






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__magic_name__   :  Union[str, Any]        =							logging.get_logger(__name__)

__magic_name__   :  Optional[Any]        =							{
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__magic_name__   :  str        =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			str:
      """simple docstring"""

      for attribute in key.split('.'):
            UpperCamelCase					       =  getattr(_UpperCamelCase       ,							_UpperCamelCase)

      if weight_type is not None:
            UpperCamelCase					       =  getattr(_UpperCamelCase       ,							_UpperCamelCase).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 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  fairseq_model.state_dict()

      UpperCamelCase					       =  hf_model.feature_extractor
      UpperCamelCase					       =  hf_model.adapter

      for name, value in fairseq_dict.items():
            UpperCamelCase					       =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							hf_model.config.feat_extract_norm == 'group'       ,							)
                  UpperCamelCase					       =  True
            elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']):
                  load_adapter(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
                  UpperCamelCase					       =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split('w2v_model.')[-1] == name.split('.')[0]:
                              UpperCamelCase					       =  True
                              if "*" in mapped_key:
                                    UpperCamelCase					       =  name.split(_UpperCamelCase)[0].split('.')[-2]
                                    UpperCamelCase					       =  mapped_key.replace('*'       ,							_UpperCamelCase)
                              if "weight_g" in name:
                                    UpperCamelCase					       =  'weight_g'
                              elif "weight_v" in name:
                                    UpperCamelCase					       =  'weight_v'
                              elif "bias" in name:
                                    UpperCamelCase					       =  'bias'
                              elif "weight" in name:
                                    UpperCamelCase					       =  'weight'
                              else:
                                    UpperCamelCase					       =  None
                              set_recursively(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)
                        continue
            if not is_used:
                  unused_weights.append(_UpperCamelCase)

      logger.warning(F'Unused weights: {unused_weights}')
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Optional[int]:
      """simple docstring"""

      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(_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  full_name.split('adaptor.')[-1]
      UpperCamelCase					       =  name.split('.')

      if items[1].isdigit():
            UpperCamelCase					       =  int(items[1])
      else:
            UpperCamelCase					       =  None

      if "adaptor" not in full_name:
            if "proj_ln" in full_name:
                  # has to be layer norm
                  if "bias" in name:
                        assert (
                            value.shape == adapter.proj_layer_norm.bias.data.shape
                        ), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
                        UpperCamelCase					       =  value
                        logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.')
                  if "weight" in name:
                        assert (
                            value.shape == adapter.proj_layer_norm.weight.data.shape
                        ), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
                        UpperCamelCase					       =  value
            else:
                  # has to be projection layer
                  if "bias" in name:
                        assert (
                            value.shape == adapter.proj.bias.data.shape
                        ), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
                        UpperCamelCase					       =  value
                        logger.info(F'Adapter proj layer bias was initialized from {full_name}.')
                  if "weight" in name:
                        assert (
                            value.shape == adapter.proj.weight.data.shape
                        ), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
                        UpperCamelCase					       =  value
                        logger.info(F'Adapter proj layer weight was initialized from {full_name}.')
      elif isinstance(_UpperCamelCase       ,							_UpperCamelCase):
            if "bias" in name:
                  assert (
                      value.shape == adapter.layers[layer_id].conv.bias.data.shape
                  ), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
                  UpperCamelCase					       =  value
                  logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.')
            elif "weight" in name:
                  assert (
                      value.shape == adapter.layers[layer_id].conv.weight.data.shape
                  ), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
                  UpperCamelCase					       =  value
                  logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.')
      else:
            unused_weights.append(_UpperCamelCase)
def 		lowercase__  (				_UpperCamelCase) ->			List[str]:
      """simple docstring"""

      UpperCamelCase       ,  UpperCamelCase					       =  emb.weight.shape
      UpperCamelCase					       =  nn.Linear(_UpperCamelCase       ,							_UpperCamelCase       ,							bias=_UpperCamelCase)
      UpperCamelCase					       =  emb.weight.data
      return lin_layer
@torch.no_grad()
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  WavaVecaConfig.from_pretrained(
          _UpperCamelCase       ,							add_adapter=_UpperCamelCase       ,							adapter_stride=_UpperCamelCase       ,							adapter_kernel_size=_UpperCamelCase       ,							use_auth_token=_UpperCamelCase       ,							output_hidden_size=_UpperCamelCase       ,							)
      UpperCamelCase					       =  MBartConfig.from_pretrained(_UpperCamelCase)

