




'''simple docstring'''





def       __a      (  _UpperCamelCase:       int = 100       )		->					int:

     """simple docstring"""




     _snake_case				= n * (n + 1) * (2 * n + 1) / 6
     _snake_case				= (n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares       )


if __name__ == "__main__":
   print(F'{solution() = }')








'''simple docstring'''





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

UpperCamelCase_		:     Optional[int]          =		{
    '''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''',
}
UpperCamelCase_		:     int          =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[int]       )		->					int:

     """simple docstring"""




     for attribute in key.split("."       ):
          _snake_case				= getattr(_UpperCamelCase   ,		_UpperCamelCase       )

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

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





def       __a      (  _UpperCamelCase:       int   ,		_UpperCamelCase:       Tuple       )		->					str:

     """simple docstring"""




     _snake_case				= []
     _snake_case				= fairseq_model.state_dict()

     _snake_case				= hf_model.feature_extractor
     _snake_case				= hf_model.adapter

     for name, value in fairseq_dict.items():
          _snake_case				= False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		hf_model.config.feat_extract_norm == "group"   ,		)
               _snake_case				= True
          elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]       ):
               load_adapter(_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase       )
               _snake_case				= True
          else:
               for key, mapped_key in MAPPING.items():
                    if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
                         _snake_case				= True
                         if "*" in mapped_key:
                              _snake_case				= name.split(_UpperCamelCase       )[0].split("."       )[-2]
                              _snake_case				= mapped_key.replace("*"   ,		_UpperCamelCase       )
                         if "weight_g" in name:
                              _snake_case				= "weight_g"
                         elif "weight_v" in name:
                              _snake_case				= "weight_v"
                         elif "bias" in name:
                              _snake_case				= "bias"
                         elif "weight" in name:
                              _snake_case				= "weight"
                         else:
                              _snake_case				= 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       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Optional[Any]   ,		_UpperCamelCase:       Optional[Any]       )		->					Dict:

     """simple docstring"""




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





def       __a      (  _UpperCamelCase:       Dict   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[int]   ,		_UpperCamelCase:       Tuple       )		->					Any:

     """simple docstring"""




     _snake_case				= full_name.split("adaptor."       )[-1]
     _snake_case				= name.split("."       )

     if items[1].isdigit():
          _snake_case				= int(items[1]       )
     else:
          _snake_case				= 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."""
                    _snake_case				= 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."""
                    _snake_case				= 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."""
                    _snake_case				= 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."""
                    _snake_case				= 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."""
               _snake_case				= 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."""
               _snake_case				= value
               logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
     else:
          unused_weights.append(_UpperCamelCase       )





def       __a      (  _UpperCamelCase:       Tuple       )		->					Optional[Any]:

     """simple docstring"""




     _snake_case				,   _snake_case				= emb.weight.shape
     _snake_case				= nn.Linear(_UpperCamelCase   ,		_UpperCamelCase   ,		bias=_UpperCamelCase       )
     _snake_case				= emb.weight.data
     return lin_layer





@torch.no_grad()
def       __a      (  _UpperCamelCase:       List[str]   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       Dict   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       Any   ,		_UpperCamelCase:       Tuple   ,		_UpperCamelCase:       List[Any]   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Tuple   ,		)		->					List[str]:

     """simple docstring"""




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

     # load model
     _snake_case				,   _snake_case				,   _snake_case				= 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,
         }   ,		)
     _snake_case				= model[0].eval()

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

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

     recursively_load_weights_wavaveca(model.encoder   ,		_UpperCamelCase       )

     # load decoder weights
     _snake_case				= MBartForCausalLM(_UpperCamelCase       )
     _snake_case				,   _snake_case				= 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}"""       )

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

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

     _snake_case				= hf_wavavec.config.to_dict()
     _snake_case				= tokenizer.pad_token_id
     _snake_case				= tokenizer.bos_token_id
     _snake_case				= tokenizer.eos_token_id
     _snake_case				= "mbart50"
     _snake_case				= "wav2vec2"

     _snake_case				= tokenizer.eos_token_id
     _snake_case				= 250_004
     _snake_case				= tokenizer.eos_token_id

     _snake_case				= SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase       )

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


if __name__ == "__main__":
   UpperCamelCase_		:     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=250004, type=int, help='''`decoder_start_token_id` of model config''')

   UpperCamelCase_		:     Tuple          =		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,
   )


