



from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


snake_case_         = logging.get_logger(__name__)

# General docstring
snake_case_         = """ResNetConfig"""

# Base docstring
snake_case_         = """microsoft/resnet-50"""
snake_case_         = [1, 2048, 7, 7]

# Image classification docstring
snake_case_         = """microsoft/resnet-50"""
snake_case_         = """tiger cat"""

snake_case_         = [
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







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



						"""simple docstring"""






						def __init__(      self				:Dict     ,						lowercase_				:int     ,						lowercase_				:int = 3     ,						lowercase_				:int = 1     ,						lowercase_				:str = "relu"     ,						**lowercase_				:Tuple      )       ->    None:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				kernel_size // 2
												UpperCAmelCase    =				tf.keras.layers.ConvaD(
												    lowercase_     ,						kernel_size=lowercase_     ,						strides=lowercase_     ,						padding='valid'     ,						use_bias=lowercase_     ,						name='convolution'      )
												# Use same default momentum and epsilon as PyTorch equivalent
												UpperCAmelCase    =				tf.keras.layers.BatchNormalization(epsilon=1E-5     ,						momentum=0.9     ,						name='normalization'      )
												UpperCAmelCase    =				ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'      )






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:tf.Tensor      )       ->    tf.Tensor:
												# Pad to match that done in the PyTorch Conv2D model
												UpperCAmelCase    =				UpperCAmelCase    =				(self.pad_value, self.pad_value)
												UpperCAmelCase    =				tf.pad(lowercase_     ,						[(0, 0), height_pad, width_pad, (0, 0)]      )
												UpperCAmelCase    =				self.conv(lowercase_      )
												return hidden_state






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False      )       ->    tf.Tensor:
												UpperCAmelCase    =				self.convolution(lowercase_      )
												UpperCAmelCase    =				self.normalization(lowercase_     ,						training=lowercase_      )
												UpperCAmelCase    =				self.activation(lowercase_      )
												return hidden_state







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



						"""simple docstring"""






						def __init__(      self				:Optional[int]     ,						lowercase_				:ResNetConfig     ,						**lowercase_				:Optional[int]      )       ->    None:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				TFResNetConvLayer(
												    config.embedding_size     ,						kernel_size=7     ,						stride=2     ,						activation=config.hidden_act     ,						name='embedder'     ,						)
												UpperCAmelCase    =				tf.keras.layers.MaxPoolaD(pool_size=3     ,						strides=2     ,						padding='valid'     ,						name='pooler'      )
												UpperCAmelCase    =				config.num_channels






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False      )       ->    tf.Tensor:
												UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				shape_list(lowercase_      )
												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.'      )
												UpperCAmelCase    =				pixel_values
												UpperCAmelCase    =				self.embedder(lowercase_      )
												UpperCAmelCase    =				tf.pad(lowercase_     ,						[[0, 0], [1, 1], [1, 1], [0, 0]]      )
												UpperCAmelCase    =				self.pooler(lowercase_      )
												return hidden_state







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



						"""simple docstring"""






						def __init__(      self				:Any     ,						lowercase_				:int     ,						lowercase_				:int = 2     ,						**lowercase_				:List[Any]      )       ->    None:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				tf.keras.layers.ConvaD(
												    lowercase_     ,						kernel_size=1     ,						strides=lowercase_     ,						use_bias=lowercase_     ,						name='convolution'      )
												# Use same default momentum and epsilon as PyTorch equivalent
												UpperCAmelCase    =				tf.keras.layers.BatchNormalization(epsilon=1E-5     ,						momentum=0.9     ,						name='normalization'      )






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False      )       ->    tf.Tensor:
												UpperCAmelCase    =				x
												UpperCAmelCase    =				self.convolution(lowercase_      )
												UpperCAmelCase    =				self.normalization(lowercase_     ,						training=lowercase_      )
												return hidden_state







