



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import torch






class SCREAMING_SNAKE_CASE__					(		TensorFormatter[Mapping, "torch.Tensor", Mapping]   ):




    '''simple docstring'''






    def __init__(	self,		lowerCamelCase__=None,		**lowerCamelCase__		):
        super().__init__(features=lowerCamelCase__		)
        A			:	Any										=       torch_tensor_kwargs
        import torch  # noqa import torch at initialization






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        import torch

        if isinstance(lowerCamelCase__,		lowerCamelCase__		) and column:
            if all(
                isinstance(lowerCamelCase__,		torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
                for x in column		):
                return torch.stack(lowerCamelCase__		)
        return column






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        import torch

        if isinstance(lowerCamelCase__,		(str, bytes, type(lowerCamelCase__		))		):
            return value
        elif isinstance(lowerCamelCase__,		(np.character, np.ndarray)		) and np.issubdtype(value.dtype,		np.character		):
            return value.tolist()

        A			:	Tuple										=       {}

        if isinstance(lowerCamelCase__,		(np.number, np.ndarray)		) and np.issubdtype(value.dtype,		np.integer		):
            A			:	List[Any]										=       {"""dtype""": torch.intaa}
        elif isinstance(lowerCamelCase__,		(np.number, np.ndarray)		) and np.issubdtype(value.dtype,		np.floating		):
            A			:	Any										=       {"""dtype""": torch.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(lowerCamelCase__,		PIL.Image.Image		):
                A			:	Tuple										=       np.asarray(lowerCamelCase__		)
        return torch.tensor(lowerCamelCase__,		**{**default_dtype, **self.torch_tensor_kwargs}		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        import torch

        # support for torch, tf, jax etc.
        if hasattr(lowerCamelCase__,		"""__array__"""		) and not isinstance(lowerCamelCase__,		torch.Tensor		):
            A			:	List[Any]										=       data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(lowerCamelCase__,		np.ndarray		):
            if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                return self._consolidate([self.recursive_tensorize(lowerCamelCase__		) for substruct in data_struct]		)
        elif isinstance(lowerCamelCase__,		(list, tuple)		):
            return self._consolidate([self.recursive_tensorize(lowerCamelCase__		) for substruct in data_struct]		)
        return self._tensorize(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return map_nested(self._recursive_tensorize,		lowerCamelCase__,		map_list=lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Optional[Any]										=       self.numpy_arrow_extractor().extract_row(lowerCamelCase__		)
        A			:	str										=       self.python_features_decoder.decode_row(lowerCamelCase__		)
        return self.recursive_tensorize(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Dict										=       self.numpy_arrow_extractor().extract_column(lowerCamelCase__		)
        A			:	Tuple										=       self.python_features_decoder.decode_column(lowerCamelCase__,		pa_table.column_names[0]		)
        A			:	Union[str, Any]										=       self.recursive_tensorize(lowerCamelCase__		)
        A			:	Optional[Any]										=       self._consolidate(lowerCamelCase__		)
        return column






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       self.numpy_arrow_extractor().extract_batch(lowerCamelCase__		)
        A			:	List[Any]										=       self.python_features_decoder.decode_batch(lowerCamelCase__		)
        A			:	Dict										=       self.recursive_tensorize(lowerCamelCase__		)
        for column_name in batch:
            A			:	Optional[int]										=       self._consolidate(batch[column_name]		)
        return batch







from random import randint, random




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase = False				,					_lowerCAmelCase = False				,					_lowerCAmelCase = 5				,					)	->  list:



    """simple docstring"""






    A			:	Any										=       [[-1] * number_of_cells]  # Create a highway without any car
    A			:	Tuple										=       0
    A			:	Dict										=       max(_lowerCAmelCase				,					0		)
    while i < number_of_cells:
        A			:	Any										=       (
            randint(0				,					_lowerCAmelCase		) if random_speed else initial_speed
        )  # Place the cars
        i += (
            randint(1				,					max_speed * 2		) if random_frequency else frequency
        )  # Arbitrary number, may need tuning
    return highway




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	List[str]										=       0
    A			:	Dict										=       highway_now[car_index + 1 :]
    for cell in range(len(_lowerCAmelCase		)		):  # May need a better name for this
        if cells[cell] != -1:  # If the cell is not empty then
            return distance  # we have the distance we wanted
        distance += 1
    # Here if the car is near the end of the highway
    return distance + get_distance(_lowerCAmelCase				,					-1		)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  list:



    """simple docstring"""






    A			:	str										=       len(_lowerCAmelCase		)
    # Beforce calculations, the highway is empty
    A			:	Any										=       [-1] * number_of_cells

    for car_index in range(_lowerCAmelCase		):
        if highway_now[car_index] != -1:
            # Add 1 to the current speed of the car and cap the speed
            A			:	str										=       min(highway_now[car_index] + 1				,					_lowerCAmelCase		)
            # Number of empty cell before the next car
            A			:	Optional[int]										=       get_distance(_lowerCAmelCase				,					_lowerCAmelCase		) - 1
            # We can't have the car causing an accident
            A			:	Any										=       min(next_highway[car_index]				,					_lowerCAmelCase		)
            if random() < probability:
                # Randomly, a driver will slow down
                A			:	Optional[Any]										=       max(next_highway[car_index] - 1				,					0		)
    return next_highway




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  list:



    """simple docstring"""






    A			:	Any										=       len(highway[0]		)

    for i in range(_lowerCAmelCase		):
        A			:	Optional[int]										=       update(highway[i]				,					_lowerCAmelCase				,					_lowerCAmelCase		)
        A			:	Tuple										=       [-1] * number_of_cells

        for car_index in range(_lowerCAmelCase		):
            A			:	Dict										=       next_speeds_calculated[car_index]
            if speed != -1:
                # Change the position based on the speed (with % to create the loop)
                A			:	Optional[int]										=       (car_index + speed) % number_of_cells
                # Commit the change of position
                A			:	Dict										=       speed
        highway.append(_lowerCAmelCase		)

    return highway


if __name__ == "__main__":
     import doctest

     doctest.testmod()


