
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowercase__          ={
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowercase__          =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ):
 __a			:  str		    =  {}
 with open(lowerCAmelCase__  ,       '''r'''  ) as file:
  for line_number, line in enumerate(lowerCAmelCase__  ):
   __a			:  Dict		    =  line.strip()
   if line:
    __a			:  List[str]		    =  line.split()
    __a			:  Optional[int]		    =  line_number
    __a			:  Optional[int]		    =  words[0]
    __a			:  List[Any]		    =  value
 return result




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Dict  ,       lowerCAmelCase__							:    List[str]  ):
 for attribute in key.split('''.'''  ):
  __a			:  Union[str, Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )

 __a			:  str		    =  None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(lowerCAmelCase__  ):
   __a			:  int		    =  PARAM_MAPPING[full_name.split('''.'''  )[-1]]
   __a			:  Dict		    =  '''param'''

 if weight_type is not None and weight_type != "param":
  __a			:  Optional[Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  ).shape
 elif weight_type is not None and weight_type == "param":
  __a			:  List[str]		    =  hf_pointer
  for attribute in hf_param_name.split('''.'''  ):
   __a			:  List[Any]		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  int		    =  shape_pointer.shape

  # let's reduce dimension
  __a			:  Union[str, Any]		    =  value[0]
 else:
  __a			:  str		    =  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			:  Union[str, Any]		    =  value
 elif weight_type == "weight_g":
  __a			:  Tuple		    =  value
 elif weight_type == "weight_v":
  __a			:  str		    =  value
 elif weight_type == "bias":
  __a			:  int		    =  value
 elif weight_type == "param":
  for attribute in hf_param_name.split('''.'''  ):
   __a			:  int		    =  getattr(lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Tuple		    =  value
 else:
  __a			:  List[str]		    =  value

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Optional[int]  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Any  ,       lowerCAmelCase__							:    List[Any]  ):
 __a			:  List[Any]		    =  None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(lowerCAmelCase__  ):
   __a			:  str		    =  PARAM_MAPPING[full_name.split('''.'''  )[-1]]
   __a			:  Optional[Any]		    =  '''param'''

 if weight_type is not None and weight_type != "param":
  __a			:  Optional[int]		    =  '''.'''.join([key, weight_type]  )
 elif weight_type is not None and weight_type == "param":
  __a			:  int		    =  '''.'''.join([key, hf_param_name]  )
 else:
  __a			:  List[str]		    =  key

 __a			:  List[str]		    =  value if '''lm_head''' in full_key else value[0]


lowercase__          ={
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[int]=None  ,       lowerCAmelCase__							:    str=None  ):
 __a			:  int		    =  False
 for key, mapped_key in MAPPING.items():
  __a			:  Union[str, Any]		    =  '''wav2vec2.''' + 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]:
   __a			:  Optional[Any]		    =  True
   if "*" in mapped_key:
    __a			:  Tuple		    =  name.split(lowerCAmelCase__  )[0].split('''.'''  )[-2]
    __a			:  Dict		    =  mapped_key.replace('''*'''  ,       lowerCAmelCase__  )
   if "weight_g" in name:
    __a			:  Optional[int]		    =  '''weight_g'''
   elif "weight_v" in name:
    __a			:  Optional[int]		    =  '''weight_v'''
   elif "bias" in name:
    __a			:  Dict		    =  '''bias'''
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    __a			:  List[str]		    =  '''weight'''
   else:
    __a			:  Union[str, Any]		    =  None
   if hf_dict is not None:
    rename_dict(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
   else:
    set_recursively(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
   return is_used
 return is_used




def        __UpperCamelCase					(    lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    Optional[int]  ):
 __a			:  List[str]		    =  []
 __a			:  Any		    =  fairseq_model.state_dict()

 __a			:  int		    =  hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  __a			:  Optional[int]		    =  False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       hf_model.config.feat_extract_norm == '''group'''  ,       )
   __a			:  Optional[Any]		    =  True
  else:
   __a			:  Optional[int]		    =  load_wavaveca_layer(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  if not is_used:
   unused_weights.append(lowerCAmelCase__  )

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Tuple  ,       lowerCAmelCase__							:    str  ):
 __a			:  Tuple		    =  full_name.split('''conv_layers.'''  )[-1]
 __a			:  Optional[int]		    =  name.split('''.'''  )
 __a			:  int		    =  int(items[0]  )
 __a			:  Dict		    =  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			:  Union[str, Any]		    =  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			:  Optional[int]		    =  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			:  int		    =  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			:  Optional[int]		    =  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        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[Any]  ,       lowerCAmelCase__							:    Tuple=None  ,       lowerCAmelCase__							:    Union[str, Any]=None  ,       lowerCAmelCase__							:    int=True  ,       lowerCAmelCase__							:    Tuple=False  ):
 if config_path is not None:
  __a			:  Dict		    =  WavaVecaConfig.from_pretrained(lowerCAmelCase__  )
 else:
  __a			:  Dict		    =  WavaVecaConfig()

 if is_seq_class:
  __a			:  str		    =  read_txt_into_dict(lowerCAmelCase__  )
  __a			:  str		    =  idalabel
  __a			:  str		    =  WavaVecaForSequenceClassification(lowerCAmelCase__  )
  __a			:  str		    =  WavaVecaFeatureExtractor(
      feature_size=1  ,       sampling_rate=1_6_0_0_0  ,       padding_value=0  ,       do_normalize=lowerCAmelCase__  ,       return_attention_mask=lowerCAmelCase__  ,       )
  feature_extractor.save_pretrained(lowerCAmelCase__  )

 elif is_finetuned:
  if dict_path:
   __a			:  Tuple		    =  Dictionary.load(lowerCAmelCase__  )

