

'''simple docstring'''




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()
_a				: str 							=						logging.get_logger(__name__)

_a				: Dict 							=						{
    '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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]       ,   SCREAMING_SNAKE_CASE      :   Tuple       ,   SCREAMING_SNAKE_CASE      :   Any						):
  for attribute in key.split('.'						):
    UpperCAmelCase													=	getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

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

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


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[Any]       ,   SCREAMING_SNAKE_CASE      :   Union[str, Any]       ,   SCREAMING_SNAKE_CASE      :   List[str]						):
  UpperCAmelCase													=	[]
  UpperCAmelCase													=	fairseq_model.state_dict()

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

  for name, value in fairseq_dict.items():
    UpperCAmelCase													=	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'       ,   )
      UpperCAmelCase													=	True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCAmelCase													=	'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]:
          UpperCAmelCase													=	True
          if "*" in mapped_key:
            UpperCAmelCase													=	name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
            if not layer_index.isnumeric():
              continue
            UpperCAmelCase													=	mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
          if "weight_g" in name:
            UpperCAmelCase													=	'weight_g'
          elif "weight_v" in name:
            UpperCAmelCase													=	'weight_v'
          elif "weight" in name:
            UpperCAmelCase													=	'weight'
          elif "bias" in name:
            UpperCAmelCase													=	'bias'
          else:
            UpperCAmelCase													=	None
          set_recursively(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       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   List[str]       ,   SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   Any       ,   SCREAMING_SNAKE_CASE      :   Dict       ,   SCREAMING_SNAKE_CASE      :   Optional[int]						):
  UpperCAmelCase													=	full_name.split('conv_layers.'						)[-1]
  UpperCAmelCase													=	name.split('.'						)
  UpperCAmelCase													=	int(items[0]						)
  UpperCAmelCase													=	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.'''
      )
      UpperCAmelCase													=	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.'''
      )
      UpperCAmelCase													=	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."
      )
      UpperCAmelCase													=	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.'''
      )
      UpperCAmelCase													=	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						)


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Optional[int]       ,   SCREAMING_SNAKE_CASE      :   Dict						):
  UpperCAmelCase													=	SEWDConfig()
  if is_finetuned:
    UpperCAmelCase													=	model.wav_encoder.wav_model.cfg
  else:
    UpperCAmelCase													=	model.cfg

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

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

  UpperCAmelCase													=	'Wav2Vec2FeatureExtractor'
  UpperCAmelCase													=	'Wav2Vec2CTCTokenizer'

  return config


@torch.no_grad()
def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]       ,   SCREAMING_SNAKE_CASE      :   str=None       ,   SCREAMING_SNAKE_CASE      :   Tuple=None       ,   SCREAMING_SNAKE_CASE      :   Optional[int]=True						):

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

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

  UpperCAmelCase													=	True if config.feat_extract_norm == 'layer' else False
  UpperCAmelCase													=	WavaVecaFeatureExtractor(
      feature_size=1       ,   sampling_rate=1_6000       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )

  if is_finetuned:
    if dict_path:
      UpperCAmelCase													=	Dictionary.load(SCREAMING_SNAKE_CASE						)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      UpperCAmelCase													=	target_dict.pad_index
      UpperCAmelCase													=	target_dict.bos_index
      UpperCAmelCase													=	target_dict.pad_index
      UpperCAmelCase													=	target_dict.bos_index
      UpperCAmelCase													=	target_dict.eos_index
      UpperCAmelCase													=	len(target_dict.symbols						)
      UpperCAmelCase													=	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						)
      with open(SCREAMING_SNAKE_CASE       ,   'w'       ,   encoding='utf-8'						) as vocab_handle:
        json.dump(target_dict.indices       ,   SCREAMING_SNAKE_CASE						)
      UpperCAmelCase													=	WavaVecaCTCTokenizer(
          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       ,   )
      UpperCAmelCase													=	WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE       ,   tokenizer=SCREAMING_SNAKE_CASE						)
      processor.save_pretrained(SCREAMING_SNAKE_CASE						)

    UpperCAmelCase													=	SEWDForCTC(SCREAMING_SNAKE_CASE						)
  else:
    UpperCAmelCase													=	SEWDModel(SCREAMING_SNAKE_CASE						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)

  recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a				: List[Any] 							=						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'
  )
  _a				: Any 							=						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'''




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   list[list[int]]						):
  if len(SCREAMING_SNAKE_CASE						) == 0 or not all(len(SCREAMING_SNAKE_CASE						) == 2 for pair in coordinates						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  if len({tuple(SCREAMING_SNAKE_CASE						) for pair in coordinates}						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	{x for x, _ in coordinates}
  if len(SCREAMING_SNAKE_CASE						) == 1:
    return f'''x={coordinates[0][0]}'''

  if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  UpperCAmelCase													=	0
  UpperCAmelCase													=	[]
  # put the x and x to the power values in a matrix
  while count_of_line < x:
    UpperCAmelCase													=	0
    UpperCAmelCase													=	coordinates[count_of_line][0]
    UpperCAmelCase													=	[]
    while count_in_line < x:
      count_line.append(a ** (x - (count_in_line + 1))						)
      count_in_line += 1
    matrix.append(SCREAMING_SNAKE_CASE						)
    count_of_line += 1

  UpperCAmelCase													=	0
  # put the y values into a vector
  UpperCAmelCase													=	[]
  while count_of_line < x:
    vector.append(coordinates[count_of_line][1]						)
    count_of_line += 1

  UpperCAmelCase													=	0

  while count < x:
    UpperCAmelCase													=	0
    while zahlen < x:
      if count == zahlen:
        zahlen += 1
      if zahlen == x:
        break
      UpperCAmelCase													=	matrix[zahlen][count] / matrix[count][count]
      for counting_columns, item in enumerate(matrix[count]						):
        # manipulating all the values in the matrix
        matrix[zahlen][counting_columns] -= item * bruch
      # manipulating the values in the vector
      vector[zahlen] -= vector[count] * bruch
      zahlen += 1
    count += 1

  UpperCAmelCase													=	0
  # make solutions
  UpperCAmelCase													=	[]
  while count < x:
    solution.append(str(vector[count] / matrix[count][count]						)						)
    count += 1

  UpperCAmelCase													=	0
  UpperCAmelCase													=	'f(x)='

  while count < x:
    UpperCAmelCase													=	solution[count].split('E'						)
    if len(SCREAMING_SNAKE_CASE						) > 1:
      UpperCAmelCase													=	f'''{remove_e[0]}*10^{remove_e[1]}'''
    solved += f'''x^{x - (count + 1)}*{solution[count]}'''
    if count + 1 != x:
      solved += "+"
    count += 1

  return solved


if __name__ == "__main__":
  print(points_to_polynomial([]))
  print(points_to_polynomial([[]]))
  print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
  print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
  print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
  print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
  print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
  print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
  print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))


