






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			:			Union[str, Any]			   =   logging.get_logger(__name__)

# General docstring
__snake_case			:			Optional[int]			   =   """ResNetConfig"""

# Base docstring
__snake_case			:			List[str]			   =   """microsoft/resnet-50"""
__snake_case			:			Any			   =   [1, 20_48, 7, 7]

# Image classification docstring
__snake_case			:			str			   =   """microsoft/resnet-50"""
__snake_case			:			Union[str, Any]			   =   """tiger cat"""

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


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






						def __init__(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = 3   ,  _UpperCamelCase = 1   ,  _UpperCamelCase = "relu"   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												super().__init__(**_lowercase      )
												lowerCAmelCase__			  =  kernel_size // 2
												lowerCAmelCase__			  =  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
												lowerCAmelCase__			  =  tf.keras.layers.BatchNormalization(epsilon=1E-5   ,  momentum=0.9   ,  name='normalization'      )
												lowerCAmelCase__			  =  ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'      )






						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  lowerCAmelCase__			  =  (self.pad_value, self.pad_value)
												lowerCAmelCase__			  =  tf.pad(_lowercase   ,  [(0, 0), height_pad, width_pad, (0, 0)]      )
												lowerCAmelCase__			  =  self.conv(_lowercase      )
												return hidden_state







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.convolution(_lowercase      )
												lowerCAmelCase__			  =  self.normalization(_lowercase   ,  training=_lowercase      )
												lowerCAmelCase__			  =  self.activation(_lowercase      )
												return hidden_state




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






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






												"""simple docstring"""
												super().__init__(**_lowercase      )
												lowerCAmelCase__			  =  TFResNetConvLayer(
												    config.embedding_size   ,  kernel_size=7   ,  stride=2   ,  activation=config.hidden_act   ,  name='embedder'   ,  )
												lowerCAmelCase__			  =  tf.keras.layers.MaxPoolaD(pool_size=3   ,  strides=2   ,  padding='valid'   ,  name='pooler'      )
												lowerCAmelCase__			  =  config.num_channels







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""
												lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__			  =  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.'      )
												lowerCAmelCase__			  =  pixel_values
												lowerCAmelCase__			  =  self.embedder(_lowercase      )
												lowerCAmelCase__			  =  tf.pad(_lowercase   ,  [[0, 0], [1, 1], [1, 1], [0, 0]]      )
												lowerCAmelCase__			  =  self.pooler(_lowercase      )
												return hidden_state




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






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






												"""simple docstring"""
												super().__init__(**_lowercase      )
												lowerCAmelCase__			  =  tf.keras.layers.ConvaD(
												    _lowercase   ,  kernel_size=1   ,  strides=_lowercase   ,  use_bias=_lowercase   ,  name='convolution'      )
												# Use same default momentum and epsilon as PyTorch equivalent
												lowerCAmelCase__			  =  tf.keras.layers.BatchNormalization(epsilon=1E-5   ,  momentum=0.9   ,  name='normalization'      )







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  x
												lowerCAmelCase__			  =  self.convolution(_lowercase      )
												lowerCAmelCase__			  =  self.normalization(_lowercase   ,  training=_lowercase      )
												return hidden_state




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






						def __init__(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = 1   ,  _UpperCamelCase = "relu"   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												super().__init__(**_lowercase      )
												lowerCAmelCase__			  =  in_channels != out_channels or stride != 1
												lowerCAmelCase__			  =  TFResNetConvLayer(_lowercase   ,  stride=_lowercase   ,  name='layer.0'      )
												lowerCAmelCase__			  =  TFResNetConvLayer(_lowercase   ,  activation=_lowercase   ,  name='layer.1'      )
												lowerCAmelCase__			  =  (
												    TFResNetShortCut(_lowercase   ,  stride=_lowercase   ,  name='shortcut'      )
												    if should_apply_shortcut
												    else tf.keras.layers.Activation('linear'   ,  name='shortcut'      )
												)
												lowerCAmelCase__			  =  ACTaFN[activation]







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  hidden_state
												lowerCAmelCase__			  =  self.conva(_lowercase   ,  training=_lowercase      )
												lowerCAmelCase__			  =  self.conva(_lowercase   ,  training=_lowercase      )
												lowerCAmelCase__			  =  self.shortcut(_lowercase   ,  training=_lowercase      )
												hidden_state += residual
												lowerCAmelCase__			  =  self.activation(_lowercase      )
												return hidden_state




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






						def __init__(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase = 1   ,  _UpperCamelCase = "relu"   ,  _UpperCamelCase = 4   ,  **_UpperCamelCase   ,  ):






												"""simple docstring"""
												super().__init__(**_lowercase      )
												lowerCAmelCase__			  =  in_channels != out_channels or stride != 1
												lowerCAmelCase__			  =  out_channels // reduction
												lowerCAmelCase__			  =  TFResNetConvLayer(_lowercase   ,  kernel_size=1   ,  name='layer.0'      )
												lowerCAmelCase__			  =  TFResNetConvLayer(_lowercase   ,  stride=_lowercase   ,  name='layer.1'      )
												lowerCAmelCase__			  =  TFResNetConvLayer(_lowercase   ,  kernel_size=1   ,  activation=_lowercase   ,  name='layer.2'      )
												lowerCAmelCase__			  =  (
												    TFResNetShortCut(_lowercase   ,  stride=_lowercase   ,  name='shortcut'      )
												    if should_apply_shortcut
												    else tf.keras.layers.Activation('linear'   ,  name='shortcut'      )
												)
												lowerCAmelCase__			  =  ACTaFN[activation]







