




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

UpperCamelCase__       				=	{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


  for attribute in key.split('''.'''			):
    a_		    =  getattr(lowercase__      ,      lowercase__			)

  if weight_type is not None:
    a_		    =  getattr(lowercase__      ,      lowercase__			).shape
  else:
    a_		    =  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":
    a_		    =  value
  elif weight_type == "weight_g":
    a_		    =  value
  elif weight_type == "weight_v":
    a_		    =  value
  elif weight_type == "bias":
    a_		    =  value
  else:
    a_		    =  value

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






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  []
  a_		    =  fairseq_model.state_dict()

  a_		    =  hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    a_		    =  False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase__      ,      lowercase__      ,      lowercase__      ,      lowercase__      ,      hf_model.config.feat_extract_norm == '''group'''      ,      )
      a_		    =  True
    else:
      for key, mapped_key in MAPPING.items():
        a_		    =  '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

        if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
          a_		    =  True
          if "*" in mapped_key:
            a_		    =  name.split(lowercase__			)[0].split('''.'''			)[-2]
            if not layer_index.isnumeric():
              continue
            a_		    =  mapped_key.replace('''*'''      ,      lowercase__			)
          if "weight_g" in name:
            a_		    =  '''weight_g'''
          elif "weight_v" in name:
            a_		    =  '''weight_v'''
          elif "weight" in name:
            a_		    =  '''weight'''
          elif "bias" in name:
            a_		    =  '''bias'''
          else:
            a_		    =  None
          set_recursively(lowercase__      ,      lowercase__      ,      lowercase__      ,      lowercase__      ,      lowercase__			)
        continue
    if not is_used:
      unused_weights.append(lowercase__			)

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






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  full_name.split('''conv_layers.'''			)[-1]
  a_		    =  name.split('''.'''			)
  a_		    =  int(items[0]			)
  a_		    =  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."
      )
      a_		    =  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."
      )
      a_		    =  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."
      )
      a_		    =  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."
      )
      a_		    =  value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
  else:
    unused_weights.append(lowercase__			)






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  SEWDConfig()
  if is_finetuned:
    a_		    =  model.wav_encoder.wav_model.cfg
  else:
    a_		    =  model.cfg

  a_		    =  fs_config.conv_bias
  a_		    =  eval(fs_config.conv_feature_layers			)
  a_		    =  [x[0] for x in conv_layers]
  a_		    =  [x[1] for x in conv_layers]
  a_		    =  [x[2] for x in conv_layers]
  a_		    =  '''gelu'''
  a_		    =  '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
  a_		    =  0.0
  a_		    =  fs_config.activation_fn.name
  a_		    =  fs_config.encoder_embed_dim
  a_		    =  0.02
  a_		    =  fs_config.encoder_ffn_embed_dim
  a_		    =  1e-5
  a_		    =  fs_config.encoder_layerdrop
  a_		    =  fs_config.encoder_attention_heads
  a_		    =  fs_config.conv_pos_groups
  a_		    =  fs_config.conv_pos
  a_		    =  len(lowercase__			)
  a_		    =  fs_config.encoder_layers
  a_		    =  fs_config.squeeze_factor
  # DeBERTa-specific parameters:
  a_		    =  fs_config.max_position_embeddings
  a_		    =  fs_config.position_buckets
  a_		    =  fs_config.share_att_key
  a_		    =  fs_config.relative_attention
  a_		    =  fs_config.position_biased_input
  a_		    =  tuple(fs_config.pos_att_type.split('''|'''			)			)
  a_		    =  fs_config.norm_rel_ebd

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    a_		    =  model.cfg
    a_		    =  fs_config.final_dropout
    a_		    =  fs_config.layerdrop
  a_		    =  fs_config.activation_dropout
  a_		    =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  a_		    =  fs_config.attention_dropout
  a_		    =  fs_config.dropout_input
  a_		    =  fs_config.dropout
  a_		    =  fs_config.mask_channel_length
  a_		    =  fs_config.mask_channel_prob
  a_		    =  fs_config.mask_length
  a_		    =  fs_config.mask_prob

  a_		    =  '''Wav2Vec2FeatureExtractor'''
  a_		    =  '''Wav2Vec2CTCTokenizer'''

  return config






@torch.no_grad()
def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A=None      ,      _A=None      ,      _A=True			):






