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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



__snake_case							      =     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.
'''

__snake_case							      =     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    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_    ,   *UpperCamelCase_    ,   **UpperCamelCase_							)
							UpperCamelCase__   :str												= TFRegNetMainLayer(UpperCamelCase_    ,   name='''regnet'''							)




		@unpack_inputs
		@add_start_docstrings_to_model_forward(UpperCamelCase_							)
		@add_code_sample_docstrings(
		    checkpoint=_CHECKPOINT_FOR_DOC    ,   output_type=UpperCamelCase_    ,   config_class=_CONFIG_FOR_DOC    ,   modality='''vision'''    ,   expected_output=_EXPECTED_OUTPUT_SHAPE    ,   )
		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_=False    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :str												= (
							    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__   :Tuple												= self.regnet(
							    pixel_values=UpperCamelCase_    ,   output_hidden_states=UpperCamelCase_    ,   return_dict=UpperCamelCase_    ,   training=UpperCamelCase_    ,   )
							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    lowercase	(							A__	,		A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


							super().__init__(UpperCamelCase_    ,   *UpperCamelCase_    ,   **UpperCamelCase_							)
							UpperCamelCase__   :List[str]												= config.num_labels
							UpperCamelCase__   :Dict												= TFRegNetMainLayer(UpperCamelCase_    ,   name='''regnet'''							)
							# classification head
							UpperCamelCase__   :str												= [
							    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(UpperCamelCase_							)
		@add_code_sample_docstrings(
		    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,   output_type=UpperCamelCase_    ,   config_class=_CONFIG_FOR_DOC    ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,   )
		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_=False    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							UpperCamelCase__   :List[Any]												= return_dict if return_dict is not None else self.config.use_return_dict

							UpperCamelCase__   :List[str]												= self.regnet(
							    UpperCamelCase_    ,   output_hidden_states=UpperCamelCase_    ,   return_dict=UpperCamelCase_    ,   training=UpperCamelCase_							)

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

							UpperCamelCase__   :Tuple												= self.classifier[0](UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= self.classifier[1](UpperCamelCase_							)

							UpperCamelCase__   :Dict												= None if labels is None else self.hf_compute_loss(labels=UpperCamelCase_    ,   logits=UpperCamelCase_							)

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

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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


__snake_case							      =     {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     ['''DPTFeatureExtractor''']
		__snake_case							      =     ['''DPTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							      =     [
		    '''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DPTForDepthEstimation''',
		    '''DPTForSemanticSegmentation''',
		    '''DPTModel''',
		    '''DPTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

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