





import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    logging.get_logger("""transformers.models.speecht5""")

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
SCREAMING_SNAKE_CASE__							:      int         =    {
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
SCREAMING_SNAKE_CASE__							:      List[Any]         =    {
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
SCREAMING_SNAKE_CASE__							:      List[Any]         =    {
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
SCREAMING_SNAKE_CASE__							:      Any         =    {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
SCREAMING_SNAKE_CASE__							:      List[str]         =    {
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
SCREAMING_SNAKE_CASE__							:      List[Any]         =    {
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
SCREAMING_SNAKE_CASE__							:      Tuple         =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE__							:      int         =    {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE__							:      Any         =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE__							:      int         =    []
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    [
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
SCREAMING_SNAKE_CASE__							:      Any         =    IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
SCREAMING_SNAKE_CASE__							:      List[str]         =    IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   for attribute in key.split("." ):
      a__						:    Tuple   		=	getattr(lowerCamelCase				,			lowerCamelCase )

   if weight_type is not None:
      a__						:    Union[str, Any]   		=	getattr(lowerCamelCase				,			lowerCamelCase ).shape
   else:
      a__						:    Tuple   		=	hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
          F""" {value.shape} for {full_name}""" )

   if weight_type == "weight":
      a__						:    List[str]   		=	value
   elif weight_type == "weight_g":
      a__						:    Any   		=	value
   elif weight_type == "weight_v":
      a__						:    Tuple   		=	value
   elif weight_type == "bias":
      a__						:    Optional[int]   		=	value
   elif weight_type == "running_mean":
      a__						:    Optional[int]   		=	value
   elif weight_type == "running_var":
      a__						:    Dict   		=	value
   elif weight_type == "num_batches_tracked":
      a__						:    List[Any]   		=	value
   else:
      a__						:    Optional[Any]   		=	value

   logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.""" )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   for key in ignore_keys:
      if key.endswith(".*" ):
         if name.startswith(key[:-1] ):
            return True
      elif ".*." in key:
         a__		,  a__						:    List[str]   		=	key.split(".*." )
         if prefix in name and suffix in name:
            return True
      elif key in name:
         return True
   return False






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    List[Any]   		=	[]

   if task == "s2t":
      a__						:    Union[str, Any]   		=	hf_model.speechta.encoder.prenet.feature_encoder
      a__						:    int   		=	MAPPING_S2T
      a__						:    int   		=	IGNORE_KEYS_S2T
   elif task == "t2s":
      a__						:    str   		=	None
      a__						:    Any   		=	MAPPING_T2S
      a__						:    Optional[int]   		=	IGNORE_KEYS_T2S
   elif task == "s2s":
      a__						:    Any   		=	hf_model.speechta.encoder.prenet.feature_encoder
      a__						:    int   		=	MAPPING_S2S
      a__						:    Optional[int]   		=	IGNORE_KEYS_S2S
   else:
      raise ValueError(F"""Unsupported task: {task}""" )

   for name, value in fairseq_dict.items():
      if should_ignore(lowerCamelCase				,			lowerCamelCase ):
         logger.info(F"""{name} was ignored""" )
         continue

      a__						:    Dict   		=	False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			hf_model.config.feat_extract_norm == "group"				,			)
         a__						:    Union[str, Any]   		=	True
      else:
         for key, mapped_key in MAPPING.items():
            # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

            if "*" in key:
               a__		,  a__						:    Dict   		=	key.split(".*." )
               if prefix in name and suffix in name:
                  a__						:    Tuple   		=	suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
            if key in name:
               a__						:    List[str]   		=	True
               if "*" in mapped_key:
                  a__						:    List[Any]   		=	name.split(lowerCamelCase )[0].split("." )[-2]
                  a__						:    str   		=	mapped_key.replace("*"				,			lowerCamelCase )
               if "weight_g" in name:
                  a__						:    List[Any]   		=	"weight_g"
               elif "weight_v" in name:
                  a__						:    Optional[int]   		=	"weight_v"
               elif "bias" in name:
                  a__						:    Optional[Any]   		=	"bias"
               elif "weight" in name:
                  a__						:    Any   		=	"weight"
               elif "running_mean" in name:
                  a__						:    Union[str, Any]   		=	"running_mean"
               elif "running_var" in name:
                  a__						:    List[str]   		=	"running_var"
               elif "num_batches_tracked" in name:
                  a__						:    Tuple   		=	"num_batches_tracked"
               else:
                  a__						:    Optional[int]   		=	None
               set_recursively(lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )
            continue
      if not is_used:
         unused_weights.append(lowerCamelCase )

   logger.warning(F"""Unused weights: {unused_weights}""" )