      # load model
      UpperCamelCase       ,  UpperCamelCase       ,  UpperCamelCase					       =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]       ,							arg_overrides={
              'config_yaml': config_yaml_path,
              'data': '/'.join(dict_path.split('/')[:-1]),
              'w2v_path': checkpoint_path,
              'load_pretrained_decoder_from': None,
          }       ,							)
      UpperCamelCase					       =  model[0].eval()

      # load feature extractor
      UpperCamelCase					       =  WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase       ,							use_auth_token=_UpperCamelCase)

      # set weights for wav2vec2 encoder
      UpperCamelCase					       =  WavaVecaModel(_UpperCamelCase)

      recursively_load_weights_wavaveca(model.encoder       ,							_UpperCamelCase)

      # load decoder weights
      UpperCamelCase					       =  MBartForCausalLM(_UpperCamelCase)
      UpperCamelCase       ,  UpperCamelCase					       =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       ,							strict=_UpperCamelCase)
      logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}')
      logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}')

      UpperCamelCase					       =  SpeechEncoderDecoderModel(encoder=_UpperCamelCase       ,							decoder=_UpperCamelCase)
      UpperCamelCase					       =  False

      UpperCamelCase					       =  MBartaaTokenizer(_UpperCamelCase)
      tokenizer.save_pretrained(_UpperCamelCase)

      UpperCamelCase					       =  hf_wavavec.config.to_dict()
      UpperCamelCase					       =  tokenizer.pad_token_id
      UpperCamelCase					       =  tokenizer.bos_token_id
      UpperCamelCase					       =  tokenizer.eos_token_id
      UpperCamelCase					       =  'mbart50'
      UpperCamelCase					       =  'wav2vec2'

      UpperCamelCase					       =  tokenizer.eos_token_id
      UpperCamelCase					       =  25_00_04
      UpperCamelCase					       =  tokenizer.eos_token_id

      UpperCamelCase					       =  SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase)

      hf_wavavec.save_pretrained(_UpperCamelCase)
      feature_extractor.save_pretrained(_UpperCamelCase)


if __name__ == "__main__":
       __magic_name__   :  str        =							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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
       parser.add_argument(
           '''--encoder_config_path''',
           default='''facebook/wav2vec2-xls-r-1b''',
           type=str,
           help='''Path to hf encoder wav2vec2 checkpoint config''',
       )
       parser.add_argument(
           '''--decoder_config_path''',
           default='''facebook/mbart-large-50-one-to-many-mmt''',
           type=str,
           help='''Path to hf decoder checkpoint config''',
       )
       parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
       parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
       parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
       parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
       parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

       __magic_name__   :  Optional[int]        =							parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           args.config_yaml_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           add_adapter=args.add_adapter,
           adapter_kernel_size=args.adapter_kernel_size,
           adapter_stride=args.adapter_stride,
           decoder_start_token_id=args.start_token_id,
           encoder_output_dim=args.encoder_output_dim,
       )











__magic_name__   :  Optional[Any]        =							0  # The first color of the flag.
__magic_name__   :  List[str]        =							1  # The second color of the flag.
__magic_name__   :  Dict        =							2  # The third color of the flag.
__magic_name__   :  Union[str, Any]        =							(red, white, blue)
def 		lowercase__  (				_UpperCamelCase) ->			list:
      """simple docstring"""

      if not sequence:
            return []
      if len(_UpperCamelCase) == 1:
            return list(_UpperCamelCase)
      UpperCamelCase					       =  0
      UpperCamelCase					       =  len(_UpperCamelCase) - 1
      UpperCamelCase					       =  0
      while mid <= high:
            if sequence[mid] == colors[0]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[mid], sequence[low]
                  low += 1
                  mid += 1
            elif sequence[mid] == colors[1]:
                  mid += 1
            elif sequence[mid] == colors[2]:
                  UpperCamelCase       ,  UpperCamelCase					       =  sequence[high], sequence[mid]
                  high -= 1
            else:
                  UpperCamelCase					       =  F'The elements inside the sequence must contains only {colors} values'
                  raise ValueError(_UpperCamelCase)
      return sequence


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __magic_name__   :  str        =							input('''Enter numbers separated by commas:\n''').strip()
       __magic_name__   :  Union[str, Any]        =							[int(item.strip()) for item in user_input.split(''',''')]
       print(F'{dutch_national_flag_sort(unsorted)}')



