

'''simple docstring'''







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin


class     A__					(					__SCREAMING_SNAKE_CASE  ,  unittest.TestCase	):
    lowercase		    =						BioGptTokenizer
    lowercase		    =						False



    def     snake_case_	(    self				)  ->					Optional[int]:





           '''simple docstring'''
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           A_		    =					[
               "l",
               "o",
               "w",
               "e",
               "r",
               "s",
               "t",
               "i",
               "d",
               "n",
               "w</w>",
               "r</w>",
               "t</w>",
               "lo",
               "low",
               "er</w>",
               "low</w>",
               "lowest</w>",
               "newer</w>",
               "wider</w>",
               "<unk>",
           ]
           A_		    =					dict(zip(UpperCAmelCase_						, range(len(UpperCAmelCase_				)				)				)				)
           A_		    =					["l o 123", "lo w 1456", "e r</w> 1789", ""]

           A_		    =					os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES["""vocab_file"""]				)
           A_		    =					os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES["""merges_file"""]				)
           with open(self.vocab_file						, """w"""				) as fp:
                  fp.write(json.dumps(UpperCAmelCase_				)				)
           with open(self.merges_file						, """w"""				) as fp:
                  fp.write("""\n""".join(UpperCAmelCase_				)				)



    def     snake_case_	(    self						, UpperCamelCase__				)  ->					Optional[Any]:





           '''simple docstring'''
           A_		    =					"lower newer"
           A_		    =					"lower newer"
           return input_text, output_text



    def     snake_case_	(    self				)  ->					Optional[int]:





           '''simple docstring'''
           A_		    =					BioGptTokenizer(self.vocab_file						, self.merges_file				)

           A_		    =					"lower"
           A_		    =					["low", "er</w>"]
           A_		    =					tokenizer.tokenize(UpperCAmelCase_				)
           self.assertListEqual(UpperCAmelCase_						, UpperCAmelCase_				)

           A_		    =					tokens + ["<unk>"]
           A_		    =					[14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_				)						, UpperCAmelCase_				)







    @slow
    def     snake_case_	(    self				)  ->					Dict:





           '''simple docstring'''
           A_		    =					BioGptTokenizer.from_pretrained("""microsoft/biogpt"""				)

           A_		    =					tokenizer.encode("""sequence builders"""						, add_special_tokens=UpperCAmelCase_				)
           A_		    =					tokenizer.encode("""multi-sequence build"""						, add_special_tokens=UpperCAmelCase_				)

           A_		    =					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				)
           A_		    =					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_						, UpperCAmelCase_				)

           self.assertTrue(encoded_sentence == [2] + text				)
           self.assertTrue(encoded_pair == [2] + text + [2] + text_a				)







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()
__A									=				logging.get_logger(__name__)

__A									=				{
    "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",
}
__A									=				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a	,    __a		)     ->		Optional[Any]:







  """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
        lowerCamelCase__:      Optional[int]			  ="lm_head"

    lowerCamelCase__:      Dict			  =getattr(__a	,    __a		)

  if weight_type is not None:
    lowerCamelCase__:      str			  =getattr(__a	,    __a		).shape
  else:
    lowerCamelCase__:      int			  =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":
    lowerCamelCase__:      Dict			  =value
  elif weight_type == "weight_g":
    lowerCamelCase__:      Optional[Any]			  =value
  elif weight_type == "weight_v":
    lowerCamelCase__:      int			  =value
  elif weight_type == "bias":
    lowerCamelCase__:      List[str]			  =value
  else:
    lowerCamelCase__:      Union[str, Any]			  =value

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





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      List[Any]			  =[]
  lowerCamelCase__:      List[str]			  =fairseq_model.state_dict()

  lowerCamelCase__:      Optional[int]			  =hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    lowerCamelCase__:      int			  =False
    if "conv_layers" in name:
      load_conv_layer(
          __a	,    __a	,    __a	,    __a	,    hf_model.config.feat_extract_norm == "group"	,    )
      lowerCamelCase__:      str			  =True
    else:
      for key, mapped_key in MAPPING.items():
        lowerCamelCase__:      List[str]			  ="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]:
          lowerCamelCase__:      Optional[Any]			  =True
          if "*" in mapped_key:
            lowerCamelCase__:      Optional[Any]			  =name.split(__a		)[0].split("."		)[-2]
            lowerCamelCase__:      List[str]			  =mapped_key.replace("*"	,    __a		)
          if "weight_g" in name:
            lowerCamelCase__:      List[str]			  ="weight_g"
          elif "weight_v" in name:
            lowerCamelCase__:      Union[str, Any]			  ="weight_v"
          elif "bias" in name:
            lowerCamelCase__:      Dict			  ="bias"
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            lowerCamelCase__:      Tuple			  ="weight"
          else:
            lowerCamelCase__:      List[Any]			  =None
          set_recursively(__a	,    __a	,    __a	,    __a	,    __a	,    __a		)
        continue
    if not is_used:
      unused_weights.append(__a		)

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





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a	,    __a		)     ->		Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =full_name.split("conv_layers."		)[-1]
  lowerCamelCase__:      List[str]			  =name.split("."		)
  lowerCamelCase__:      str			  =int(items[0]		)
  lowerCamelCase__:      Union[str, Any]			  =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."""
      )
      lowerCamelCase__:      List[str]			  =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."""
      )
      lowerCamelCase__:      Dict			  =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."
      )
      lowerCamelCase__:      List[Any]			  =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."""
      )
      lowerCamelCase__:      List[str]			  =value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
  else:
    unused_weights.append(__a		)





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a=None	,    __a=None	,    __a=True		)     ->		int:







  """simple docstring"""





  if config_path is not None:
    lowerCamelCase__:      str			  =UniSpeechConfig.from_pretrained(__a		)
  else:
    lowerCamelCase__:      List[Any]			  =UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      lowerCamelCase__:      str			  =Dictionary.load_from_json(__a		)

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

      # fairseq has the <pad> and <s> switched
      lowerCamelCase__:      Optional[Any]			  =42
      lowerCamelCase__:      List[Any]			  =43
      with open(__a	,    "w"	,    encoding="utf-8"		) as vocab_handle:
        json.dump(__a	,    __a		)
      lowerCamelCase__:      List[str]			  =WavaVecaPhonemeCTCTokenizer(
          __a	,    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=__a	,    )
      lowerCamelCase__:      Dict			  =True if config.feat_extract_norm == "layer" else False
      lowerCamelCase__:      Tuple			  =WavaVecaFeatureExtractor(
          feature_size=1	,    sampling_rate=16000	,    padding_value=0	,    do_normalize=__a	,    return_attention_mask=__a	,    )
      lowerCamelCase__:      List[Any]			  =WavaVecaProcessor(feature_extractor=__a	,    tokenizer=__a		)
      processor.save_pretrained(__a		)

    lowerCamelCase__:      int			  =UniSpeechForCTC(__a		)
  else:
    lowerCamelCase__:      int			  =UniSpeechForPreTraining(__a		)

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

  lowerCamelCase__:      List[str]			  =model[0].eval()

  recursively_load_weights(__a	,    __a	,    __a		)

  hf_unispeech.save_pretrained(__a		)


if __name__ == "__main__":
     __A									=				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"
     )
     __A									=				parser.parse_args()
     convert_unispeech_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )