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    int   		=	full_name.split("conv_layers." )[-1]
   a__						:    str   		=	name.split("." )
   a__						:    Union[str, Any]   		=	int(items[0] )
   a__						:    Union[str, Any]   		=	int(items[1] )

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
         a__						:    str   		=	value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
         a__						:    Union[str, Any]   		=	value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
         a__						:    Any   		=	value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                F"""{full_name} has size {value.shape}, but"""
                F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
         a__						:    Dict   		=	value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
   else:
      unused_weights.append(lowerCamelCase )






@torch.no_grad()
def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase=None				,			lowerCamelCase=None				,			lowerCamelCase=None				,			):
   if config_path is not None:
      a__						:    str   		=	SpeechTaConfig.from_pretrained(lowerCamelCase )
   else:
      a__						:    Union[str, Any]   		=	SpeechTaConfig()

   if task == "s2t":
      a__						:    Dict   		=	config.max_text_positions
      a__						:    Optional[int]   		=	SpeechTaForSpeechToText(lowerCamelCase )
   elif task == "t2s":
      a__						:    Union[str, Any]   		=	1876
      a__						:    Dict   		=	600
      a__						:    Tuple   		=	config.max_speech_positions
      a__						:    List[str]   		=	SpeechTaForTextToSpeech(lowerCamelCase )
   elif task == "s2s":
      a__						:    Optional[int]   		=	1876
      a__						:    Optional[int]   		=	config.max_speech_positions
      a__						:    Tuple   		=	SpeechTaForSpeechToSpeech(lowerCamelCase )
   else:
      raise ValueError(F"""Unknown task name: {task}""" )

   if vocab_path:
      a__						:    int   		=	SpeechTaTokenizer(lowerCamelCase				,			model_max_length=config.max_text_positions )

      # Mask token behaves like a normal word, i.e. include the space before it
      a__						:    List[str]   		=	AddedToken("<mask>"				,			lstrip=lowerCamelCase				,			rstrip=lowerCamelCase )
      a__						:    Tuple   		=	mask_token
      tokenizer.add_special_tokens({"mask_token": mask_token} )
      tokenizer.add_tokens(["<ctc_blank>"] )

   a__						:    Union[str, Any]   		=	SpeechTaFeatureExtractor()
   a__						:    Tuple   		=	SpeechTaProcessor(tokenizer=lowerCamelCase				,			feature_extractor=lowerCamelCase )
   processor.save_pretrained(lowerCamelCase )

   a__						:    Union[str, Any]   		=	torch.load(lowerCamelCase )
   recursively_load_weights(fairseq_checkpoint["model"]				,			lowerCamelCase				,			lowerCamelCase )

   model.save_pretrained(lowerCamelCase )

   if repo_id:
      print("Pushing to the hub..." )
      processor.push_to_hub(lowerCamelCase )
      model.push_to_hub(lowerCamelCase )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__							:      Dict         =    argparse.ArgumentParser()
  parser.add_argument(
      """--task""",
      default="""s2t""",
      type=str,
      help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
  )
  parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
  parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
  parser.add_argument(
      """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
  )

