





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


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

# General docstring
_SCREAMING_SNAKE_CASE   						=		"""ResNetConfig"""

# Base docstring
_SCREAMING_SNAKE_CASE   						=		"""microsoft/resnet-50"""
_SCREAMING_SNAKE_CASE   						=		[1, 20_48, 7, 7]

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

_SCREAMING_SNAKE_CASE   						=		[
    """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   :   Union[str, Any]		, _A   :   int		, _A   :   int = 3		, _A   :   int = 1		, _A   :   str = "relu"		, **_A   :   List[Any]						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							snake_case_		:      Any           =						kernel_size // 2
							snake_case_		:      Optional[Any]           =						tf.keras.layers.ConvaD(
							    _A		, kernel_size=_A		, strides=_A		, padding='valid'		, use_bias=_A		, name='convolution'						)
							# Use same default momentum and epsilon as PyTorch equivalent
							snake_case_		:      List[Any]           =						tf.keras.layers.BatchNormalization(epsilon=1E-5		, momentum=0.9		, name='normalization'						)
							snake_case_		:      Any           =						ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'						)






						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   tf.Tensor						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_		:      Any           =						(self.pad_value, self.pad_value)
							snake_case_		:      int           =						tf.pad(_A		, [(0, 0), height_pad, width_pad, (0, 0)]						)
							snake_case_		:      str           =						self.conv(_A						)
							return hidden_state






						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   tf.Tensor		, _A   :   bool = False						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_		:      int           =						self.convolution(_A						)
							snake_case_		:      Tuple           =						self.normalization(_A		, training=_A						)
							snake_case_		:      Tuple           =						self.activation(_A						)
							return hidden_state
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   List[Any]		, _A   :   ResNetConfig		, **_A   :   Dict						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							snake_case_		:      Dict           =						TFResNetConvLayer(
							    config.embedding_size		, kernel_size=7		, stride=2		, activation=config.hidden_act		, name='embedder'		, )
							snake_case_		:      Dict           =						tf.keras.layers.MaxPoolaD(pool_size=3		, strides=2		, padding='valid'		, name='pooler'						)
							snake_case_		:      int           =						config.num_channels






						def UpperCAmelCase_					(   self   :   Tuple		, _A   :   tf.Tensor		, _A   :   bool = False						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      str           =						shape_list(_A						)
							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.'						)
							snake_case_		:      Any           =						pixel_values
							snake_case_		:      Union[str, Any]           =						self.embedder(_A						)
							snake_case_		:      Optional[int]           =						tf.pad(_A		, [[0, 0], [1, 1], [1, 1], [0, 0]]						)
							snake_case_		:      str           =						self.pooler(_A						)
							return hidden_state
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   Optional[int]		, _A   :   int		, _A   :   int = 2		, **_A   :   Tuple						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							snake_case_		:      int           =						tf.keras.layers.ConvaD(
							    _A		, kernel_size=1		, strides=_A		, use_bias=_A		, name='convolution'						)
							# Use same default momentum and epsilon as PyTorch equivalent
							snake_case_		:      Dict           =						tf.keras.layers.BatchNormalization(epsilon=1E-5		, momentum=0.9		, name='normalization'						)






						def UpperCAmelCase_					(   self   :   int		, _A   :   tf.Tensor		, _A   :   bool = False						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						x
							snake_case_		:      Optional[int]           =						self.convolution(_A						)
							snake_case_		:      Optional[Any]           =						self.normalization(_A		, training=_A						)
							return hidden_state
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   Tuple		, _A   :   int		, _A   :   int		, _A   :   int = 1		, _A   :   str = "relu"		, **_A   :   Any						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							snake_case_		:      List[str]           =						in_channels != out_channels or stride != 1
							snake_case_		:      Union[str, Any]           =						TFResNetConvLayer(_A		, stride=_A		, name='layer.0'						)
							snake_case_		:      Union[str, Any]           =						TFResNetConvLayer(_A		, activation=_A		, name='layer.1'						)
							snake_case_		:      Optional[int]           =						(
							    TFResNetShortCut(_A		, stride=_A		, name='shortcut'						)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation('linear'		, name='shortcut'						)
							)
							snake_case_		:      Tuple           =						ACTaFN[activation]






