






"""simple docstring"""







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


a :				Dict      					=		logging.get_logger(__name__)

# General docstring
a :				Any      					=		"""ResNetConfig"""

# Base docstring
a :				List[Any]      					=		"""microsoft/resnet-50"""
a :				Any      					=		[1, 2048, 7, 7]

# Image classification docstring
a :				List[str]      					=		"""microsoft/resnet-50"""
a :				Optional[Any]      					=		"""tiger cat"""

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


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




						"""simple docstring"""




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




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												# Pad to match that done in the PyTorch Conv2D model
												lowercase__				:      int=       (self.pad_value, self.pad_value)
												lowercase__				:      Tuple=       tf.pad(snake_case__       ,  [(0, 0), height_pad, width_pad, (0, 0)]			)
												lowercase__				:      List[Any]=       self.conv(snake_case__			)
												return hidden_state




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.convolution(snake_case__			)
												lowercase__				:      Optional[Any]=       self.normalization(snake_case__       ,  training=snake_case__			)
												lowercase__				:      Dict=       self.activation(snake_case__			)
												return hidden_state


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(**snake_case__			)
												lowercase__				:      List[Any]=       TFResNetConvLayer(
												    config.embedding_size       ,  kernel_size=7       ,  stride=2       ,  activation=config.hidden_act       ,  name="embedder"       ,  )
												lowercase__				:      Union[str, Any]=       tf.keras.layers.MaxPoolaD(pool_size=3       ,  strides=2       ,  padding="valid"       ,  name="pooler"			)
												lowercase__				:      Tuple=       config.num_channels




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												lowercase__,	lowercase__,	lowercase__,	lowercase__				:      Optional[int]=       shape_list(snake_case__			)
												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."			)
												lowercase__				:      Union[str, Any]=       pixel_values
												lowercase__				:      str=       self.embedder(snake_case__			)
												lowercase__				:      List[str]=       tf.pad(snake_case__       ,  [[0, 0], [1, 1], [1, 1], [0, 0]]			)
												lowercase__				:      Any=       self.pooler(snake_case__			)
												return hidden_state


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__ = 2       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(**snake_case__			)
												lowercase__				:      str=       tf.keras.layers.ConvaD(
												    snake_case__       ,  kernel_size=1       ,  strides=snake_case__       ,  use_bias=snake_case__       ,  name="convolution"			)
												# Use same default momentum and epsilon as PyTorch equivalent
												lowercase__				:      int=       tf.keras.layers.BatchNormalization(epsilon=1e-5       ,  momentum=0.9       ,  name="normalization"			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												lowercase__				:      Any=       x
												lowercase__				:      Dict=       self.convolution(snake_case__			)
												lowercase__				:      List[Any]=       self.normalization(snake_case__       ,  training=snake_case__			)
												return hidden_state


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 1       ,  snake_case__ = "relu"       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(**snake_case__			)
												lowercase__				:      Optional[int]=       in_channels != out_channels or stride != 1
												lowercase__				:      Any=       TFResNetConvLayer(snake_case__       ,  stride=snake_case__       ,  name="layer.0"			)
												lowercase__				:      List[Any]=       TFResNetConvLayer(snake_case__       ,  activation=snake_case__       ,  name="layer.1"			)
												lowercase__				:      List[Any]=       (
												    TFResNetShortCut(snake_case__       ,  stride=snake_case__       ,  name="shortcut"			)
												    if should_apply_shortcut
												    else tf.keras.layers.Activation("linear"       ,  name="shortcut"			)
												)
												lowercase__				:      List[Any]=       ACTaFN[activation]




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												lowercase__				:      Any=       hidden_state
												lowercase__				:      List[Any]=       self.conva(snake_case__       ,  training=snake_case__			)
												lowercase__				:      str=       self.conva(snake_case__       ,  training=snake_case__			)
												lowercase__				:      Any=       self.shortcut(snake_case__       ,  training=snake_case__			)
												hidden_state += residual
												lowercase__				:      Dict=       self.activation(snake_case__			)
												return hidden_state


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 1       ,  snake_case__ = "relu"       ,  snake_case__ = 4       ,  **snake_case__       ,  ):
												'''simple docstring'''
												super().__init__(**snake_case__			)
												lowercase__				:      Tuple=       in_channels != out_channels or stride != 1
												lowercase__				:      List[str]=       out_channels // reduction
												lowercase__				:      List[Any]=       TFResNetConvLayer(snake_case__       ,  kernel_size=1       ,  name="layer.0"			)
												lowercase__				:      Tuple=       TFResNetConvLayer(snake_case__       ,  stride=snake_case__       ,  name="layer.1"			)
												lowercase__				:      Union[str, Any]=       TFResNetConvLayer(snake_case__       ,  kernel_size=1       ,  activation=snake_case__       ,  name="layer.2"			)
												lowercase__				:      Union[str, Any]=       (
												    TFResNetShortCut(snake_case__       ,  stride=snake_case__       ,  name="shortcut"			)
												    if should_apply_shortcut
												    else tf.keras.layers.Activation("linear"       ,  name="shortcut"			)
												)
												lowercase__				:      str=       ACTaFN[activation]




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       hidden_state
												lowercase__				:      Any=       self.conva(snake_case__       ,  training=snake_case__			)
												lowercase__				:      List[Any]=       self.conva(snake_case__       ,  training=snake_case__			)
												lowercase__				:      Any=       self.conva(snake_case__       ,  training=snake_case__			)
												lowercase__				:      List[Any]=       self.shortcut(snake_case__       ,  training=snake_case__			)
												hidden_state += residual
												lowercase__				:      Any=       self.activation(snake_case__			)
												return hidden_state


