



"""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     =    logging.get_logger(__name__)

# General docstring
__a     =    "ResNetConfig"

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

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

__a     =    [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class     lowerCamelCase  (      tf.keras.layers.Layer    ):






					'''simple docstring'''





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






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






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							tf.Tensor						,					snake_case:							bool = False						)       ->     tf.Tensor:
											snake_case_			:Optional[int]						=      self.convolution(snake_case						)
											snake_case_			:str						=      self.normalization(snake_case						,					training=snake_case						)
											snake_case_			:Union[str, Any]						=      self.activation(snake_case						)
											return hidden_state

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






					'''simple docstring'''





					def __init__(	self:							Any						,					snake_case:							ResNetConfig						,					**snake_case:							Dict						)       ->     None:
											super().__init__(**snake_case						)
											snake_case_			:Optional[int]						=      TFResNetConvLayer(
											    config.embedding_size						,					kernel_size=7						,					stride=2						,					activation=config.hidden_act						,					name="""embedder"""						,					)
											snake_case_			:Any						=      tf.keras.layers.MaxPoolaD(pool_size=3						,					strides=2						,					padding="""valid"""						,					name="""pooler"""						)
											snake_case_			:Union[str, Any]						=      config.num_channels






					def        lowerCAmelCase_    (	self:							List[Any]						,					snake_case:							tf.Tensor						,					snake_case:							bool = False						)       ->     tf.Tensor:
											snake_case_			:Any						=      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."""						)
											snake_case_			:str						=      pixel_values
											snake_case_			:int						=      self.embedder(snake_case						)
											snake_case_			:Optional[Any]						=      tf.pad(snake_case						,					[[0, 0], [1, 1], [1, 1], [0, 0]]						)
											snake_case_			:Union[str, Any]						=      self.pooler(snake_case						)
											return hidden_state

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






					'''simple docstring'''





					def __init__(	self:							str						,					snake_case:							int						,					snake_case:							int = 2						,					**snake_case:							List[str]						)       ->     None:
											super().__init__(**snake_case						)
											snake_case_			:Dict						=      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
											snake_case_			:Dict						=      tf.keras.layers.BatchNormalization(epsilon=1E-5						,					momentum=0.9						,					name="""normalization"""						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							tf.Tensor						,					snake_case:							bool = False						)       ->     tf.Tensor:
											snake_case_			:str						=      x
											snake_case_			:List[Any]						=      self.convolution(snake_case						)
											snake_case_			:List[Any]						=      self.normalization(snake_case						,					training=snake_case						)
											return hidden_state

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






					'''simple docstring'''





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






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							tf.Tensor						,					snake_case:							bool = False						)       ->     tf.Tensor:
											snake_case_			:Tuple						=      hidden_state
											snake_case_			:List[Any]						=      self.conva(snake_case						,					training=snake_case						)
											snake_case_			:Dict						=      self.conva(snake_case						,					training=snake_case						)
											snake_case_			:int						=      self.shortcut(snake_case						,					training=snake_case						)
											hidden_state += residual
											snake_case_			:int						=      self.activation(snake_case						)
											return hidden_state

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






					'''simple docstring'''





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






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

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






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							ResNetConfig						,					snake_case:							int						,					snake_case:							int						,					snake_case:							int = 2						,					snake_case:							int = 2						,					**snake_case:							Optional[int]						)       ->     None:
											super().__init__(**snake_case						)

											snake_case_			:Optional[int]						=      TFResNetBottleNeckLayer if config.layer_type == """bottleneck""" else TFResNetBasicLayer

											snake_case_			:str						=      [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						)
											]
											snake_case_			:Dict						=      layers






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							tf.Tensor						,					snake_case:							bool = False						)       ->     tf.Tensor:
											for layer in self.stage_layers:
																	snake_case_			:Any						=      layer(snake_case						,					training=snake_case						)
											return hidden_state

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






					'''simple docstring'''





					def __init__(	self:							Union[str, Any]						,					snake_case:							ResNetConfig						,					**snake_case:							Tuple						)       ->     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
											snake_case_			:List[str]						=      [
											    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        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							tf.Tensor						,					snake_case:							bool = False						,					snake_case:							bool = True						,					snake_case:							bool = False						,					)       ->     TFBaseModelOutputWithNoAttention:
											snake_case_			:int						=      () if output_hidden_states else None

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

																	snake_case_			:List[Any]						=      stage_module(snake_case						,					training=snake_case						)

											if output_hidden_states:
																	snake_case_			: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=snake_case						,					hidden_states=snake_case						)

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Union[str, Any]						=					ResNetConfig
					_A		:      List[Any]						=					"""resnet"""
					_A		:      str						=					"""pixel_values"""






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

__a     =    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"


__a     =    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     lowerCamelCase  (      tf.keras.layers.Layer    ):






					'''simple docstring'''





					_A		:      Dict						=					ResNetConfig






					def __init__(	self:							Dict						,					snake_case:							ResNetConfig						,					**snake_case:							Dict						)       ->     None:
											super().__init__(**snake_case						)
											snake_case_			:Optional[Any]						=      config
											snake_case_			:Union[str, Any]						=      TFResNetEmbeddings(snake_case						,					name="""embedder"""						)
											snake_case_			:List[str]						=      TFResNetEncoder(snake_case						,					name="""encoder"""						)
											snake_case_			:str						=      tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case						)