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   tf.Tensor		, _A   :   bool = False						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						hidden_state
							snake_case_		:      List[Any]           =						self.conva(_A		, training=_A						)
							snake_case_		:      List[Any]           =						self.conva(_A		, training=_A						)
							snake_case_		:      Optional[int]           =						self.shortcut(_A		, training=_A						)
							hidden_state += residual
							snake_case_		:      int           =						self.activation(_A						)
							return hidden_state
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   Union[str, Any]		, _A   :   int		, _A   :   int		, _A   :   int = 1		, _A   :   str = "relu"		, _A   :   int = 4		, **_A   :   int		, )				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							snake_case_		:      Tuple           =						in_channels != out_channels or stride != 1
							snake_case_		:      str           =						out_channels // reduction
							snake_case_		:      Tuple           =						TFResNetConvLayer(_A		, kernel_size=1		, name='layer.0'						)
							snake_case_		:      Optional[Any]           =						TFResNetConvLayer(_A		, stride=_A		, name='layer.1'						)
							snake_case_		:      int           =						TFResNetConvLayer(_A		, kernel_size=1		, activation=_A		, name='layer.2'						)
							snake_case_		:      str           =						(
							    TFResNetShortCut(_A		, stride=_A		, name='shortcut'						)
							    if should_apply_shortcut
							    else tf.keras.layers.Activation('linear'		, name='shortcut'						)
							)
							snake_case_		:      str           =						ACTaFN[activation]






						def UpperCAmelCase_					(   self   :   str		, _A   :   tf.Tensor		, _A   :   bool = False						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						hidden_state
							snake_case_		:      List[str]           =						self.conva(_A		, training=_A						)
							snake_case_		:      Union[str, Any]           =						self.conva(_A		, training=_A						)
							snake_case_		:      Optional[int]           =						self.conva(_A		, training=_A						)
							snake_case_		:      List[Any]           =						self.shortcut(_A		, training=_A						)
							hidden_state += residual
							snake_case_		:      Tuple           =						self.activation(_A						)
							return hidden_state
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   Any		, _A   :   ResNetConfig		, _A   :   int		, _A   :   int		, _A   :   int = 2		, _A   :   int = 2		, **_A   :   List[Any]						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)

							snake_case_		:      int           =						TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

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






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   tf.Tensor		, _A   :   bool = False						)				->     tf.Tensor:







							"""simple docstring"""


							for layer in self.stage_layers:
								snake_case_		:      Union[str, Any]           =						layer(_A		, training=_A						)
							return hidden_state
class SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   Tuple		, _A   :   ResNetConfig		, **_A   :   Optional[int]						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
							snake_case_		:      List[str]           =						[
							    TFResNetStage(
							        _A		, 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(_A		, _A		, _A		, depth=_A		, name=F"""stages.{i + 1}"""						)						)






						def UpperCAmelCase_					(   self   :   Any		, _A   :   tf.Tensor		, _A   :   bool = False		, _A   :   bool = True		, _A   :   bool = False		, )				->     TFBaseModelOutputWithNoAttention:







							"""simple docstring"""


							snake_case_		:      List[str]           =						() if output_hidden_states else None

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

								snake_case_		:      Union[str, Any]           =						stage_module(_A		, training=_A						)

							if output_hidden_states:
								snake_case_		:      List[Any]           =						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=_A		, hidden_states=_A						)
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[int]			   =							ResNetConfig
						__magic_name__:      Union[str, Any]			   =							"resnet"
						__magic_name__:      Dict			   =							"pixel_values"






						@property
						def UpperCAmelCase_					(   self   :   List[str]						)				->     Optional[int]:







							"""simple docstring"""


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


_SCREAMING_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 SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):
						__magic_name__:      str			   =							ResNetConfig






						def __init__(   self   :   int		, _A   :   ResNetConfig		, **_A   :   int						)				->     None:







							"""simple docstring"""


							super().__init__(**_A						)
							snake_case_		:      Tuple           =						config
							snake_case_		:      Union[str, Any]           =						TFResNetEmbeddings(_A		, name='embedder'						)
							snake_case_		:      Tuple           =						TFResNetEncoder(_A		, name='encoder'						)
							snake_case_		:      Optional[Any]           =						tf.keras.layers.GlobalAveragePoolingaD(keepdims=_A						)






