


'''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


_UpperCAmelCase					:			int				     =					logging.get_logger(__name__)

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

# Base docstring
_UpperCAmelCase					:			str				     =					"""microsoft/resnet-50"""
_UpperCAmelCase					:			Union[str, Any]				     =					[1, 2_0_4_8, 7, 7]

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

_UpperCAmelCase					:			Tuple				     =					[
    """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							,      __lowercase							,      __lowercase = 3							,      __lowercase = 1							,      __lowercase = "relu"							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase		):
										# Pad to match that done in the PyTorch Conv2D model
										__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        _snake_case	(self							,      __lowercase							,      __lowercase = False		):
										__lowerCAmelCase											=					self.convolution(__lowercase		)
										__lowerCAmelCase											=					self.normalization(__lowercase							,      training=__lowercase		)
										__lowerCAmelCase											=					self.activation(__lowercase		)
										return hidden_state


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







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = False		):
										__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    a__					(			tf.keras.layers.Layer   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase = 2							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = False		):
										__lowerCAmelCase											=					x
										__lowerCAmelCase											=					self.convolution(__lowercase		)
										__lowerCAmelCase											=					self.normalization(__lowercase							,      training=__lowercase		)
										return hidden_state


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







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase							,      __lowercase = 1							,      __lowercase = "relu"							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = False		):
										__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    a__					(			tf.keras.layers.Layer   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase							,      __lowercase = 1							,      __lowercase = "relu"							,      __lowercase = 4							,      **__lowercase							,      ):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = False		):
										__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    a__					(			tf.keras.layers.Layer   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase							,      __lowercase							,      __lowercase = 2							,      __lowercase = 2							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = False		):
										for layer in self.stage_layers:
																__lowerCAmelCase											=					layer(__lowercase							,      training=__lowercase		)
										return hidden_state


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







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = False							,      __lowercase = True							,      __lowercase = False							,      ):
										__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    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		Any						=						ResNetConfig
				__UpperCamelCase     :		Optional[Any]						=						'resnet'
				__UpperCamelCase     :		Optional[int]						=						'pixel_values'




				@property
				def        _snake_case	(self		):
										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)							,      dtype=tf.floataa		)}

_UpperCAmelCase					:			str				     =					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.
"""


_UpperCAmelCase					:			Optional[Any]				     =					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     :		Any						=						ResNetConfig




				def __init__(self							,      __lowercase							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = None							,      __lowercase = None							,      __lowercase = False							,      ):
										__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.'				,    __A				,    )
class    a__					(			__A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase							,      __lowercase = None							,      __lowercase = None							,      __lowercase = False							,      ):
										__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    '				,    __A				,    )
class    a__					(			__A				,    __A   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      **__lowercase		):
										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        _snake_case	(self							,      __lowercase		):
										__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        _snake_case	(self							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = None							,      __lowercase = False							,      ):
										__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		)





'''simple docstring'''

import math

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					math.log(2,					1_0) * number
						__lowerCAmelCase											=					round((log_number - int(lowerCamelCase)),					1_5)

						return difference

def 				__magic_name__(    lowerCamelCase = 6_7_8_9_1_0):
						__lowerCAmelCase											=					9_0
						__lowerCAmelCase											=					0

						__lowerCAmelCase											=					math.log(1.23,					1_0)
						__lowerCAmelCase											=					math.log(1.24,					1_0)
						__lowerCAmelCase											=					0

						while position < number:
												__lowerCAmelCase											=					log_difference(lowerCamelCase)

												if difference >= upper_limit:
																		power_iterator += 9_3

												elif difference < lower_limit:
																		power_iterator += 1_9_6

												else:
																		__lowerCAmelCase											=					power_iterator
																		power_iterator += 1_9_6
																		position += 1

						return previous_power


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	print(f"""{solution() = }""")

