





import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Any        =							[
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCAmelCase      ,  UpperCAmelCase					)



def 	_a						(   UpperCAmelCase					)    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							emb.weight.shape
   lowerCamelCase__       :						Dict        =							nn.Linear(UpperCAmelCase      ,  UpperCAmelCase      ,  bias=UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							emb.weight.data
   return lin_layer



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase=None					)    ->    List[str]:







   """simple docstring"""
   lowerCamelCase__       :						Optional[Any]        =							{}
   for old_key in state_dict.keys():
      lowerCamelCase__       :						Dict        =							old_key
      if "moe_layer.experts." in key:
         if expert_idx is not None:
            lowerCamelCase__       :						Optional[int]        =							key.replace('''moe_layer.experts.0'''      ,  f"ffn.experts.expert_{expert_idx}"					)
         else:
            lowerCamelCase__       :						List[str]        =							key.replace('''moe_layer.experts.'''      ,  '''ffn.experts.expert_'''					)
      if "gate" in key:
         lowerCamelCase__       :						List[str]        =							key.replace('''.moe_layer.gate.wg'''      ,  '''.ffn.router.classifier'''					)
      if "fc2" and "experts" not in key:
         lowerCamelCase__       :						List[Any]        =							key.replace('''.fc2.'''      ,  '''.ffn.fc2.'''					)
      if "fc1" and "experts" not in key:
         lowerCamelCase__       :						Optional[int]        =							key.replace('''.fc1.'''      ,  '''.ffn.fc1.'''					)
      if ".encoder_attn." in key:
         lowerCamelCase__       :						int        =							key.replace('''.encoder_attn.'''      ,  '''.cross_attention.'''					)
      if "encoder_attn_layer_norm" in key:
         lowerCamelCase__       :						Union[str, Any]        =							key.replace('''encoder_attn_layer_norm'''      ,  '''cross_attention_layer_norm'''					)
      if "final_layer_norm" in key:
         lowerCamelCase__       :						Dict        =							key.replace('''final_layer_norm'''      ,  '''ff_layer_norm'''					)
      lowerCamelCase__       :						Dict        =							state_dict[old_key]
   return new_dict



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase = WEIGHTS_NAME					)    ->    Any:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							[]
   lowerCamelCase__       :						int        =							0
   os.makedirs(UpperCAmelCase      ,  exist_ok=UpperCAmelCase					)

   for expert in range(UpperCAmelCase					):
      lowerCamelCase__       :						int        =							switch_checkpoint_path + f"-rank-{expert}.pt"
      if os.path.isfile(UpperCAmelCase					):
         lowerCamelCase__       :						Union[str, Any]        =							torch.load(UpperCAmelCase					)['''model''']
         remove_ignore_keys_(UpperCAmelCase					)
         lowerCamelCase__       :						List[Any]        =							rename_fairseq_keys(UpperCAmelCase      ,  UpperCAmelCase					)
         lowerCamelCase__       :						List[Any]        =							os.path.join(
             UpperCAmelCase      ,  weights_name.replace('''.bin'''      ,  f"-{len(UpperCAmelCase					)+1:05d}-of-???.bin"					)					)
         torch.save(UpperCAmelCase      ,  UpperCAmelCase					)
         sharded_state_dicts.append(expert_state.keys()					)
         total_size += sum([value.numel() for key, value in expert_state.items()]					) * dtype_byte_size(
             expert_state[list(UpperCAmelCase					)[0]].dtype					)

    # Add the last block
   lowerCamelCase__       :						str        =							os.path.join(UpperCAmelCase      ,  weights_name.replace('''.bin'''      ,  f"-{len(UpperCAmelCase					)+1:05d}-of-???.bin"					)					)
   lowerCamelCase__       :						str        =							torch.load(switch_checkpoint_path + '''-shared.pt'''					)['''model''']
   remove_ignore_keys_(UpperCAmelCase					)
   lowerCamelCase__       :						Union[str, Any]        =							rename_fairseq_keys(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Optional[int]        =							shared_weights['''decoder.embed_tokens.weight''']
   sharded_state_dicts.append(shared_weights.keys()					)

