


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__snake_case									=		{
    '''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''',
}
__snake_case									=		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 						_A						(						_lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase      ) ->      Any:


     """simple docstring"""


     for attribute in key.split('.'      ):
          __UpperCamelCase      =       getattr(_A ,   _A      )

     if weight_type is not None:
          __UpperCamelCase      =       getattr(_A ,   _A      ).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 						_A						(						_lowercase ,   _lowercase      ) ->      str:


     """simple docstring"""


     __UpperCamelCase      =       []
     __UpperCamelCase      =       fairseq_model.state_dict()

     __UpperCamelCase      =       hf_model.feature_extractor

     # if encoder has different dim to decoder -> use proj_weight
     __UpperCamelCase      =       None

     for name, value in fairseq_dict.items():
          __UpperCamelCase      =       False
          if "conv_layers" in name:
               load_conv_layer(
                   _A ,   _A ,   _A ,   _A ,   hf_model.config.feat_extract_norm == 'group' ,   )
               __UpperCamelCase      =       True
          elif name.split('.'      )[0] == "proj":
               __UpperCamelCase      =       fairseq_model.proj
               __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(_A      )[0].split('.'      )[-2]
                              __UpperCamelCase      =       mapped_key.replace('*' ,   _A      )
                         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(_A ,   _A ,   _A ,   _A ,   _A      )
                    continue
          if not is_used:
               unused_weights.append(_A      )

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

     return proj_weight






def 						_A						(						_lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase      ) ->      Any:


     """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(_A      )






def 						_A						(						_lowercase      ) ->      List[str]:


     """simple docstring"""


     __UpperCamelCase,  __UpperCamelCase      =       emb.weight.shape
     __UpperCamelCase      =       nn.Linear(_A ,   _A ,   bias=_A      )
     __UpperCamelCase      =       emb.weight.data
     return lin_layer






def 						_A						(						_lowercase      ) ->      List[str]:


     """simple docstring"""


     with open(_A ,   'r' ,   encoding='utf-8'      ) as f:
          __UpperCamelCase      =       f.readlines()
          __UpperCamelCase      =       [line.split(' '      )[0] for line in lines]

     __UpperCamelCase      =       len(_A      )

     __UpperCamelCase      =       {
         '<s>': 0,
         '<pad>': 1,
         '</s>': 2,
         '<unk>': 3,
     }

     vocab_dict.update(dict(zip(_A ,   range(4 ,   num_words + 4      )      )      )      )
     return vocab_dict






@torch.no_grad()
def 						_A						(						_lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   _lowercase ,   ) ->      Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase      =       WavaVecaConfig.from_pretrained(_A      )
     __UpperCamelCase      =       SpeechaTextaConfig.from_pretrained(
         _A ,   vocab_size=_A ,   decoder_layers=_A ,   do_stable_layer_norm=_A      )

     __UpperCamelCase      =       WavaVecaFeatureExtractor(
         feature_size=1 ,   sampling_rate=1_60_00 ,   padding_value=0 ,   do_normalize=_A ,   return_attention_mask=_A ,   )

     __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase      =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path] ,   arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     __UpperCamelCase      =       model[0].eval()

     # set weights for wav2vec2 encoder
     __UpperCamelCase      =       WavaVecaModel(_A      )
     __UpperCamelCase      =       recursively_load_weights_wavaveca(model.encoder ,   _A      )

     __UpperCamelCase      =       SpeechaTextaForCausalLM(_A      )
     __UpperCamelCase,  __UpperCamelCase      =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,   strict=_A      )

     # set output linear layer
     unexpected_keys.remove('embed_out'      )
     __UpperCamelCase      =       nn.Parameter(model.decoder.embed_out.detach()      )

     # layer norm is init to identity matrix so leaving it is fine
     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=_A ,   decoder=_A      )
     __UpperCamelCase      =       False

     # add projection layer
     __UpperCamelCase      =       nn.Parameter(projection_layer.weight      )
     __UpperCamelCase      =       nn.Parameter(projection_layer.bias      )

     __UpperCamelCase      =       create_vocab_dict(_A      )

     with open(os.path.join(_A ,   'vocab.json'      ) ,   'w'      ) as fp:
          json.dump(_A ,   _A      )

     __UpperCamelCase      =       SpeechaTextaTokenizer(os.path.join(_A ,   'vocab.json'      )      )
     tokenizer.save_pretrained(_A      )

     __UpperCamelCase      =       hf_wavavec.config.to_dict()
     __UpperCamelCase      =       tokenizer.pad_token_id
     __UpperCamelCase      =       tokenizer.bos_token_id
     __UpperCamelCase      =       tokenizer.eos_token_id
     __UpperCamelCase      =       'speech_to_text_2'
     __UpperCamelCase      =       'wav2vec2'

     __UpperCamelCase      =       SpeechEncoderDecoderConfig.from_dict(_A      )

     hf_wavavec.save_pretrained(_A      )
     feature_extractor.save_pretrained(_A      )


if __name__ == "__main__":
    __snake_case									=		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(
        '''--encoder_config_path''',
        default='''facebook/wav2vec2-large-lv60''',
        type=str,
        help='''Path to hf encoder wav2vec2 checkpoint config''',
    )
    parser.add_argument(
        '''--decoder_config_path''',
        default='''facebook/s2t-small-mustc-en-fr-st''',
        type=str,
        help='''Path to hf decoder s2t checkpoint config''',
    )
    parser.add_argument('''--vocab_size''', default=1_0_2_2_4, type=int, help='''Vocab size of decoder''')
    parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

    __snake_case									=		parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        vocab_size=args.vocab_size,
        num_decoder_layers=args.num_decoder_layers,
    )



'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowercase          =  {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''NezhaForNextSentencePrediction''',
						    '''NezhaForMaskedLM''',
						    '''NezhaForPreTraining''',
						    '''NezhaForMultipleChoice''',
						    '''NezhaForQuestionAnswering''',
						    '''NezhaForSequenceClassification''',
						    '''NezhaForTokenClassification''',
						    '''NezhaModel''',
						    '''NezhaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