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

   # fairseq has the <pad> and <s> switched
   __a			:  str		    =  0
   __a			:  Any		    =  1
   with open(lowerCAmelCase__  ,       '''w'''  ,       encoding='''utf-8'''  ) as vocab_handle:
    json.dump(lowerCAmelCase__  ,       lowerCAmelCase__  )
   __a			:  Tuple		    =  WavaVecaCTCTokenizer(
       lowerCAmelCase__  ,       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=lowerCAmelCase__  ,       )
   __a			:  Any		    =  True if config.feat_extract_norm == '''layer''' else False
   __a			:  Union[str, Any]		    =  WavaVecaFeatureExtractor(
       feature_size=1  ,       sampling_rate=1_6_0_0_0  ,       padding_value=0  ,       do_normalize=lowerCAmelCase__  ,       return_attention_mask=lowerCAmelCase__  ,       )
   __a			:  Dict		    =  WavaVecaProcessor(feature_extractor=lowerCAmelCase__  ,       tokenizer=lowerCAmelCase__  )
   processor.save_pretrained(lowerCAmelCase__  )

  __a			:  Tuple		    =  WavaVecaForCTC(lowerCAmelCase__  )
 else:
  __a			:  List[str]		    =  WavaVecaForPreTraining(lowerCAmelCase__  )

 if is_finetuned or is_seq_class:
  __a    ,	__a    ,	__a			:  int		    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]  ,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
 else:
  __a			:  List[str]		    =  argparse.Namespace(task='''audio_pretraining'''  )
  __a			:  int		    =  fairseq.tasks.setup_task(lowerCAmelCase__  )

  __a    ,	__a    ,	__a			:  List[Any]		    =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,       task=lowerCAmelCase__  )

 __a			:  int		    =  model[0].eval()

 recursively_load_weights(lowerCAmelCase__  ,       lowerCAmelCase__  ,       not is_finetuned  )

 hf_wavavec.save_pretrained(lowerCAmelCase__  )


if __name__ == "__main__":
      lowercase__          =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'
      )
      parser.add_argument(
          '--is_seq_class',
          action='store_true',
          help='Whether the model to convert is a fine-tuned sequence classification model or not',
      )
      lowercase__          =parser.parse_args()

      lowercase__          =not args.not_finetuned and not args.is_seq_class
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.dict_path,
          is_finetuned,
          args.is_seq_class,
      )


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowercase__          =50000
lowercase__          =5000

lowercase__       ,      lowercase__          =os.path.split(__file__)
lowercase__          =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ):
 for i in range(lowerCAmelCase__  ):
  __a			:  str		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Tuple  ):
 for i in range(0  ,       len(lowerCAmelCase__  )  ,       lowerCAmelCase__  ):
  __a			:  Optional[int]		    =  dataset[i : i + batch_size]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    str  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(lowerCAmelCase__  ):
   __a			:  Dict		    =  dataset[i]




@get_duration
def        __UpperCamelCase					(    lowerCAmelCase__							:    datasets.Dataset  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    int  ):
 with dataset.formatted_as(type=lowerCAmelCase__  ):
  for i in range(0  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ):
   __a			:  int		    =  dataset[i : i + batch_size]




def        __UpperCamelCase					(    ):
 __a			:  Any		    =  {'''num examples''': SPEED_TEST_N_EXAMPLES}
 __a			:  List[Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
     (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]

 __a			:  Union[str, Any]		    =  [
     (read, {'''length''': SMALL_TEST}),
     (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0}),
     (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1_0_0_0}),
     (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0}),
     (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1_0_0_0}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print('''generating dataset'''  )
  __a			:  Optional[Any]		    =  datasets.Features(
      {'''list''': datasets.Sequence(datasets.Value('''float32'''  )  ), '''numbers''': datasets.Value('''float32'''  )}  )
  __a			:  Optional[int]		    =  generate_example_dataset(
      os.path.join(lowerCAmelCase__  ,       '''dataset.arrow'''  )  ,       lowerCAmelCase__  ,       num_examples=lowerCAmelCase__  ,       seq_shapes={'''list''': (1_0_0,)}  ,       )
  print('''first set of iterations'''  )
  for func, kwargs in functions:
   print(func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  str		    =  func(lowerCAmelCase__  ,       **lowerCAmelCase__  )

  print('''shuffling dataset'''  )
  __a			:  int		    =  dataset.shuffle()
  print('''Second set of iterations (after shuffling'''  )
  for func, kwargs in functions_shuffled:
   print('''shuffled '''  ,       func.__name__  ,       str(lowerCAmelCase__  )  )
   __a			:  List[Any]		    =  func(
       lowerCAmelCase__  ,       **lowerCAmelCase__  )

 with open(lowerCAmelCase__  ,       '''wb'''  ) as f:
  f.write(json.dumps(lowerCAmelCase__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
      benchmark_iterating()