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



						"""simple docstring"""






						def __init__(      self				:Optional[int]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:int = 1     ,						lowercase_				:str = "relu"     ,						**lowercase_				:Tuple      )       ->    None:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				in_channels != out_channels or stride != 1
												UpperCAmelCase    =				TFResNetConvLayer(lowercase_     ,						stride=lowercase_     ,						name='layer.0'      )
												UpperCAmelCase    =				TFResNetConvLayer(lowercase_     ,						activation=lowercase_     ,						name='layer.1'      )
												UpperCAmelCase    =				(
												    TFResNetShortCut(lowercase_     ,						stride=lowercase_     ,						name='shortcut'      )
												    if should_apply_shortcut
												    else tf.keras.layers.Activation('linear'     ,						name='shortcut'      )
												)
												UpperCAmelCase    =				ACTaFN[activation]






						def 			UpperCAmelCase__  (      self				:Optional[int]     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False      )       ->    tf.Tensor:
												UpperCAmelCase    =				hidden_state
												UpperCAmelCase    =				self.conva(lowercase_     ,						training=lowercase_      )
												UpperCAmelCase    =				self.conva(lowercase_     ,						training=lowercase_      )
												UpperCAmelCase    =				self.shortcut(lowercase_     ,						training=lowercase_      )
												hidden_state += residual
												UpperCAmelCase    =				self.activation(lowercase_      )
												return hidden_state







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



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:int = 1     ,						lowercase_				:str = "relu"     ,						lowercase_				:int = 4     ,						**lowercase_				:Optional[int]     ,						)       ->    None:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				in_channels != out_channels or stride != 1
												UpperCAmelCase    =				out_channels // reduction
												UpperCAmelCase    =				TFResNetConvLayer(lowercase_     ,						kernel_size=1     ,						name='layer.0'      )
												UpperCAmelCase    =				TFResNetConvLayer(lowercase_     ,						stride=lowercase_     ,						name='layer.1'      )
												UpperCAmelCase    =				TFResNetConvLayer(lowercase_     ,						kernel_size=1     ,						activation=lowercase_     ,						name='layer.2'      )
												UpperCAmelCase    =				(
												    TFResNetShortCut(lowercase_     ,						stride=lowercase_     ,						name='shortcut'      )
												    if should_apply_shortcut
												    else tf.keras.layers.Activation('linear'     ,						name='shortcut'      )
												)
												UpperCAmelCase    =				ACTaFN[activation]






						def 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False      )       ->    tf.Tensor:
												UpperCAmelCase    =				hidden_state
												UpperCAmelCase    =				self.conva(lowercase_     ,						training=lowercase_      )
												UpperCAmelCase    =				self.conva(lowercase_     ,						training=lowercase_      )
												UpperCAmelCase    =				self.conva(lowercase_     ,						training=lowercase_      )
												UpperCAmelCase    =				self.shortcut(lowercase_     ,						training=lowercase_      )
												hidden_state += residual
												UpperCAmelCase    =				self.activation(lowercase_      )
												return hidden_state







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



						"""simple docstring"""






						def __init__(      self				:Optional[int]     ,						lowercase_				:ResNetConfig     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:int = 2     ,						lowercase_				:int = 2     ,						**lowercase_				:Optional[int]      )       ->    None:
												super().__init__(**lowercase_      )

												UpperCAmelCase    =				TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

												UpperCAmelCase    =				[layer(lowercase_     ,						lowercase_     ,						stride=lowercase_     ,						activation=config.hidden_act     ,						name='layers.0'      )]
												layers += [
												    layer(lowercase_     ,						lowercase_     ,						activation=config.hidden_act     ,						name=f"""layers.{i + 1}"""      )
												    for i in range(depth - 1      )
												]
												UpperCAmelCase    =				layers






						def 			UpperCAmelCase__  (      self				:Optional[Any]     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False      )       ->    tf.Tensor:
												for layer in self.stage_layers:
																		UpperCAmelCase    =				layer(lowercase_     ,						training=lowercase_      )
												return hidden_state