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 2       ,  snake_case__ = 2       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(**snake_case__			)

												lowercase__				:      str=       TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

												lowercase__				:      Optional[Any]=       [layer(snake_case__       ,  snake_case__       ,  stride=snake_case__       ,  activation=config.hidden_act       ,  name="layers.0"			)]
												layers += [
												    layer(snake_case__       ,  snake_case__       ,  activation=config.hidden_act       ,  name=F'''layers.{i + 1}'''			)
												    for i in range(depth - 1			)
												]
												lowercase__				:      int=       layers




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False			):
												'''simple docstring'''
												for layer in self.stage_layers:
																		lowercase__				:      List[Any]=       layer(snake_case__       ,  training=snake_case__			)
												return hidden_state


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(**snake_case__			)
												# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
												lowercase__				:      Any=       [
												    TFResNetStage(
												        snake_case__       ,  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(snake_case__       ,  snake_case__       ,  snake_case__       ,  depth=snake_case__       ,  name=F'''stages.{i + 1}'''			)			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = False       ,  snake_case__ = True       ,  snake_case__ = False       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       () if output_hidden_states else None

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

																		lowercase__				:      List[str]=       stage_module(snake_case__       ,  training=snake_case__			)

												if output_hidden_states:
																		lowercase__				:      Tuple=       hidden_states + (hidden_state,)

												if not return_dict:
																		return tuple(v for v in [hidden_state, hidden_states] if v is not None			)

												return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case__       ,  hidden_states=snake_case__			)


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					ResNetConfig
						__lowerCamelCase				       =					"resnet"
						__lowerCamelCase				       =					"pixel_values"




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


a :				Dict      					=		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.
"""


a :				Tuple      					=		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     __UpperCAmelCase(   tf.keras.layers.Layer							):




						"""simple docstring"""




						__lowerCamelCase				       =					ResNetConfig




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(**snake_case__			)
												lowercase__				:      str=       config
												lowercase__				:      Optional[int]=       TFResNetEmbeddings(snake_case__       ,  name="embedder"			)
												lowercase__				:      Any=       TFResNetEncoder(snake_case__       ,  name="encoder"			)
												lowercase__				:      str=       tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__			)




						@unpack_inputs
						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = None       ,  snake_case__ = None       ,  snake_case__ = False       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												lowercase__				:      Tuple=       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)
												lowercase__				:      Tuple=       tf.transpose(snake_case__       ,  perm=[0, 2, 3, 1]			)
												lowercase__				:      List[Any]=       self.embedder(snake_case__       ,  training=snake_case__			)

												lowercase__				:      Optional[Any]=       self.encoder(
												    snake_case__       ,  output_hidden_states=snake_case__       ,  return_dict=snake_case__       ,  training=snake_case__			)

												lowercase__				:      Dict=       encoder_outputs[0]

												lowercase__				:      int=       self.pooler(snake_case__			)

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

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

												lowercase__				:      Optional[Any]=       hidden_states if output_hidden_states else None

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


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(snake_case__       ,  **snake_case__			)
												lowercase__				:      List[str]=       TFResNetMainLayer(config=snake_case__       ,  name="resnet"			)




						@add_start_docstrings_to_model_forward(snake_case__			)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC       ,  output_type=snake_case__       ,  config_class=_CONFIG_FOR_DOC       ,  modality="vision"       ,  expected_output=_EXPECTED_OUTPUT_SHAPE       ,  )
						@unpack_inputs
						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__ = None       ,  snake_case__ = None       ,  snake_case__ = False       ,  ):
												'''simple docstring'''
												lowercase__				:      str=       (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												lowercase__				:      Optional[Any]=       return_dict if return_dict is not None else self.config.use_return_dict

												lowercase__				:      str=       self.resnet(
												    pixel_values=snake_case__       ,  output_hidden_states=snake_case__       ,  return_dict=snake_case__       ,  training=snake_case__       ,  )
												return resnet_outputs


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




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  **snake_case__			):
												'''simple docstring'''
												super().__init__(snake_case__       ,  **snake_case__			)
												lowercase__				:      Optional[Any]=       config.num_labels
												lowercase__				:      Union[str, Any]=       TFResNetMainLayer(snake_case__       ,  name="resnet"			)
												# classification head
												lowercase__				:      List[Any]=       (
												    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       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      str=       tf.keras.layers.Flatten()(snake_case__			)
												lowercase__				:      List[str]=       self.classifier_layer(snake_case__			)
												return logits




						@add_start_docstrings_to_model_forward(snake_case__			)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,  output_type=snake_case__       ,  config_class=_CONFIG_FOR_DOC       ,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,  )
						@unpack_inputs
						def 		UpperCAmelCase_       (   self       ,  snake_case__ = None       ,  snake_case__ = None       ,  snake_case__ = None       ,  snake_case__ = None       ,  snake_case__ = False       ,  ):
												'''simple docstring'''
												lowercase__				:      Dict=       return_dict if return_dict is not None else self.config.use_return_dict

												lowercase__				:      Optional[int]=       self.resnet(
												    snake_case__       ,  output_hidden_states=snake_case__       ,  return_dict=snake_case__       ,  training=snake_case__			)

												lowercase__				:      int=       outputs.pooler_output if return_dict else outputs[1]

												lowercase__				:      Optional[Any]=       self.classifier(snake_case__			)

												lowercase__				:      int=       None if labels is None else self.hf_compute_loss(snake_case__       ,  snake_case__			)

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

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












def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						str      )  ->							int:
    UpperCAmelCase_							 =     5381
    for x in s:
        UpperCAmelCase_							 =     ((hash_value << 5) + hash_value) + ord(__UpperCamelCase      )
    return hash_value & 0xffff_ffff





