import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCamelCase__											=					False
try:
      UpperCamelCase__											=					_is_package_available('google.colab')
except ModuleNotFoundError:
      pass





@input.register
class      A							:


   def __init__(self     :						Optional[int]       ,  __UpperCAmelCase     :						str = None       ,  __UpperCAmelCase     :						list = []		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  0
         UpperCAmelCase__								=  choices
         UpperCAmelCase__								=  prompt
         if sys.platform == "win32":
               UpperCAmelCase__								=  "*"
         else:
               UpperCAmelCase__								=  "➔ "


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						str = ""		)						->				Optional[int]:
         """simple docstring"""



         if sys.platform != "win32":
               writeColor(self.choices[index]       ,  3_2       ,  __UpperCAmelCase		)
         else:
               forceWrite(self.choices[index]       ,  __UpperCAmelCase		)


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						int		)						->				Dict:
         """simple docstring"""



         if index == self.position:
               forceWrite(f""" {self.arrow_char} """		)
               self.write_choice(__UpperCAmelCase		)
         else:
               forceWrite(f"""    {self.choices[index]}"""		)
         reset_cursor()


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Direction       ,  __UpperCAmelCase     :						int = 1		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.position
         if direction == Direction.DOWN:
               if self.position + 1 >= len(self.choices		):
                     return
               self.position += num_spaces
         else:
               if self.position - 1 < 0:
                     return
               self.position -= num_spaces
         clear_line()
         self.print_choice(__UpperCAmelCase		)
         move_cursor(__UpperCAmelCase       ,  direction.name		)
         self.print_choice(self.position		)


   @input.mark(KEYMAP["up"]		)
   def 			lowercase_      (self     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         self.move_direction(Direction.UP		)


   @input.mark(KEYMAP["down"]		)
   def 			lowercase_      (self     :						Any		)						->				Optional[int]:
         """simple docstring"""



         self.move_direction(Direction.DOWN		)


   @input.mark(KEYMAP["newline"]		)
   def 			lowercase_      (self     :						List[str]		)						->				Optional[Any]:
         """simple docstring"""



         move_cursor(len(self.choices		) - self.position       ,  "DOWN"		)
         return self.position


   @input.mark(KEYMAP["interrupt"]		)
   def 			lowercase_      (self     :						Tuple		)						->				Union[str, Any]:
         """simple docstring"""



         move_cursor(len(self.choices		) - self.position       ,  "DOWN"		)
         raise KeyboardInterrupt


   @input.mark_multiple(*[KEYMAP[str(__UpperCAmelCase		)] for number in range(1_0		)]		)
   def 			lowercase_      (self     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  int(chr(self.current_selection		)		)
         UpperCAmelCase__								=  index - self.position
         if index == self.position:
               return
         if index < len(self.choices		):
               if self.position > index:
                     self.move_direction(Direction.UP       ,  -movement		)
               elif self.position < index:
                     self.move_direction(Direction.DOWN       ,  __UpperCAmelCase		)
               else:
                     return
         else:
               return





   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int = 0		)						->				Union[str, Any]:
         """simple docstring"""



         if self.prompt:
               linebreak()
               forceWrite(self.prompt       ,  "\n"		)
               if in_colab:
                     forceWrite("Please input a choice index (starting from 0), and press enter"       ,  "\n"		)
               else:
                     forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"       ,  "\n"		)
         UpperCAmelCase__								=  default_choice
         for i in range(len(self.choices		)		):
               self.print_choice(__UpperCAmelCase		)
               forceWrite("\n"		)
         move_cursor(len(self.choices		) - self.position       ,  "UP"		)
         with cursor.hide():
               while True:
                     if in_colab:
                           try:
                                 UpperCAmelCase__								=  int(builtins.input()		)
                           except ValueError:
                                 UpperCAmelCase__								=  default_choice
                     else:
                           UpperCAmelCase__								=  self.handle_input()
                     if choice is not None:
                           reset_cursor()
                           for _ in range(len(self.choices		) + 1		):
                                 move_cursor(1       ,  "UP"		)
                                 clear_line()
                           self.write_choice(__UpperCAmelCase       ,  "\n"		)
                           return choice




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      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  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:
               UpperCAmelCase__								=  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       ,  )