						@unpack_inputs
						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   tf.Tensor		, _A   :   Optional[bool] = None		, _A   :   Optional[bool] = None		, _A   :   bool = False		, )				->     Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							snake_case_		:      Optional[Any]           =						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)
							snake_case_		:      Optional[Any]           =						tf.transpose(_A		, perm=[0, 2, 3, 1]						)
							snake_case_		:      List[Any]           =						self.embedder(_A		, training=_A						)

							snake_case_		:      List[Any]           =						self.encoder(
							    _A		, output_hidden_states=_A		, return_dict=_A		, training=_A						)

							snake_case_		:      Optional[int]           =						encoder_outputs[0]

							snake_case_		:      List[str]           =						self.pooler(_A						)

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

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

							snake_case_		:      str           =						hidden_states if output_hidden_states else None

							return TFBaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=_A		, pooler_output=_A		, hidden_states=_A		, )
@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."							,					snake_case_							,					)
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):






						def __init__(   self   :   List[str]		, _A   :   ResNetConfig		, **_A   :   Optional[int]						)				->     None:







							"""simple docstring"""


							super().__init__(_A		, **_A						)
							snake_case_		:      Optional[Any]           =						TFResNetMainLayer(config=_A		, name='resnet'						)






						@add_start_docstrings_to_model_forward(_A						)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC		, output_type=_A		, config_class=_CONFIG_FOR_DOC		, modality='vision'		, expected_output=_EXPECTED_OUTPUT_SHAPE		, )
						@unpack_inputs
						def UpperCAmelCase_					(   self   :   List[Any]		, _A   :   tf.Tensor		, _A   :   Optional[bool] = None		, _A   :   Optional[bool] = None		, _A   :   bool = False		, )				->     Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:







							"""simple docstring"""


							snake_case_		:      str           =						(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							snake_case_		:      int           =						return_dict if return_dict is not None else self.config.use_return_dict

							snake_case_		:      Optional[int]           =						self.resnet(
							    pixel_values=_A		, output_hidden_states=_A		, return_dict=_A		, training=_A		, )
							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    "							,					snake_case_							,					)
class SCREAMING_SNAKE_CASE_    (       snake_case_							,					snake_case_       ):






						def __init__(   self   :   Optional[int]		, _A   :   ResNetConfig		, **_A   :   List[str]						)				->     None:







							"""simple docstring"""


							super().__init__(_A		, **_A						)
							snake_case_		:      Union[str, Any]           =						config.num_labels
							snake_case_		:      int           =						TFResNetMainLayer(_A		, name='resnet'						)
							# classification head
							snake_case_		:      Tuple           =						(
							    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   :   Tuple		, _A   :   tf.Tensor						)				->     tf.Tensor:







							"""simple docstring"""


							snake_case_		:      List[Any]           =						tf.keras.layers.Flatten()(_A						)
							snake_case_		:      Tuple           =						self.classifier_layer(_A						)
							return logits






						@add_start_docstrings_to_model_forward(_A						)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT		, output_type=_A		, config_class=_CONFIG_FOR_DOC		, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		, )
						@unpack_inputs
						def UpperCAmelCase_					(   self   :   Any		, _A   :   tf.Tensor = None		, _A   :   tf.Tensor = None		, _A   :   bool = None		, _A   :   bool = None		, _A   :   bool = False		, )				->     Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:







							"""simple docstring"""


							snake_case_		:      int           =						return_dict if return_dict is not None else self.config.use_return_dict

							snake_case_		:      Union[str, Any]           =						self.resnet(
							    _A		, output_hidden_states=_A		, return_dict=_A		, training=_A						)

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

							snake_case_		:      Any           =						self.classifier(_A						)

							snake_case_		:      Any           =						None if labels is None else self.hf_compute_loss(_A		, _A						)

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

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







def 		_lowerCamelCase					(       UpperCamelCase       ,  UpperCamelCase				)      ->		int:





  """simple docstring"""





  return (input_a, input_a).count(0				) % 2


def 		_lowerCamelCase					(       )      ->		None:





  """simple docstring"""





  assert xor_gate(0       ,  0				) == 0
  assert xor_gate(0       ,  1				) == 1
  assert xor_gate(1       ,  0				) == 1
  assert xor_gate(1       ,  1				) == 0


if __name__ == "__main__":
   print(xor_gate(0, 0))
   print(xor_gate(0, 1))