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



						"""simple docstring"""






						def __init__(      self				:int     ,						lowercase_				:ResNetConfig     ,						**lowercase_				:Tuple      )       ->    None:
												super().__init__(**lowercase_      )
												# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
												UpperCAmelCase    =				[
												    TFResNetStage(
												        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'     ,						)
												]
												for i, (in_channels, out_channels, depth) in enumerate(
												    zip(config.hidden_sizes     ,						config.hidden_sizes[1:]     ,						config.depths[1:]      )      ):
																		self.stages.append(TFResNetStage(lowercase_     ,						lowercase_     ,						lowercase_     ,						depth=lowercase_     ,						name=f"""stages.{i + 1}"""      )      )






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:tf.Tensor     ,						lowercase_				:bool = False     ,						lowercase_				:bool = True     ,						lowercase_				:bool = False     ,						)       ->    TFBaseModelOutputWithNoAttention:
												UpperCAmelCase    =				() if output_hidden_states else None

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

																		UpperCAmelCase    =				stage_module(lowercase_     ,						training=lowercase_      )

												if output_hidden_states:
																		UpperCAmelCase    =				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_      )







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							ResNetConfig
						__UpperCamelCase	       =							"""resnet"""
						__UpperCamelCase	       =							"""pixel_values"""






						@property
						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    Optional[int]:
												return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)     ,						dtype=tf.floataa      )}


snake_case_         = R"""
    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.

    Parameters:
        config ([`ResNetConfig`]): 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
            [`ConvNextImageProcessor.__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.
"""







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



						"""simple docstring"""
						__UpperCamelCase	       =							ResNetConfig






						def __init__(      self				:Dict     ,						lowercase_				:ResNetConfig     ,						**lowercase_				:List[Any]      )       ->    None:
												super().__init__(**lowercase_      )
												UpperCAmelCase    =				config
												UpperCAmelCase    =				TFResNetEmbeddings(lowercase_     ,						name='embedder'      )
												UpperCAmelCase    =				TFResNetEncoder(lowercase_     ,						name='encoder'      )
												UpperCAmelCase    =				tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowercase_      )






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

												# TF 2.0 image layers can't use NCHW format when running on CPU.
												# We transpose to NHWC format and then transpose back after the full forward pass.
												# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
												UpperCAmelCase    =				tf.transpose(lowercase_     ,						perm=[0, 2, 3, 1]      )
												UpperCAmelCase    =				self.embedder(lowercase_     ,						training=lowercase_      )

												UpperCAmelCase    =				self.encoder(
												    lowercase_     ,						output_hidden_states=lowercase_     ,						return_dict=lowercase_     ,						training=lowercase_      )

												UpperCAmelCase    =				encoder_outputs[0]

												UpperCAmelCase    =				self.pooler(lowercase_      )

												# Transpose all the outputs to the NCHW format
												# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
												UpperCAmelCase    =				tf.transpose(lowercase_     ,						(0, 3, 1, 2)      )
												UpperCAmelCase    =				tf.transpose(lowercase_     ,						(0, 3, 1, 2)      )
												UpperCAmelCase    =				()
												for hidden_state in encoder_outputs[1:]:
																		UpperCAmelCase    =				hidden_states + tuple(tf.transpose(lowercase_     ,						(0, 3, 1, 2)      ) for h in hidden_state      )

												if not return_dict:
																		return (last_hidden_state, pooled_output) + hidden_states

												UpperCAmelCase    =				hidden_states if output_hidden_states else None

												return TFBaseModelOutputWithPoolingAndNoAttention(
												    last_hidden_state=lowercase_     ,						pooler_output=lowercase_     ,						hidden_states=lowercase_     ,						)







@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""			,     SCREAMING_SNAKE_CASE_			,     )
class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






						def __init__(      self				:Optional[int]     ,						lowercase_				:ResNetConfig     ,						**lowercase_				:Optional[Any]      )       ->    None:
												super().__init__(lowercase_     ,						**lowercase_      )
												UpperCAmelCase    =				TFResNetMainLayer(config=lowercase_     ,						name='resnet'      )






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

												UpperCAmelCase    =				self.resnet(
												    pixel_values=lowercase_     ,						output_hidden_states=lowercase_     ,						return_dict=lowercase_     ,						training=lowercase_     ,						)
												return resnet_outputs