					@unpack_inputs
					def        lowerCAmelCase_    (	self:							List[str]						,					snake_case:							tf.Tensor						,					snake_case:							Optional[bool] = None						,					snake_case:							Optional[bool] = None						,					snake_case:							bool = False						,					)       ->     Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
											snake_case_			:Tuple						=      (
											    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_			:int						=      tf.transpose(snake_case						,					perm=[0, 2, 3, 1]						)
											snake_case_			:Dict						=      self.embedder(snake_case						,					training=snake_case						)

											snake_case_			:Any						=      self.encoder(
											    snake_case						,					output_hidden_states=snake_case						,					return_dict=snake_case						,					training=snake_case						)

											snake_case_			:Dict						=      encoder_outputs[0]

											snake_case_			:str						=      self.pooler(snake_case						)

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

											snake_case_			: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."""   ,						_lowerCAmelCase   ,						)
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					def __init__(	self:							List[str]						,					snake_case:							ResNetConfig						,					**snake_case:							Dict						)       ->     None:
											super().__init__(snake_case						,					**snake_case						)
											snake_case_			: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        lowerCAmelCase_    (	self:							Any						,					snake_case:							tf.Tensor						,					snake_case:							Optional[bool] = None						,					snake_case:							Optional[bool] = None						,					snake_case:							bool = False						,					)       ->     Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
											snake_case_			:str						=      (
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											snake_case_			:Tuple						=      return_dict if return_dict is not None else self.config.use_return_dict

											snake_case_			:Tuple						=      self.resnet(
											    pixel_values=snake_case						,					output_hidden_states=snake_case						,					return_dict=snake_case						,					training=snake_case						,					)
											return resnet_outputs

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






					'''simple docstring'''





					def __init__(	self:							Tuple						,					snake_case:							ResNetConfig						,					**snake_case:							Dict						)       ->     None:
											super().__init__(snake_case						,					**snake_case						)
											snake_case_			:Optional[Any]						=      config.num_labels
											snake_case_			:Dict						=      TFResNetMainLayer(snake_case						,					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        lowerCAmelCase_    (	self:							Any						,					snake_case:							tf.Tensor						)       ->     tf.Tensor:
											snake_case_			:Union[str, Any]						=      tf.keras.layers.Flatten()(snake_case						)
											snake_case_			: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        lowerCAmelCase_    (	self:							Tuple						,					snake_case:							tf.Tensor = None						,					snake_case:							tf.Tensor = None						,					snake_case:							bool = None						,					snake_case:							bool = None						,					snake_case:							bool = False						,					)       ->     Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
											snake_case_			:str						=      return_dict if return_dict is not None else self.config.use_return_dict

											snake_case_			:Optional[Any]						=      self.resnet(
											    snake_case						,					output_hidden_states=snake_case						,					return_dict=snake_case						,					training=snake_case						)

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

											snake_case_			:Optional[Any]						=      self.classifier(snake_case						)

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

											if not return_dict:
																	snake_case_			:Union[str, 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						)








"""simple docstring"""
from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
__a     =    9.8_06_65
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						if not isinstance(_lowercase, (int, float)     ):
												raise TypeError("""Invalid velocity. Should be a positive number."""     )

						if not isinstance(_lowercase, (int, float)     ):
												raise TypeError("""Invalid angle. Range is 1-90 degrees."""     )

						# Ensure valid angle
						if angle > 90 or angle < 1:
												raise ValueError("""Invalid angle. Range is 1-90 degrees."""     )

						# Ensure valid velocity
						if init_velocity < 0:
												raise ValueError("""Invalid velocity. Should be a positive number."""     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						check_args(_lowercase, _lowercase     )
						snake_case_			:str						=      angle_to_radians(2 * angle     )
						return round(init_velocity**2 * sin(_lowercase     ) / g, 2     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						check_args(_lowercase, _lowercase     )
						snake_case_			:str						=      angle_to_radians(_lowercase     )
						return round(init_velocity**2 * sin(_lowercase     ) ** 2 / (2 * g), 2     )
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						check_args(_lowercase, _lowercase     )
						snake_case_			:Optional[int]						=      angle_to_radians(_lowercase     )
						return round(2 * init_velocity * sin(_lowercase     ) / g, 2     )
def        A_  (      ):







						'''simple docstring'''


						snake_case_,	snake_case_			:List[str]						=      25, 20
						assert horizontal_distance(_lowercase, _lowercase     ) == 40.97
						assert max_height(_lowercase, _lowercase     ) == 3.73
						assert total_time(_lowercase, _lowercase     ) == 1.74


if __name__ == "__main__":
						from doctest import testmod

						testmod()

						# Get input from user
						__a     =    float(input("Initial Velocity: ").strip())

						# Get input from user
						__a     =    float(input("angle: ").strip())

						# Print results
						print()
						print("Results: ")
						print(F"""Horizontal Distance: {horizontal_distance(init_vel, angle)!s} [m]""")
						print(F"""Maximum Height: {max_height(init_vel, angle)!s} [m]""")
						print(F"""Total Time: {total_time(init_vel, angle)!s} [s]""")
