
'''simple docstring'''

from math import radians as angle_to_radians
from math import sin

# Acceleration Constant on Earth (unit m/s^2)
_UpperCAmelCase   :      Optional[Any] 						=  9.8_0665







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      None:

		# Ensure valid instance
		if not isinstance(UpperCamelCase			,(int, float)	):
				raise TypeError('''Invalid velocity. Should be a positive number.'''	)

		if not isinstance(UpperCamelCase			,(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 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      float:
		check_args(UpperCamelCase			,UpperCamelCase	)
		_UpperCamelCase       : List[str]    =      angle_to_radians(2 * angle	)
		return round(init_velocity**2 * sin(UpperCamelCase	) / g			,2	)







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      float:
		check_args(UpperCamelCase			,UpperCamelCase	)
		_UpperCamelCase       : int    =      angle_to_radians(UpperCamelCase	)
		return round(init_velocity**2 * sin(UpperCamelCase	) ** 2 / (2 * g)			,2	)







def 						snake_case__	(      UpperCamelCase			,UpperCamelCase	) ->      float:
		check_args(UpperCamelCase			,UpperCamelCase	)
		_UpperCamelCase       : Dict    =      angle_to_radians(UpperCamelCase	)
		return round(2 * init_velocity * sin(UpperCamelCase	) / g			,2	)







def 						snake_case__	(      ) ->      None:
		_UpperCamelCase,							_UpperCamelCase       : Any    =      25, 20
		assert horizontal_distance(UpperCamelCase			,UpperCamelCase	) == 40.97
		assert max_height(UpperCamelCase			,UpperCamelCase	) == 3.73
		assert total_time(UpperCamelCase			,UpperCamelCase	) == 1.74


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

	testmod()

	# Get input from user
	_UpperCAmelCase   :      List[Any] 						=  float(input("""Initial Velocity: """).strip())

	# Get input from user
	_UpperCAmelCase   :      str 						=  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]""")







'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

_UpperCAmelCase   :      Any 						=  {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		A__  :   List[str]          =    'bigbird_pegasus'
		A__  :   Union[str, Any]          =    ['past_key_values']
		A__  :   Optional[int]          =    {
		    'num_attention_heads': 'encoder_attention_heads',
		    'hidden_size': 'd_model',
		    'attention_probs_dropout_prob': 'attention_dropout',
		}
		def __init__(    self  ,					_snake_case=96103  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu_new"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=2  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=1  ,					_snake_case="block_sparse"  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=False  ,					**_snake_case  ,					)				->    Optional[Any]:
				_UpperCamelCase       : str    =      vocab_size
				_UpperCamelCase       : Tuple    =      max_position_embeddings
				_UpperCamelCase       : int    =      d_model
				_UpperCamelCase       : List[str]    =      encoder_ffn_dim
				_UpperCamelCase       : str    =      encoder_layers
				_UpperCamelCase       : Dict    =      encoder_attention_heads
				_UpperCamelCase       : str    =      decoder_ffn_dim
				_UpperCamelCase       : List[str]    =      decoder_layers
				_UpperCamelCase       : Any    =      decoder_attention_heads
				_UpperCamelCase       : str    =      dropout
				_UpperCamelCase       : int    =      attention_dropout
				_UpperCamelCase       : Optional[int]    =      activation_dropout
				_UpperCamelCase       : Optional[Any]    =      activation_function
				_UpperCamelCase       : Tuple    =      init_std
				_UpperCamelCase       : Any    =      encoder_layerdrop
				_UpperCamelCase       : List[str]    =      decoder_layerdrop
				_UpperCamelCase       : Any    =      classifier_dropout
				_UpperCamelCase       : int    =      use_cache
				_UpperCamelCase       : Union[str, Any]    =      encoder_layers
				_UpperCamelCase       : str    =      scale_embedding  # scale factor will be sqrt(d_model) if True

				# extra config
				_UpperCamelCase       : str    =      attention_type
				_UpperCamelCase       : Tuple    =      block_size
				_UpperCamelCase       : Optional[Any]    =      num_random_blocks
				_UpperCamelCase       : Dict    =      use_bias

				super().__init__(
				    pad_token_id=_snake_case  ,					bos_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_encoder_decoder=_snake_case  ,					decoder_start_token_id=_snake_case  ,					**_snake_case  ,					)







class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		@property
		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : str    =      OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]     )