   # If we only have the shared weights (dummy model/experts saved on the same file)
   if len(UpperCAmelCase					) == 1:
      lowerCamelCase__       :						Tuple        =							os.path.join(UpperCAmelCase      ,  UpperCAmelCase					)
      torch.save(UpperCAmelCase      ,  UpperCAmelCase					)
      return {weights_name: sharded_state_dicts[0]}, None
   else:
      torch.save(UpperCAmelCase      ,  UpperCAmelCase					)
   # Otherwise, let's build the index
   lowerCamelCase__       :						str        =							{}
   for idx, shard in enumerate(UpperCAmelCase					):
      lowerCamelCase__       :						Dict        =							weights_name.replace('''.bin'''      ,  f"-{idx+1:05d}-of-{len(UpperCAmelCase					):05d}.bin"					)
      lowerCamelCase__       :						List[str]        =							os.path.join(UpperCAmelCase      ,  weights_name.replace('''.bin'''      ,  f"-{idx+1:05d}-of-???.bin"					)					)
      os.rename(UpperCAmelCase      ,  os.path.join(UpperCAmelCase      ,  UpperCAmelCase					)					)
      for key in shard:
         lowerCamelCase__       :						str        =							shard_file

    # Add the metadata
   lowerCamelCase__       :						Optional[Any]        =							{'''total_size''': total_size}
   lowerCamelCase__       :						List[Any]        =							{'''metadata''': metadata, '''weight_map''': weight_map}

   with open(os.path.join(UpperCAmelCase      ,  UpperCAmelCase					)      ,  '''w'''      ,  encoding='''utf-8'''					) as f:
      lowerCamelCase__       :						Optional[int]        =							json.dumps(UpperCAmelCase      ,  indent=2      ,  sort_keys=UpperCAmelCase					) + '''\n'''
      f.write(UpperCAmelCase					)

   return metadata, index


if __name__ == "__main__":
       _A :   Optional[Any]  			=  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--nllb_moe_checkpoint_path',
           default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000',
           type=str,
           required=False,
           help='Path to a directory containing a folder per layer. Follows the original Google format.',
       )
       parser.add_argument('--dtype', default='float32', type=str, required=False, help='dtype of the saved model')
       parser.add_argument(
           '--pytorch_dump_folder_path',
           default='/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b',
           type=str,
           required=False,
           help='Path to the output pytorch model.',
       )
       _A :   Union[str, Any]  			=  parser.parse_args()
       _A			,  _A :   int  			=  shard_on_the_fly(
           args.nllb_moe_checkpoint_path,
           args.pytorch_dump_folder_path,
           1_28,
           args.dtype,
       )

       _A :   str  			=  NllbMoeConfig.from_pretrained(
           'facebook/nllb-200-3.3B', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_28
       )
       config.save_pretrained(args.pytorch_dump_folder_path)
       _A :   int  			=  NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
       print('Done')
       model.save_pretrained(args.pytorch_dump_folder_path)










from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


_A :   List[str]  			=  logging.get_logger(__name__)


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  Any			   =   ["audio_values", "audio_mask"]







       def __init__(				self					:     Any		, A					:     Union[str, Any]=2_0_4_8		, A					:     Any=1		, A					:     int=[1_6, 1_6]		, A					:     Any=1_2_8		, A					:     List[Any]=4_4_1_0_0		, A					:     Dict=8_6		, A					:     Dict=2_0_4_8		, A					:     str=0.0		, **A					:     Union[str, Any]		, )       ->List[Any]:
          super().__init__(
              feature_size=A		, sampling_rate=A		, padding_value=A		, **A		, )

          lowerCamelCase__       :						Dict        =							spectrogram_length
          lowerCamelCase__       :						Optional[int]        =							num_channels
          lowerCamelCase__       :						str        =							patch_size
          lowerCamelCase__       :						Any        =							feature_size // self.patch_size[1]
          lowerCamelCase__       :						Union[str, Any]        =							n_fft
          lowerCamelCase__       :						Union[str, Any]        =							sampling_rate // hop_length_to_sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							sampling_rate
          lowerCamelCase__       :						Optional[Any]        =							padding_value
          lowerCamelCase__       :						Union[str, Any]        =							mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2		, num_mel_filters=A		, min_frequency=0.0		, max_frequency=2_20_50.0		, sampling_rate=A		, norm='''slaney'''		, mel_scale='''slaney'''		, ).T