  """simple docstring"""


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

  if config_path is not None:
    a_		    =  SEWDConfig.from_pretrained(lowercase__			)
  else:
    a_		    =  convert_config(model[0]      ,      lowercase__			)
  a_		    =  model[0].eval()

  a_		    =  True if config.feat_extract_norm == '''layer''' else False
  a_		    =  WavaVecaFeatureExtractor(
      feature_size=1      ,      sampling_rate=16_000      ,      padding_value=0      ,      do_normalize=lowercase__      ,      return_attention_mask=lowercase__      ,      )

  if is_finetuned:
    if dict_path:
      a_		    =  Dictionary.load(lowercase__			)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      a_		    =  target_dict.pad_index
      a_		    =  target_dict.bos_index
      a_		    =  target_dict.pad_index
      a_		    =  target_dict.bos_index
      a_		    =  target_dict.eos_index
      a_		    =  len(target_dict.symbols			)
      a_		    =  os.path.join(lowercase__      ,      '''vocab.json'''			)
      if not os.path.isdir(lowercase__			):
        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase__			)			)
        return
      os.makedirs(lowercase__      ,      exist_ok=lowercase__			)
      with open(lowercase__      ,      '''w'''      ,      encoding='''utf-8'''			) as vocab_handle:
        json.dump(target_dict.indices      ,      lowercase__			)
      a_		    =  WavaVecaCTCTokenizer(
          lowercase__      ,      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=lowercase__      ,      )
      a_		    =  WavaVecaProcessor(feature_extractor=lowercase__      ,      tokenizer=lowercase__			)
      processor.save_pretrained(lowercase__			)

    a_		    =  SEWDForCTC(lowercase__			)
  else:
    a_		    =  SEWDModel(lowercase__			)
    feature_extractor.save_pretrained(lowercase__			)

  recursively_load_weights(lowercase__      ,      lowercase__      ,      lowercase__			)

  hf_model.save_pretrained(lowercase__			)


if __name__ == "__main__":
  UpperCamelCase__       				=	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(
      '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
  )
  UpperCamelCase__       				=	parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )



"""simple docstring"""




import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class      __UpperCamelCase					(  unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       EsmTokenizer


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()
							A     =   tempfile.mkdtemp()
							# fmt: off
							A     =   ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
							# fmt: on
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"])
							with open(self.vocab_file	,	"w"	,	encoding="utf-8") as vocab_writer:
												vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))


		def    SCREAMING_SNAKE_CASE__  (self					:						Any	,	**__SCREAMING_SNAKE_CASE					:						str):
							return [self.get_tokenizer(**__SCREAMING_SNAKE_CASE)]


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]	,	**__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,	**__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   tokenizer.tokenize("LAGVS")
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	["L", "A", "G", "V", "S"])
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)	,	[4, 5, 6, 7, 8])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   "LAGVS"
							self.assertListEqual(tokenizer.encode(__SCREAMING_SNAKE_CASE)	,	[0, 4, 5, 6, 7, 8, 2])


		def    SCREAMING_SNAKE_CASE__  (self					:						List[str]):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]])


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   self.tokenizer_class(self.vocab_file)

							A     =   ["LAGVS", "WCB"]
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	padding=__SCREAMING_SNAKE_CASE)["input_ids"]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]])


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE)
							for tokenizer in tokenizers:
												with self.subTest(F"""{tokenizer.__class__.__name__}"""):
																	A     =   "<unk>"
																	A     =   "<mask>"

																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)
																	A     =   tokenizer.tokenize(__SCREAMING_SNAKE_CASE)

																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1)
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1)
																	self.assertEqual(token_a[0]	,	__SCREAMING_SNAKE_CASE)
																	self.assertEqual(token_a[0]	,	__SCREAMING_SNAKE_CASE)





