






'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def 					__lowercase						(			__lowercase       )       ->						Dict:

      '''simple docstring'''


      _A			     =	[
          "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(__lowercase   ,  __lowercase       )

def 					__lowercase						(			__lowercase       )       ->						List[Any]:

      '''simple docstring'''


      _A			     =	list(s_dict.keys()       )
      for key in keys:
            if "transformer_layers" in key:
                  _A			     =	s_dict.pop(__lowercase       )
            elif "subsample" in key:
                  _A			     =	s_dict.pop(__lowercase       )

def 					__lowercase						(			__lowercase       )       ->						Tuple:

      '''simple docstring'''


      _A     ,	_A			     =	emb.weight.shape
      _A			     =	nn.Linear(__lowercase   ,  __lowercase   ,  bias=__lowercase       )
      _A			     =	emb.weight.data
      return lin_layer

def 					__lowercase						(			__lowercase   ,  __lowercase       )       ->						List[Any]:

      '''simple docstring'''


      _A			     =	torch.load(__lowercase   ,  map_location="cpu"       )
      _A			     =	mam_aaa["args"]
      _A			     =	mam_aaa["model"]
      _A			     =	state_dict["decoder.output_projection.weight"]

      remove_ignore_keys_(__lowercase       )
      rename_keys(__lowercase       )

      _A			     =	state_dict["decoder.embed_tokens.weight"].shape[0]

      _A			     =	args.share_decoder_input_output_embed

      _A			     =	[int(__lowercase       ) for i in args.conv_kernel_sizes.split(","       )]
      _A			     =	SpeechaTextConfig(
          vocab_size=__lowercase   ,  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(__lowercase       )   ,  conv_channels=args.conv_channels   ,  conv_kernel_sizes=__lowercase   ,  input_feat_per_channel=args.input_feat_per_channel   ,  input_channels=args.input_channels   ,  tie_word_embeddings=__lowercase   ,  num_beams=5   ,  max_length=200   ,  use_cache=__lowercase   ,  decoder_start_token_id=2   ,  early_stopping=__lowercase   ,  )

      _A			     =	SpeechaTextForConditionalGeneration(__lowercase       )
      _A     ,	_A			     =	model.model.load_state_dict(__lowercase   ,  strict=__lowercase       )
      if len(__lowercase       ) > 0 and not set(__lowercase       ) <= {
          "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:
            _A			     =	make_linear_from_emb(model.model.decoder.embed_tokens       )
      else:
            _A			     =	lm_head_weights

      model.save_pretrained(__lowercase       )


if __name__ == "__main__":
    lowerCamelCase_												=					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.''')
    lowerCamelCase_												=					parser.parse_args()
    convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







"""simple docstring"""




import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ArgumentParser(
							    description=(
							        """PyTorch TPU distributed training launch """
							        """helper utility that will spawn up """
							        """multiple distributed processes"""
							    )						)

							# Optional arguments for the launch helper
							parser.add_argument("""--num_cores"""   ,type=_snake_case   ,default=1   ,help="""Number of TPU cores to use (1 or 8)."""						)

							# positional
							parser.add_argument(
							    """training_script"""   ,type=_snake_case   ,help=(
							        """The full path to the single TPU training """
							        """program/script to be launched in parallel, """
							        """followed by all the arguments for the """
							        """training script"""
							    )   ,)

							# rest from the training program
							parser.add_argument("""training_script_args"""   ,nargs=_snake_case						)

							return parser.parse_args()

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       int           =   parse_args()

							# Import training_script as a module.
							SCREAMING_SNAKE_CASE__						:       Dict           =   Path(args.training_script						)
							sys.path.append(str(script_fpath.parent.resolve()						)						)
							SCREAMING_SNAKE_CASE__						:       int           =   script_fpath.stem
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   importlib.import_module(_snake_case						)

							# Patch sys.argv
							SCREAMING_SNAKE_CASE__						:       str           =   [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores						)]
							xmp.spawn(mod._mp_fn   ,args=()   ,nprocs=args.num_cores						)


if __name__ == "__main__":
					main()