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     np.array      )       ->np.ndarray:
          lowerCamelCase__       :						Any        =							spectrogram(
              A		, window_function(self.n_fft		, '''hann'''      )		, frame_length=self.n_fft		, hop_length=self.hop_length		, power=2.0		, mel_filters=self.mel_filters.T		, log_mel='''dB'''		, db_range=80.0		, )
          lowerCamelCase__       :						Any        =							log_spec[:, :-1]
          lowerCamelCase__       :						int        =							log_spec - 20.0
          lowerCamelCase__       :						int        =							np.clip(log_spec / 40.0		, -2.0		, 0.0      ) + 1.0
          return log_spec







       def __call__(				self					:     Union[str, Any]		, A					:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		, A					:     Optional[Union[str, TensorType]] = None		, A					:     Optional[bool] = True		, A					:     Optional[int] = None		, A					:     bool = False		, A					:     bool = False		, **A					:     str		, )       ->BatchFeature:

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    '''This feature extractor is set to support sampling rate'''
                    F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                    F" with {self.sampling_rate} and not {sampling_rate}."      )
          else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''      )

          lowerCamelCase__       :						List[str]        =							isinstance(A		, np.ndarray      ) and len(raw_speech.shape      ) > 1
          if is_batched_numpy and len(raw_speech.shape      ) > 2:
             raise ValueError(F"Only mono-channel audio is supported for input to {self}"      )
          lowerCamelCase__       :						Any        =							is_batched_numpy or (
              isinstance(A		, (list, tuple)      ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list)      ))
          )
          if is_batched:
             lowerCamelCase__       :						Optional[int]        =							[np.asarray([speech]		, dtype=np.floataa      ).T for speech in raw_speech]
          elif not is_batched and not isinstance(A		, np.ndarray      ):
             lowerCamelCase__       :						Optional[int]        =							np.asarray(A		, dtype=np.floataa      )
          elif isinstance(A		, np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
             lowerCamelCase__       :						Optional[Any]        =							raw_speech.astype(np.floataa      )
          # always return batch
          if not is_batched:
             lowerCamelCase__       :						int        =							[np.asarray([raw_speech]      ).T]

          # Convert audio signals to log mel spectrograms, truncate by time axis
          lowerCamelCase__       :						List[str]        =							[
              self._np_extract_fbank_features(waveform.squeeze()      ).T[: self.spectrogram_length] for waveform in raw_speech
          ]
          if isinstance(audio_features[0]		, A      ):
             lowerCamelCase__       :						Dict        =							[np.asarray(A		, dtype=np.floataa      ) for feature in audio_features]

          # Create audio attention mask
          lowerCamelCase__       :						Optional[Any]        =							max(
              [ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len for feature in audio_features]      )  # The maximum number of audio patches in a batch
          if return_attention_mask:
             lowerCamelCase__       :						List[Any]        =							[
                 (ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]      ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             lowerCamelCase__       :						List[str]        =							np.array(A      ).astype(np.floataa      )

          # convert into correct format for padding
          lowerCamelCase__       :						str        =							max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
          lowerCamelCase__       :						Optional[Any]        =							np.ones([len(A      ), 1, max_time_len, self.feature_size]      ).astype(np.floataa      )
          lowerCamelCase__       :						Tuple        =							padded_audio_features * self.padding_value
          for i in range(len(A      )      ):
             lowerCamelCase__       :						int        =							audio_features[i]
             lowerCamelCase__       :						Optional[int]        =							feature

          # return as BatchFeature
          if return_attention_mask:
             lowerCamelCase__       :						Optional[int]        =							{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
          else:
             lowerCamelCase__       :						Tuple        =							{'''audio_values''': padded_audio_features}

          lowerCamelCase__       :						Union[str, Any]        =							BatchFeature(data=A		, tensor_type=A      )
          return encoded_inputs



