





'''simple docstring'''




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None
     lowerCAmelCase						     =			None




class 					SCREAMING_SNAKE_CASE       (a__				):


     def __init__(		self		,      _UpperCAmelCase=1		,      _UpperCAmelCase=0		,      _UpperCAmelCase=2		,      _UpperCAmelCase=512		,      _UpperCAmelCase="cls"		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase		,      bos_token_id=_UpperCAmelCase		,      eos_token_id=_UpperCAmelCase		,      **_UpperCAmelCase)
          __A		:					Any						=			project_dim
          __A		:					List[str]						=			pooler_fn
          __A		:					Tuple						=			learn_encoder
          __A		:					Optional[int]						=			use_attention_mask




class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			[r'''pooler''', r'''logit_scale''']
     lowerCAmelCase						     =			[r'''position_ids''', r'''predictions.decoder.bias''']
     lowerCAmelCase						     =			'''roberta'''
     lowerCAmelCase						     =			RobertaSeriesConfig


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          super().__init__(_UpperCAmelCase)
          __A		:					List[str]						=			XLMRobertaModel(_UpperCAmelCase)
          __A		:					List[str]						=			nn.Linear(config.hidden_size		,      config.project_dim)
          __A		:					List[Any]						=			getattr(_UpperCAmelCase		,      'has_pre_transformation'		,      _UpperCAmelCase)
          if self.has_pre_transformation:
               __A		:					Tuple						=			nn.Linear(config.hidden_size		,      config.project_dim)
               __A		:					Optional[int]						=			nn.LayerNorm(config.hidden_size		,      eps=config.layer_norm_eps)
          self.post_init()


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      ):

          '''simple docstring'''






          __A		:					Optional[Any]						=			return_dict if return_dict is not None else self.config.use_return_dict

          __A		:					Any						=			self.base_model(
              input_ids=_UpperCAmelCase		,      attention_mask=_UpperCAmelCase		,      token_type_ids=_UpperCAmelCase		,      position_ids=_UpperCAmelCase		,      head_mask=_UpperCAmelCase		,      inputs_embeds=_UpperCAmelCase		,      encoder_hidden_states=_UpperCAmelCase		,      encoder_attention_mask=_UpperCAmelCase		,      output_attentions=_UpperCAmelCase		,      output_hidden_states=True if self.has_pre_transformation else output_hidden_states		,      return_dict=_UpperCAmelCase		,      )

          if self.has_pre_transformation:
               __A		:					Optional[int]						=			outputs['hidden_states'][-2]
               __A		:					Dict						=			self.pre_LN(_UpperCAmelCase)
               __A		:					Optional[Any]						=			self.transformation_pre(_UpperCAmelCase)

               return TransformationModelOutput(
                   projection_state=_UpperCAmelCase		,      last_hidden_state=outputs.last_hidden_state		,      hidden_states=outputs.hidden_states		,      attentions=outputs.attentions		,      )
          else:
               __A		:					Optional[Any]						=			self.transformation(outputs.last_hidden_state)
               return TransformationModelOutput(
                   projection_state=_UpperCAmelCase		,      last_hidden_state=outputs.last_hidden_state		,      hidden_states=outputs.hidden_states		,      attentions=outputs.attentions		,      )




"""simple docstring"""







from random import randint, random

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							int = 5     ,		):
						"""simple docstring"""






						snake_case   :			str						    = [[-1] * number_of_cells]  # Create a highway without any car
						snake_case   :			str						    = 0
						snake_case   :			Any						    = max(lowerCamelCase_     ,		0 )
						while i < number_of_cells:
												snake_case   :			Optional[int]						    = (
												    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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Any						    = 0
						snake_case   :			int						    = 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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			List[str]						    = len(lowerCamelCase_ )
						# Beforce calculations, the highway is empty
						snake_case   :			Dict						    = [-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
																		snake_case   :			Tuple						    = min(highway_now[car_index] + 1     ,		lowerCamelCase_ )
																		# Number of empty cell before the next car
																		snake_case   :			int						    = get_distance(lowerCamelCase_     ,		lowerCamelCase_ ) - 1
																		# We can't have the car causing an accident
																		snake_case   :			str						    = min(next_highway[car_index]     ,		lowerCamelCase_ )
																		if random() < probability:
																								# Randomly, a driver will slow down
																								snake_case   :			Optional[Any]						    = max(next_highway[car_index] - 1     ,		0 )
						return next_highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = len(highway[0] )

						for i in range(lowerCamelCase_ ):
												snake_case   :			Union[str, Any]						    = update(highway[i]     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Optional[int]						    = [-1] * number_of_cells

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

						return highway


if __name__ == "__main__":
			import doctest

			doctest.testmod()