@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """			,     SCREAMING_SNAKE_CASE_			,     )
class    A_    (  SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""






						def __init__(      self				:List[Any]     ,						lowercase_				:ResNetConfig     ,						**lowercase_				:Any      )       ->    None:
												super().__init__(lowercase_     ,						**lowercase_      )
												UpperCAmelCase    =				config.num_labels
												UpperCAmelCase    =				TFResNetMainLayer(lowercase_     ,						name='resnet'      )
												# classification head
												UpperCAmelCase    =				(
												    tf.keras.layers.Dense(config.num_labels     ,						name='classifier.1'      )
												    if config.num_labels > 0
												    else tf.keras.layers.Activation('linear'     ,						name='classifier.1'      )
												)






						def 			UpperCAmelCase__  (      self				:Union[str, Any]     ,						lowercase_				:tf.Tensor      )       ->    tf.Tensor:
												UpperCAmelCase    =				tf.keras.layers.Flatten()(lowercase_      )
												UpperCAmelCase    =				self.classifier_layer(lowercase_      )
												return logits






						@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     ,						)
						@unpack_inputs
						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:tf.Tensor = None     ,						lowercase_				:tf.Tensor = None     ,						lowercase_				:bool = None     ,						lowercase_				:bool = None     ,						lowercase_				:bool = False     ,						)       ->    Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
												UpperCAmelCase    =				return_dict if return_dict is not None else self.config.use_return_dict

												UpperCAmelCase    =				self.resnet(
												    lowercase_     ,						output_hidden_states=lowercase_     ,						return_dict=lowercase_     ,						training=lowercase_      )

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

												UpperCAmelCase    =				self.classifier(lowercase_      )

												UpperCAmelCase    =				None if labels is None else self.hf_compute_loss(lowercase_     ,						lowercase_      )

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

												return TFImageClassifierOutputWithNoAttention(loss=lowercase_     ,						logits=lowercase_     ,						hidden_states=outputs.hidden_states      )










"""simple docstring"""


from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

snake_case_         = TypeVar("""T""", bound=bool)







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:str     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				value
												UpperCAmelCase    =				None
												UpperCAmelCase    =				None






						@property
						def 			UpperCAmelCase__  (      self				:Any      )       ->    T:
												return self._value






						@staticmethod
						def 			UpperCAmelCase__  (      lowercase_				:RandomizedHeapNode[T] | None     ,						lowercase_				:RandomizedHeapNode[T] | None      )       ->    RandomizedHeapNode[T] | None:
												if not roota:
																		return roota

												if not roota:
																		return roota

												if roota.value > roota.value:
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota, roota

												if random.choice([True, False]      ):
																		UpperCAmelCase   ,					UpperCAmelCase    =				roota.right, roota.left

												UpperCAmelCase    =				RandomizedHeapNode.merge(roota.left     ,						lowercase_      )

												return roota







class    A_    (  Generic[T]       ):



						"""simple docstring"""






						def __init__(      self				:Optional[Any]     ,						lowercase_				:Iterable[T] | None = ()      )       ->    None:
												UpperCAmelCase    =				None

												if data:
																		for item in data:
																								self.insert(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:T      )       ->    None:
												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root     ,						RandomizedHeapNode(lowercase_      )      )






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    T | None:
												UpperCAmelCase    =				self.top()

												if self._root is None:
																		return None

												UpperCAmelCase    =				RandomizedHeapNode.merge(self._root.left     ,						self._root.right      )

												return result






						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    T:
												if not self._root:
																		raise IndexError('Can\'t get top element for the empty heap.'      )
												return self._root.value






						def 			UpperCAmelCase__  (      self				:Dict      )       ->    None:
												UpperCAmelCase    =				None






						def 			UpperCAmelCase__  (      self				:Union[str, Any]      )       ->    list[Any]:
												UpperCAmelCase    =				[]
												while self:
																		result.append(self.pop()      )

												return result






						def __bool__(      self				:Optional[Any]      )       ->    bool:
												return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





