

'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase__      				=			{
    "configuration_rwkv": ["RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP", "RwkvConfig", "RwkvOnnxConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__      				=			[
						    "RWKV_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RwkvForCausalLM",
						    "RwkvModel",
						    "RwkvPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rwkv import (
												    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RwkvForCausalLM,
												    RwkvModel,
												    RwkvPreTrainedModel,
												)
else:
						import sys

						lowerCAmelCase__      				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''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


lowerCAmelCase__      				=			logging.get_logger(__name__)

# General docstring
lowerCAmelCase__      				=			"ResNetConfig"

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

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

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



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


							def __init__(      self     ,      _snake_case     ,      _snake_case = 3     ,      _snake_case = 1     ,      _snake_case = "relu"     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       kernel_size // 2
								UpperCAmelCase_		:   Dict													=       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
								UpperCAmelCase_		:   Dict													=       tf.keras.layers.BatchNormalization(epsilon=1e-5     ,      momentum=0.9     ,      name='normalization')
								UpperCAmelCase_		:   Optional[int]													=       ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear')


							def     _snake_case				(      self     ,      _snake_case)				->					tf.Tensor:
								# Pad to match that done in the PyTorch Conv2D model
								UpperCAmelCase_		:   List[str]													=       (self.pad_value, self.pad_value)
								UpperCAmelCase_		:   Dict													=       tf.pad(_snake_case     ,      [(0, 0), height_pad, width_pad, (0, 0)])
								UpperCAmelCase_		:   str													=       self.conv(_snake_case)
								return hidden_state





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   int													=       self.convolution(_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       self.normalization(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Any													=       self.activation(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   List[Any]													=       TFResNetConvLayer(
								    config.embedding_size     ,      kernel_size=7     ,      stride=2     ,      activation=config.hidden_act     ,      name='embedder'     ,      )
								UpperCAmelCase_		:   str													=       tf.keras.layers.MaxPoolaD(pool_size=3     ,      strides=2     ,      padding='valid'     ,      name='pooler')
								UpperCAmelCase_		:   Any													=       config.num_channels





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_						,     UpperCAmelCase_		:   str													=       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.')
								UpperCAmelCase_		:   Any													=       pixel_values
								UpperCAmelCase_		:   int													=       self.embedder(_snake_case)
								UpperCAmelCase_		:   Tuple													=       tf.pad(_snake_case     ,      [[0, 0], [1, 1], [1, 1], [0, 0]])
								UpperCAmelCase_		:   Any													=       self.pooler(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      _snake_case = 2     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								UpperCAmelCase_		:   Any													=       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
								UpperCAmelCase_		:   List[str]													=       tf.keras.layers.BatchNormalization(epsilon=1e-5     ,      momentum=0.9     ,      name='normalization')





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   Dict													=       x
								UpperCAmelCase_		:   List[str]													=       self.convolution(_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.normalization(_snake_case     ,      training=_snake_case)
								return hidden_state



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


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





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   int													=       hidden_state
								UpperCAmelCase_		:   Tuple													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   List[Any]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   int													=       self.shortcut(_snake_case     ,      training=_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:   str													=       self.activation(_snake_case)
								return hidden_state



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


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





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								UpperCAmelCase_		:   Dict													=       hidden_state
								UpperCAmelCase_		:   Tuple													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Union[str, Any]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.conva(_snake_case     ,      training=_snake_case)
								UpperCAmelCase_		:   Optional[int]													=       self.shortcut(_snake_case     ,      training=_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:   str													=       self.activation(_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      _snake_case     ,      _snake_case     ,      _snake_case = 2     ,      _snake_case = 2     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:   Dict													=       TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

								UpperCAmelCase_		:   List[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)
								]
								UpperCAmelCase_		:   Optional[Any]													=       layers





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False)				->					tf.Tensor:
								for layer in self.stage_layers:
									UpperCAmelCase_		:   List[Any]													=       layer(_snake_case     ,      training=_snake_case)
								return hidden_state



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


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(**_snake_case)
								# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
								UpperCAmelCase_		:   Optional[int]													=       [
								    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     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False     ,      _snake_case = True     ,      _snake_case = False     ,      )				->					TFBaseModelOutputWithNoAttention:
								UpperCAmelCase_		:   Optional[Any]													=       () if output_hidden_states else None

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

									UpperCAmelCase_		:   List[Any]													=       stage_module(_snake_case     ,      training=_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:   int													=       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        lowercase    (	a_			):
							_lowerCamelCase		:   Optional[int]= ResNetConfig
							_lowerCamelCase		:   str= "resnet"
							_lowerCamelCase		:   Optional[int]= "pixel_values"


							@property
							def     _snake_case				(      self)				->					int:
								return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)     ,      dtype=tf.floataa)}


lowerCAmelCase__      				=			r"\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"


lowerCAmelCase__      				=			r"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@keras_serializable
class        lowercase    (	tf.keras.layers.Layer			):
							_lowerCamelCase		:   Any= ResNetConfig


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





							@unpack_inputs
							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
								UpperCAmelCase_		:   Any													=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:   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)
								UpperCAmelCase_		:   Any													=       tf.transpose(_snake_case     ,      perm=[0, 2, 3, 1])
								UpperCAmelCase_		:   Any													=       self.embedder(_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   str													=       self.encoder(
								    _snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case)

								UpperCAmelCase_		:   List[Any]													=       encoder_outputs[0]

								UpperCAmelCase_		:   Union[str, Any]													=       self.pooler(_snake_case)

								# Transpose all the outputs to the NCHW format
								# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
								UpperCAmelCase_		:   Any													=       tf.transpose(_snake_case     ,      (0, 3, 1, 2))
								UpperCAmelCase_		:   Dict													=       tf.transpose(_snake_case     ,      (0, 3, 1, 2))
								UpperCAmelCase_		:   Any													=       ()
								for hidden_state in encoder_outputs[1:]:
									UpperCAmelCase_		:   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

								UpperCAmelCase_		:   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.",       a_,       )
class        lowercase    (	a_			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(_snake_case     ,      **_snake_case)
								UpperCAmelCase_		:   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     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
								UpperCAmelCase_		:   Optional[Any]													=       (
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:   Tuple													=       return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:   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    ",       a_,       )
class        lowercase    (	a_,       a_			):


							def __init__(      self     ,      _snake_case     ,      **_snake_case)				->					None:
								super().__init__(_snake_case     ,      **_snake_case)
								UpperCAmelCase_		:   Tuple													=       config.num_labels
								UpperCAmelCase_		:   Optional[Any]													=       TFResNetMainLayer(_snake_case     ,      name='resnet')
								# classification head
								UpperCAmelCase_		:   str													=       (
								    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     _snake_case				(      self     ,      _snake_case)				->					tf.Tensor:
								UpperCAmelCase_		:   List[str]													=       tf.keras.layers.Flatten()(_snake_case)
								UpperCAmelCase_		:   List[Any]													=       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     _snake_case				(      self     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = None     ,      _snake_case = False     ,      )				->					Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
								UpperCAmelCase_		:   Dict													=       return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:   List[Any]													=       self.resnet(
								    _snake_case     ,      output_hidden_states=_snake_case     ,      return_dict=_snake_case     ,      training=_snake_case)

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

								UpperCAmelCase_		:   Union[str, Any]													=       self.classifier(_snake_case)

								UpperCAmelCase_		:   Optional[int]													=       None if labels is None else self.hf_compute_loss(_snake_case     ,      _snake_case)

								if not return_dict:
									UpperCAmelCase_		:   int													=       (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)






