





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
SCREAMING_SNAKE_CASE_						   =       [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   str:


      '''simple docstring'''


      for attribute in key.split("""."""	):
            if is_finetuned:
                  if attribute in ["quantizer", "project_q", "project_hid"]:
                        # those layers are only relevant for pretraining and should be dropped
                        return

                  if attribute == "ctc_proj":
                        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                        SCREAMING_SNAKE_CASE		=						"""lm_head"""

            SCREAMING_SNAKE_CASE		=						getattr(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

      if weight_type is not None:
            SCREAMING_SNAKE_CASE		=						getattr(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	).shape
      else:
            SCREAMING_SNAKE_CASE		=						hf_pointer.shape

      assert hf_shape == value.shape, (
          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":
            SCREAMING_SNAKE_CASE		=						value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE		=						value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE		=						value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE		=						value
      else:
            SCREAMING_SNAKE_CASE		=						value

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



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[]
      SCREAMING_SNAKE_CASE		=						fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE		=						hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE		=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						hf_model.config.feat_extract_norm == """group""" ,						)
                  SCREAMING_SNAKE_CASE		=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE		=						"""unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                        if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
                              SCREAMING_SNAKE_CASE		=						True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE		=						name.split(_SCREAMING_SNAKE_CASE	)[0].split("""."""	)[-2]
                                    SCREAMING_SNAKE_CASE		=						mapped_key.replace("""*""" ,						_SCREAMING_SNAKE_CASE	)
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE		=						"""weight_g"""
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE		=						"""weight_v"""
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE		=						"""bias"""
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    SCREAMING_SNAKE_CASE		=						"""weight"""
                              else:
                                    SCREAMING_SNAKE_CASE		=						None
                              set_recursively(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
                        continue
            if not is_used:
                  unused_weights.append(_SCREAMING_SNAKE_CASE	)

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



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						full_name.split("""conv_layers."""	)[-1]
      SCREAMING_SNAKE_CASE		=						name.split("""."""	)
      SCREAMING_SNAKE_CASE		=						int(items[0]	)
      SCREAMING_SNAKE_CASE		=						int(items[1]	)

      if type_id == 0:
            if "bias" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                  )
                  SCREAMING_SNAKE_CASE		=						value
                  logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                  )
                  SCREAMING_SNAKE_CASE		=						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:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                      F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                      " found."
                  )
                  SCREAMING_SNAKE_CASE		=						value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
            elif "weight" in name:
                  assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                      F"""{full_name} has size {value.shape}, but"""
                      F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                  )
                  SCREAMING_SNAKE_CASE		=						value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
      else:
            unused_weights.append(_SCREAMING_SNAKE_CASE	)



@torch.no_grad()
def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=None ,						_SCREAMING_SNAKE_CASE=True	) ->   List[Any]:


      '''simple docstring'''


      if config_path is not None:
            SCREAMING_SNAKE_CASE		=						UniSpeechConfig.from_pretrained(_SCREAMING_SNAKE_CASE	)
      else:
            SCREAMING_SNAKE_CASE		=						UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  SCREAMING_SNAKE_CASE		=						Dictionary.load_from_json(_SCREAMING_SNAKE_CASE	)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  SCREAMING_SNAKE_CASE		=						target_dict.pad_index
                  SCREAMING_SNAKE_CASE		=						target_dict.bos_index
                  SCREAMING_SNAKE_CASE		=						target_dict.eos_index
                  SCREAMING_SNAKE_CASE		=						len(target_dict.symbols	)
                  SCREAMING_SNAKE_CASE		=						os.path.join(_SCREAMING_SNAKE_CASE ,						"""vocab.json"""	)
                  if not os.path.isdir(_SCREAMING_SNAKE_CASE	):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_SCREAMING_SNAKE_CASE	)	)
                        return
                  os.makedirs(_SCREAMING_SNAKE_CASE ,						exist_ok=_SCREAMING_SNAKE_CASE	)
                  SCREAMING_SNAKE_CASE		=						target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  SCREAMING_SNAKE_CASE		=						42
                  SCREAMING_SNAKE_CASE		=						43
                  with open(_SCREAMING_SNAKE_CASE ,						"""w""" ,						encoding="""utf-8"""	) as vocab_handle:
                        json.dump(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)
                  SCREAMING_SNAKE_CASE		=						WavaVecaPhonemeCTCTokenizer(
                      _SCREAMING_SNAKE_CASE ,						unk_token=target_dict.unk_word ,						pad_token=target_dict.pad_word ,						bos_token=target_dict.bos_word ,						eos_token=target_dict.eos_word ,						word_delimiter_token="""|""" ,						do_lower_case=_SCREAMING_SNAKE_CASE ,						)
                  SCREAMING_SNAKE_CASE		=						True if config.feat_extract_norm == """layer""" else False
                  SCREAMING_SNAKE_CASE		=						WavaVecaFeatureExtractor(
                      feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=_SCREAMING_SNAKE_CASE ,						return_attention_mask=_SCREAMING_SNAKE_CASE ,						)
                  SCREAMING_SNAKE_CASE		=						WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE ,						tokenizer=_SCREAMING_SNAKE_CASE	)
                  processor.save_pretrained(_SCREAMING_SNAKE_CASE	)

            SCREAMING_SNAKE_CASE		=						UniSpeechForCTC(_SCREAMING_SNAKE_CASE	)
      else:
            SCREAMING_SNAKE_CASE		=						UniSpeechForPreTraining(_SCREAMING_SNAKE_CASE	)

      if is_finetuned:
            SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path] ,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	), """w2v_path""": checkpoint_path}	)
      else:
            SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

      SCREAMING_SNAKE_CASE		=						model[0].eval()

      recursively_load_weights(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	)

      hf_unispeech.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						   =       argparse.ArgumentParser()
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
      parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      parser.add_argument(
          """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      SCREAMING_SNAKE_CASE_						   =       parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )









import random

class 		UpperCamelCase__ :



     '''simple docstring'''

     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    str       )     ->		tuple[list[int], list[int]]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[ord(lowerCamelCase__       ) for i in text]
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]
           for i in plain:
                 SCREAMING_SNAKE_CASE		=						random.randint(1						,300       )
                 SCREAMING_SNAKE_CASE		=						(i + k) * k
                 cipher.append(lowerCamelCase__       )
                 key.append(lowerCamelCase__       )
           return cipher, key






     @staticmethod
     def 				SCREAMING_SNAKE_CASE__  (							lowerCamelCase__						:    list[int]						,lowerCamelCase__						:    list[int]       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           for i in range(len(lowerCamelCase__       )       ):
                 SCREAMING_SNAKE_CASE		=						int((cipher[i] - (key[i]) ** 2) / key[i]       )
                 plain.append(chr(lowerCamelCase__       )       )
           return "".join(lowerCamelCase__       )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_   ,						SCREAMING_SNAKE_CASE_						   =       Onepad().encrypt("""Hello""")
      print(c, k)
      print(Onepad().decrypt(c, k))