						if self.use_past:
								_UpperCamelCase       : List[Any]    =      {0: '''batch'''}
								_UpperCamelCase       : Tuple    =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}
								_UpperCamelCase       : Union[str, Any]    =      {0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
								self.fill_with_past_key_values_(_snake_case  ,					direction='''inputs'''     )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						_UpperCamelCase       : Optional[int]    =      OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]     )
						if self.use_past:
								_UpperCamelCase,							_UpperCamelCase       : List[str]    =      self.num_layers
								for i in range(_snake_case     ):
										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
										_UpperCamelCase       : Dict    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
						_UpperCamelCase       : Optional[int]    =      OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
						        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
						    ]     )

				return common_inputs
		@property
		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : int    =      super().outputs
				else:
						_UpperCamelCase       : Optional[int]    =      super(_snake_case  ,					self     ).outputs
						if self.use_past:
								_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
								for i in range(_snake_case     ):
										_UpperCamelCase       : int    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				_UpperCamelCase       : List[str]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )

				# Generate decoder inputs
				_UpperCamelCase       : Optional[int]    =      seq_length if not self.use_past else 1
				_UpperCamelCase       : Optional[int]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
				_UpperCamelCase       : Optional[int]    =      {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				_UpperCamelCase       : Any    =      dict(**_snake_case  ,					**_snake_case     )

				if self.use_past:
						if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
						else:
								import torch
						_UpperCamelCase,							_UpperCamelCase       : Dict    =      common_inputs['''input_ids'''].shape
						_UpperCamelCase       : Dict    =      common_inputs['''decoder_input_ids'''].shape[1]
						_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_attention_heads
						_UpperCamelCase       : List[Any]    =      (
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						_UpperCamelCase       : int    =      decoder_seq_length + 3
						_UpperCamelCase       : Union[str, Any]    =      (
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						_UpperCamelCase       : Any    =      torch.cat(
						    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,					_snake_case     )]  ,					dim=1     )

						_UpperCamelCase       : Any    =      []
						# If the number of encoder and decoder layers are present in the model configuration, both are considered
						_UpperCamelCase,							_UpperCamelCase       : int    =      self.num_layers
						_UpperCamelCase       : Union[str, Any]    =      min(_snake_case  ,					_snake_case     )
						_UpperCamelCase       : Union[str, Any]    =      max(_snake_case  ,					_snake_case     ) - min_num_layers
						_UpperCamelCase       : Optional[int]    =      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

						for _ in range(_snake_case     ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								    )     )
						# TODO: test this.
						_UpperCamelCase       : Optional[Any]    =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
						for _ in range(_snake_case  ,					_snake_case     ):
								common_inputs["past_key_values"].append((torch.zeros(_snake_case     ), torch.zeros(_snake_case     ))     )
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				_UpperCamelCase       : Dict    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )

				if self.use_past:
						if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''     )
						else:
								import torch
						_UpperCamelCase,							_UpperCamelCase       : Union[str, Any]    =      common_inputs['''input_ids'''].shape
						# Not using the same length for past_key_values
						_UpperCamelCase       : str    =      seqlen + 2
						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_layers
						_UpperCamelCase,							_UpperCamelCase       : str    =      self.num_attention_heads
						_UpperCamelCase       : List[str]    =      (
						    batch,
						    num_encoder_attention_heads,
						    past_key_values_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)

						_UpperCamelCase       : Optional[int]    =      common_inputs['''attention_mask'''].dtype
						_UpperCamelCase       : Union[str, Any]    =      torch.cat(
						    [common_inputs['''attention_mask'''], torch.ones(_snake_case  ,					_snake_case  ,					dtype=_snake_case     )]  ,					dim=1     )
						_UpperCamelCase       : int    =      [
						    (torch.zeros(_snake_case     ), torch.zeros(_snake_case     )) for _ in range(_snake_case     )
						]
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				# Copied from OnnxConfig.generate_dummy_inputs
				# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
				# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
				_UpperCamelCase       : Union[str, Any]    =      compute_effective_axis_dimension(
				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_batch  ,					num_token_to_add=0     )

				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				_UpperCamelCase       : List[Any]    =      tokenizer.num_special_tokens_to_add(_snake_case     )
				_UpperCamelCase       : List[str]    =      compute_effective_axis_dimension(
				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_sequence  ,					num_token_to_add=_snake_case     )

				# Generate dummy inputs according to compute batch and sequence
				_UpperCamelCase       : Optional[Any]    =      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
				_UpperCamelCase       : Any    =      dict(tokenizer(_snake_case  ,					return_tensors=_snake_case     )     )
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )

				elif self.task == "causal-lm":
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_causal_lm(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )
				else:
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )

				return common_inputs




		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Tuple:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : List[Any]    =      super()._flatten_past_key_values_(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
				else:
						_UpperCamelCase       : int    =      super(_snake_case  ,					self     )._flatten_past_key_values_(
						    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )





