


'''simple docstring'''



import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						Dict:
     __lowerCamelCase  :      Any														=						[
         'encoder.version',
         'decoder.version',
         'model.encoder.version',
         'model.decoder.version',
         'decoder.output_projection.weight',
         '_float_tensor',
         'encoder.embed_positions._float_tensor',
         'decoder.embed_positions._float_tensor',
     ]
     for k in ignore_keys:
          state_dict.pop(UpperCAmelCase_ ,       UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						Optional[Any]:
     __lowerCamelCase  :      Optional[Any]														=						list(s_dict.keys()				)
     for key in keys:
          if "transformer_layers" in key:
               __lowerCamelCase  :      List[str]														=						s_dict.pop(UpperCAmelCase_				)
          elif "subsample" in key:
               __lowerCamelCase  :      Optional[int]														=						s_dict.pop(UpperCAmelCase_				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						int:
     __lowerCamelCase							,			__lowerCamelCase  :      List[str]														=						emb.weight.shape
     __lowerCamelCase  :      List[str]														=						nn.Linear(UpperCAmelCase_ ,       UpperCAmelCase_ ,       bias=UpperCAmelCase_				)
     __lowerCamelCase  :      int														=						emb.weight.data
     return lin_layer



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							Any				)	->						List[Any]:
     __lowerCamelCase  :      Tuple														=						torch.load(UpperCAmelCase_ ,       map_location='cpu'				)
     __lowerCamelCase  :      Any														=						mam_aaa['args']
     __lowerCamelCase  :      Any														=						mam_aaa['model']
     __lowerCamelCase  :      Optional[Any]														=						state_dict['decoder.output_projection.weight']

     remove_ignore_keys_(UpperCAmelCase_				)
     rename_keys(UpperCAmelCase_				)

     __lowerCamelCase  :      Tuple														=						state_dict['decoder.embed_tokens.weight'].shape[0]

     __lowerCamelCase  :      Optional[int]														=						args.share_decoder_input_output_embed

     __lowerCamelCase  :      Optional[int]														=						[int(UpperCAmelCase_				) for i in args.conv_kernel_sizes.split(','				)]
     __lowerCamelCase  :      int														=						SpeechaTextConfig(
         vocab_size=UpperCAmelCase_ ,       max_source_positions=args.max_source_positions ,       max_target_positions=args.max_target_positions ,       encoder_layers=args.encoder_layers ,       decoder_layers=args.decoder_layers ,       encoder_attention_heads=args.encoder_attention_heads ,       decoder_attention_heads=args.decoder_attention_heads ,       encoder_ffn_dim=args.encoder_ffn_embed_dim ,       decoder_ffn_dim=args.decoder_ffn_embed_dim ,       d_model=args.encoder_embed_dim ,       dropout=args.dropout ,       attention_dropout=args.attention_dropout ,       activation_dropout=args.activation_dropout ,       activation_function='relu' ,       num_conv_layers=len(UpperCAmelCase_				) ,       conv_channels=args.conv_channels ,       conv_kernel_sizes=UpperCAmelCase_ ,       input_feat_per_channel=args.input_feat_per_channel ,       input_channels=args.input_channels ,       tie_word_embeddings=UpperCAmelCase_ ,       num_beams=5 ,       max_length=2_00 ,       use_cache=UpperCAmelCase_ ,       decoder_start_token_id=2 ,       early_stopping=UpperCAmelCase_ ,       )

     __lowerCamelCase  :      str														=						SpeechaTextForConditionalGeneration(UpperCAmelCase_				)
     __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						model.model.load_state_dict(UpperCAmelCase_ ,       strict=UpperCAmelCase_				)
     if len(UpperCAmelCase_				) > 0 and not set(UpperCAmelCase_				) <= {
         "encoder.embed_positions.weights",
         "decoder.embed_positions.weights",
     }:
          raise ValueError(
              'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
              F' but all the following weights are missing {missing}'				)

     if tie_embeds:
          __lowerCamelCase  :      List[Any]														=						make_linear_from_emb(model.model.decoder.embed_tokens				)
     else:
          __lowerCamelCase  :      int														=						lm_head_weights

     model.save_pretrained(UpperCAmelCase_				)


if __name__ == "__main__":
      A__						:   Any				=    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--fairseq_path""", type=str, help="""Path to the fairseq model (.pt) file.""")
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__						:   List[str]				=    parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)








"""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