  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    parser.parse_args()
  convert_speechta_checkpoint(
      args.task,
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.vocab_path,
      args.push_to_hub,
  )








import os
import string
import sys


SCREAMING_SNAKE_CASE__							:      int         =    1 << 8

SCREAMING_SNAKE_CASE__							:      List[str]         =    {
    """tab""": ord("""\t"""),
    """newline""": ord("""\r"""),
    """esc""": 2_7,
    """up""": 6_5 + ARROW_KEY_FLAG,
    """down""": 6_6 + ARROW_KEY_FLAG,
    """right""": 6_7 + ARROW_KEY_FLAG,
    """left""": 6_8 + ARROW_KEY_FLAG,
    """mod_int""": 9_1,
    """undefined""": sys.maxsize,
    """interrupt""": 3,
    """insert""": 5_0,
    """delete""": 5_1,
    """pg_up""": 5_3,
    """pg_down""": 5_4,
}

SCREAMING_SNAKE_CASE__							:      Any         =    KEYMAP["""up"""]
SCREAMING_SNAKE_CASE__							:      Tuple         =    KEYMAP["""left"""]

if sys.platform == "win32":
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    []
  SCREAMING_SNAKE_CASE__							:      Dict         =    {
      B"""\xe0H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
      B"""\x00H""": KEYMAP["""up"""] - ARROW_KEY_FLAG,
      B"""\xe0P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
      B"""\x00P""": KEYMAP["""down"""] - ARROW_KEY_FLAG,
      B"""\xe0M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
      B"""\x00M""": KEYMAP["""right"""] - ARROW_KEY_FLAG,
      B"""\xe0K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
      B"""\x00K""": KEYMAP["""left"""] - ARROW_KEY_FLAG,
  }

for i in range(1_0):
  SCREAMING_SNAKE_CASE__							:      Dict         =    ord(str(i))






def    _A	(   ):
   if os.name == "nt":
      import msvcrt

      a__						:    int   		=	"mbcs"
      # Flush the keyboard buffer
      while msvcrt.kbhit():
         msvcrt.getch()
      if len(lowerCamelCase ) == 0:
         # Read the keystroke
         a__						:    int   		=	msvcrt.getch()

         # If it is a prefix char, get second part
         if ch in (b"\x00", b"\xe0"):
            a__						:    Union[str, Any]   		=	ch + msvcrt.getch()
            # Translate actual Win chars to bullet char types
            try:
               a__						:    Optional[int]   		=	chr(WIN_KEYMAP[cha] )
               WIN_CH_BUFFER.append(chr(KEYMAP["mod_int"] ) )
               WIN_CH_BUFFER.append(lowerCamelCase )
               if ord(lowerCamelCase ) in (
                   KEYMAP["insert"] - 1 << 9,
                   KEYMAP["delete"] - 1 << 9,
                   KEYMAP["pg_up"] - 1 << 9,
                   KEYMAP["pg_down"] - 1 << 9,
               ):
                  WIN_CH_BUFFER.append(chr(126 ) )
               a__						:    int   		=	chr(KEYMAP["esc"] )
            except KeyError:
               a__						:    Dict   		=	cha[1]
         else:
            a__						:    Union[str, Any]   		=	ch.decode(lowerCamelCase )
      else:
         a__						:    Optional[int]   		=	WIN_CH_BUFFER.pop(0 )
   elif os.name == "posix":
      import termios
      import tty

      a__						:    Tuple   		=	sys.stdin.fileno()
      a__						:    int   		=	termios.tcgetattr(lowerCamelCase )
      try:
         tty.setraw(lowerCamelCase )
         a__						:    Tuple   		=	sys.stdin.read(1 )
      finally:
         termios.tcsetattr(lowerCamelCase				,			termios.TCSADRAIN				,			lowerCamelCase )
   return ch






def    _A	(   ):
   a__						:    str   		=	get_raw_chars()
   if ord(lowerCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
      return char

   elif ord(lowerCamelCase ) == KEYMAP["esc"]:
      a__						:    Tuple   		=	get_raw_chars()
      if ord(lowerCamelCase ) == KEYMAP["mod_int"]:
         a__						:    List[Any]   		=	get_raw_chars()
         if ord(lowerCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(lowerCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
            return chr(ord(lowerCamelCase ) + ARROW_KEY_FLAG )
         else:
            return KEYMAP["undefined"]
      else:
         return get_raw_chars()

   else:
      if char in string.printable:
         return char
      else:
         return KEYMAP["undefined"]

