




from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_  :      Union[str, Any]		=		logging.get_logger(__name__)

snake_case_  :      Optional[int]		=		{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class 					snake_case_   (     __A	):



				'''simple docstring'''
				lowerCamelCase       =  "time_series_transformer"
				lowerCamelCase       =  {
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				    "num_hidden_layers": "encoder_layers",
				}





				def __init__(      self							:   str      ,							__magic_name__							:   Optional[int] = None      ,							__magic_name__							:   Optional[int] = None      ,							__magic_name__							:   str = "student_t"      ,							__magic_name__							:   str = "nll"      ,							__magic_name__							:   int = 1      ,							__magic_name__							:   List[int] = [1, 2, 3, 4, 5, 6, 7]      ,							__magic_name__							:   Optional[Union[str, bool]] = "mean"      ,							__magic_name__							:   int = 0      ,							__magic_name__							:   int = 0      ,							__magic_name__							:   int = 0      ,							__magic_name__							:   int = 0      ,							__magic_name__							:   Optional[List[int]] = None      ,							__magic_name__							:   Optional[List[int]] = None      ,							__magic_name__							:   int = 32      ,							__magic_name__							:   int = 32      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   int = 2      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   str = "gelu"      ,							__magic_name__							:   int = 64      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   float = 0.1      ,							__magic_name__							:   int = 100      ,							__magic_name__							:   float = 0.02      ,							__magic_name__							:   Optional[int]=True      ,							**__magic_name__							:   str      ,							)							->			int:
											# time series specific configuration
											lowerCamelCase_			:    Any   						=      prediction_length
											lowerCamelCase_			:    int   						=      context_length or prediction_length
											lowerCamelCase_			:    Union[str, Any]   						=      distribution_output
											lowerCamelCase_			:    List[Any]   						=      loss
											lowerCamelCase_			:    Union[str, Any]   						=      input_size
											lowerCamelCase_			:    Optional[Any]   						=      num_time_features
											lowerCamelCase_			:    str   						=      lags_sequence
											lowerCamelCase_			:    Union[str, Any]   						=      scaling
											lowerCamelCase_			:    Union[str, Any]   						=      num_dynamic_real_features
											lowerCamelCase_			:    Optional[int]   						=      num_static_real_features
											lowerCamelCase_			:    Optional[Any]   						=      num_static_categorical_features
											if cardinality and num_static_categorical_features > 0:
																		if len(__a							) != num_static_categorical_features:
																									raise ValueError(
																									    "The cardinality should be a list of the same length as `num_static_categorical_features`"							)
																		lowerCamelCase_			:    int   						=      cardinality
											else:
																		lowerCamelCase_			:    Tuple   						=      [0]
											if embedding_dimension and num_static_categorical_features > 0:
																		if len(__a							) != num_static_categorical_features:
																									raise ValueError(
																									    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"							)
																		lowerCamelCase_			:    Dict   						=      embedding_dimension
											else:
																		lowerCamelCase_			:    Any   						=      [min(50      ,							(cat + 1) // 2							) for cat in self.cardinality]
											lowerCamelCase_			:    List[str]   						=      num_parallel_samples

											# Transformer architecture configuration
											lowerCamelCase_			:    Union[str, Any]   						=      input_size * len(__a							) + self._number_of_features
											lowerCamelCase_			:    str   						=      d_model
											lowerCamelCase_			:    Union[str, Any]   						=      encoder_attention_heads
											lowerCamelCase_			:    Union[str, Any]   						=      decoder_attention_heads
											lowerCamelCase_			:    Union[str, Any]   						=      encoder_ffn_dim
											lowerCamelCase_			:    str   						=      decoder_ffn_dim
											lowerCamelCase_			:    str   						=      encoder_layers
											lowerCamelCase_			:    int   						=      decoder_layers

											lowerCamelCase_			:    List[str]   						=      dropout
											lowerCamelCase_			:    Optional[Any]   						=      attention_dropout
											lowerCamelCase_			:    List[Any]   						=      activation_dropout
											lowerCamelCase_			:    List[Any]   						=      encoder_layerdrop
											lowerCamelCase_			:    Optional[Any]   						=      decoder_layerdrop

											lowerCamelCase_			:    List[Any]   						=      activation_function
											lowerCamelCase_			:    str   						=      init_std

											lowerCamelCase_			:    Union[str, Any]   						=      use_cache

											super().__init__(is_encoder_decoder=__a      ,							**__a							)





				@property
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			int:
											return (
											    sum(self.embedding_dimension							)
											    + self.num_dynamic_real_features
											    + self.num_time_features
											    + self.num_static_real_features
											    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
											)


'''simple docstring'''


from random import randint, random





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: bool = False     ,       UpperCamelCase__					: int = 5     ,       ):


	"""simple docstring"""
	__UpperCAmelCase      					=      [[-1] * number_of_cells]  # Create a highway without any car
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      max(UpperCamelCase__     ,       0							)
	while i < number_of_cells:
		__UpperCAmelCase      					=      (
		    randint(0     ,       UpperCamelCase__							) 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    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      0
	__UpperCAmelCase      					=      highway_now[car_index + 1 :]
	for cell in range(len(UpperCamelCase__							)							):  # 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(UpperCamelCase__     ,       -1							)





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      len(UpperCamelCase__							)
	# Beforce calculations, the highway is empty
	__UpperCAmelCase      					=      [-1] * number_of_cells

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





def    lowerCAmelCase       (		UpperCamelCase__					: list     ,       UpperCamelCase__					: int     ,       UpperCamelCase__					: float     ,       UpperCamelCase__					: int							):


	"""simple docstring"""
	__UpperCAmelCase      					=      len(highway[0]							)

	for i in range(UpperCamelCase__							):
		__UpperCAmelCase      					=      update(highway[i]     ,       UpperCamelCase__     ,       UpperCamelCase__							)
		__UpperCAmelCase      					=      [-1] * number_of_cells

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

	return highway


if __name__ == "__main__":
					import doctest

					doctest.testmod()