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  hidden_state
												lowerCAmelCase__			  =  self.conva(_lowercase   ,  training=_lowercase      )
												lowerCAmelCase__			  =  self.conva(_lowercase   ,  training=_lowercase      )
												lowerCAmelCase__			  =  self.conva(_lowercase   ,  training=_lowercase      )
												lowerCAmelCase__			  =  self.shortcut(_lowercase   ,  training=_lowercase      )
												hidden_state += residual
												lowerCAmelCase__			  =  self.activation(_lowercase      )
												return hidden_state




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






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






												"""simple docstring"""
												super().__init__(**_lowercase      )

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

												lowerCAmelCase__			  =  [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      )
												]
												lowerCAmelCase__			  =  layers







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = False      ):






												"""simple docstring"""





												for layer in self.stage_layers:
																		lowerCAmelCase__			  =  layer(_lowercase   ,  training=_lowercase      )
												return hidden_state




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






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






												"""simple docstring"""
												super().__init__(**_lowercase      )
												# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
												lowerCAmelCase__			  =  [
												    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   ,  _UpperCamelCase   ,  _UpperCamelCase = False   ,  _UpperCamelCase = True   ,  _UpperCamelCase = False   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  () if output_hidden_states else None

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

																		lowerCAmelCase__			  =  stage_module(_lowercase   ,  training=_lowercase      )

												if output_hidden_states:
																		lowerCAmelCase__			  =  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 			__SCREAMING_SNAKE_CASE    (      lowercase_):
						_SCREAMING_SNAKE_CASE		:				Optional[Any]            =    ResNetConfig
						_SCREAMING_SNAKE_CASE		:				Optional[Any]            =    '''resnet'''
						_SCREAMING_SNAKE_CASE		:				List[Any]            =    '''pixel_values'''






						@property
						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)   ,  dtype=tf.floataa      )}




__snake_case			:			List[Any]			   =   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			:			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 			__SCREAMING_SNAKE_CASE    (      tf.keras.layers.Layer):
						_SCREAMING_SNAKE_CASE		:				Union[str, Any]            =    ResNetConfig






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






												"""simple docstring"""
												super().__init__(**_lowercase      )
												lowerCAmelCase__			  =  config
												lowerCAmelCase__			  =  TFResNetEmbeddings(_lowercase   ,  name='embedder'      )
												lowerCAmelCase__			  =  TFResNetEncoder(_lowercase   ,  name='encoder'      )
												lowerCAmelCase__			  =  tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowercase      )







						@unpack_inputs
						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = False   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												lowerCAmelCase__			  =  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)
												lowerCAmelCase__			  =  tf.transpose(_lowercase   ,  perm=[0, 2, 3, 1]      )
												lowerCAmelCase__			  =  self.embedder(_lowercase   ,  training=_lowercase      )

												lowerCAmelCase__			  =  self.encoder(
												    _lowercase   ,  output_hidden_states=_lowercase   ,  return_dict=_lowercase   ,  training=_lowercase      )

												lowerCAmelCase__			  =  encoder_outputs[0]

												lowerCAmelCase__			  =  self.pooler(_lowercase      )

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

												lowerCAmelCase__			  =  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.'''						,    lowercase_						,    )
class 			__SCREAMING_SNAKE_CASE    (      lowercase_):






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






												"""simple docstring"""
												super().__init__(_lowercase   ,  **_lowercase      )
												lowerCAmelCase__			  =  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   ,  _UpperCamelCase   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = False   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  (
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												lowerCAmelCase__			  =  return_dict if return_dict is not None else self.config.use_return_dict

												lowerCAmelCase__			  =  self.resnet(
												    pixel_values=_lowercase   ,  output_hidden_states=_lowercase   ,  return_dict=_lowercase   ,  training=_lowercase   ,  )
												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    '''						,    lowercase_						,    )
class 			__SCREAMING_SNAKE_CASE    (      lowercase_						,    lowercase_):






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






												"""simple docstring"""
												super().__init__(_lowercase   ,  **_lowercase      )
												lowerCAmelCase__			  =  config.num_labels
												lowerCAmelCase__			  =  TFResNetMainLayer(_lowercase   ,  name='resnet'      )
												# classification head
												lowerCAmelCase__			  =  (
												    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   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  tf.keras.layers.Flatten()(_lowercase      )
												lowerCAmelCase__			  =  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   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = None   ,  _UpperCamelCase = False   ,  ):






												"""simple docstring"""
												lowerCAmelCase__			  =  return_dict if return_dict is not None else self.config.use_return_dict

												lowerCAmelCase__			  =  self.resnet(
												    _lowercase   ,  output_hidden_states=_lowercase   ,  return_dict=_lowercase   ,  training=_lowercase      )

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

												lowerCAmelCase__			  =  self.classifier(_lowercase      )

												lowerCAmelCase__			  =  None if labels is None else self.hf_compute_loss(_lowercase   ,  _lowercase      )

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

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












from typing import TYPE_CHECKING

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


_snake_case				= {"""configuration_bit""": ["""BIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BitConfig""", """BitOnnxConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """BIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BitForImageClassification""",
      """BitModel""",
      """BitPreTrainedModel""",
      """BitBackbone""",
  ]


try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""BitImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bit import (
        BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BitBackbone,
        BitForImageClassification,
        BitModel,
        BitPreTrainedModel,
    )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_bit import BitImageProcessor

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure)






