
"""simple docstring"""




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


_A     :			str  						=			False
try:
   _A     :			Dict  						=			_is_package_available("""google.colab""")
except ModuleNotFoundError:
   pass

@input.register
class 						a__		:
   def __init__(     self      ,       _a = None      ,       _a = []							):
          lowercase       :							Union[str, Any]									=				0
          lowercase       :							str									=				choices
          lowercase       :							List[Any]									=				prompt
          if sys.platform == "win32":
                 lowercase       :							Union[str, Any]									=				"*"
          else:
                 lowercase       :							int									=				"➔ "
   def      __magic_name__     (     self      ,       _a      ,       _a = ""							):
          if sys.platform != "win32":
                 writeColor(self.choices[index]      ,       32      ,       _a							)
          else:
                 forceWrite(self.choices[index]      ,       _a							)
   def      __magic_name__     (     self      ,       _a							):
          if index == self.position:
                 forceWrite(f""" {self.arrow_char} """							)
                 self.write_choice(_a							)
          else:
                 forceWrite(f"""    {self.choices[index]}"""							)
          reset_cursor()
   def      __magic_name__     (     self      ,       _a      ,       _a = 1							):
          lowercase       :							Optional[int]									=				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(_a							)
          move_cursor(_a      ,       direction.name							)
          self.print_choice(self.position							)
   @input.mark(KEYMAP["up"]							)
   def      __magic_name__     (     self							):
          self.move_direction(Direction.UP							)
   @input.mark(KEYMAP["down"]							)
   def      __magic_name__     (     self							):
          self.move_direction(Direction.DOWN							)
   @input.mark(KEYMAP["newline"]							)
   def      __magic_name__     (     self							):
          move_cursor(len(self.choices							) - self.position      ,       "DOWN"							)
          return self.position
   @input.mark(KEYMAP["interrupt"]							)
   def      __magic_name__     (     self							):
          move_cursor(len(self.choices							) - self.position      ,       "DOWN"							)
          raise KeyboardInterrupt
   @input.mark_multiple(*[KEYMAP[str(_a							)] for number in range(10							)]							)
   def      __magic_name__     (     self							):
          lowercase       :							List[str]									=				int(chr(self.current_selection							)							)
          lowercase       :							Union[str, Any]									=				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      ,       _a							)
                 else:
                        return
          else:
                 return

   def      __magic_name__     (     self      ,       _a = 0							):
          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"							)
          lowercase       :							Tuple									=				default_choice
          for i in range(len(self.choices							)							):
                 self.print_choice(_a							)
                 forceWrite("\n"							)
          move_cursor(len(self.choices							) - self.position      ,       "UP"							)
          with cursor.hide():
                 while True:
                        if in_colab:
                               try:
                                      lowercase       :							Tuple									=				int(builtins.input()							)
                               except ValueError:
                                      lowercase       :							Optional[int]									=				default_choice
                        else:
                               lowercase       :							int									=				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(_a      ,       "\n"							)
                               return choice




"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     __magic_name__      ( __snake_case							:			List[Any]    )					->							Dict:
       lowercase       :							Optional[int]									=				[
           "decoder.version",
           "decoder.output_projection.weight",
           "_float_tensor",
           "decoder.embed_positions._float_tensor",
       ]
       for k in ignore_keys:
              state_dict.pop(__snake_case	,					__snake_case    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[Any]:
       lowercase						,       lowercase       :							Tuple									=				emb.weight.shape
       lowercase       :							str									=				nn.Linear(__snake_case	,					__snake_case	,					bias=__snake_case    )
       lowercase       :							Optional[Any]									=				emb.weight.data
       return lin_layer


def     __magic_name__      ( __snake_case							:			Any    )					->							str:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       lowercase       :							Optional[int]									=				Namespace(**checkpoint["cfg"]["model"]    )
       lowercase       :							Union[str, Any]									=				checkpoint["model"]
       remove_ignore_keys_(__snake_case    )
       lowercase       :							Optional[int]									=				state_dict["decoder.embed_tokens.weight"].shape[0]

       lowercase       :							int									=				{key.replace("decoder"	,					"model"    ): val for key, val in state_dict.items()}

       lowercase       :							List[Any]									=				XGLMConfig(
           vocab_size=__snake_case	,					max_position_embeddings=args.max_target_positions	,					num_layers=args.decoder_layers	,					attention_heads=args.decoder_attention_heads	,					ffn_dim=args.decoder_ffn_embed_dim	,					d_model=args.decoder_embed_dim	,					layerdrop=args.decoder_layerdrop	,					dropout=args.dropout	,					attention_dropout=args.attention_dropout	,					activation_dropout=args.activation_dropout	,					activation_function="gelu"	,					scale_embedding=not args.no_scale_embedding	,					tie_word_embeddings=args.share_decoder_input_output_embed	,					)

       lowercase       :							int									=				XGLMForCausalLM(__snake_case    )
       lowercase       :							Any									=				model.load_state_dict(__snake_case	,					strict=__snake_case    )
       print(__snake_case    )
       lowercase       :							Tuple									=				make_linear_from_emb(model.model.embed_tokens    )

       return model


if __name__ == "__main__":
   _A     :			List[Any]  						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   _A     :			Tuple  						=			parser.parse_args()
   _A     :			List[str]  						=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)


