






"""simple docstring"""







import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


lowerCAmelCase_	:    Optional[int]            =      ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase_	:    List[str]            =      {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
 raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
lowerCAmelCase_	:    Optional[int]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[Any]            =      ''' Hello world! cécé herlolip'''

lowerCAmelCase_	:    Optional[Any]            =      [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]






def       _lowerCAmelCase       (      lowerCAmelCase		):







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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			dct.pop(lowerCAmelCase		)
       UpperCAmelCase	    =			val






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)
       UpperCAmelCase	    =			torch.hub.load("""pytorch/fairseq"""	,      """bart.large.cnn"""		).eval()
       hub_interface.model.load_state_dict(sd["""model"""]		)
       return hub_interface






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase    ,       UpperCAmelCase	    =			emb.weight.shape
       UpperCAmelCase	    =			nn.Linear(lowerCAmelCase	,      lowerCAmelCase	,      bias=lowerCAmelCase		)
       UpperCAmelCase	    =			emb.weight.data
       return lin_layer






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None		):







       '''simple docstring'''
       if not os.path.exists(lowerCAmelCase		):
              UpperCAmelCase	    =			torch.hub.load("""pytorch/fairseq"""	,      lowerCAmelCase		).eval()
       else:
              UpperCAmelCase	    =			load_xsum_checkpoint(lowerCAmelCase		)

       bart.model.upgrade_state_dict(bart.model.state_dict()		)
       if hf_checkpoint_name is None:
              UpperCAmelCase	    =			checkpoint_path.replace("""."""	,      """-"""		)
       UpperCAmelCase	    =			BartConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			bart.encode(lowerCAmelCase		).unsqueeze(0		)
       UpperCAmelCase	    =			BartTokenizer.from_pretrained(lowerCAmelCase		).encode(lowerCAmelCase	,      return_tensors="""pt"""		).unsqueeze(0		)
       if not torch.eq(lowerCAmelCase	,      lowerCAmelCase		).all():
              raise ValueError(
                  F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''		)

       if checkpoint_path == "bart.large.mnli":
              UpperCAmelCase	    =			bart.state_dict()
              remove_ignore_keys_(lowerCAmelCase		)
              UpperCAmelCase	    =			state_dict["""model.decoder.embed_tokens.weight"""]
              for src, dest in mnli_rename_keys:
                     rename_key(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              UpperCAmelCase	    =			BartForSequenceClassification(lowerCAmelCase		).eval()
              model.load_state_dict(lowerCAmelCase		)
              UpperCAmelCase	    =			bart.predict("""mnli"""	,      lowerCAmelCase	,      return_logits=lowerCAmelCase		)
              UpperCAmelCase	    =			model(lowerCAmelCase		)[0]  # logits
       else:  # no classification heads to worry about
              UpperCAmelCase	    =			bart.model.state_dict()
              remove_ignore_keys_(lowerCAmelCase		)
              UpperCAmelCase	    =			state_dict["""decoder.embed_tokens.weight"""]
              UpperCAmelCase	    =			bart.extract_features(lowerCAmelCase		)
              if hf_checkpoint_name == "facebook/bart-large":
                     UpperCAmelCase	    =			BartModel(lowerCAmelCase		).eval()
                     model.load_state_dict(lowerCAmelCase		)
                     UpperCAmelCase	    =			model(lowerCAmelCase		).model[0]
              else:
                     UpperCAmelCase	    =			BartForConditionalGeneration(lowerCAmelCase		).eval()  # an existing summarization ckpt
                     model.model.load_state_dict(lowerCAmelCase		)
                     if hasattr(lowerCAmelCase	,      """lm_head"""		):
                            UpperCAmelCase	    =			make_linear_from_emb(model.model.shared		)
                     UpperCAmelCase	    =			model.model(lowerCAmelCase		)[0]

    # Check results
       if fairseq_output.shape != new_model_outputs.shape:
              raise ValueError(
                  F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''		)
       if (fairseq_output != new_model_outputs).any().item():
              raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""		)
       Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
       model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    int            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
 )
 parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument(
     '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
 )
 lowerCAmelCase_	:    Tuple            =      parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)








"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""projector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""projector.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""model.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForXVector.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""connector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              UpperCAmelCase	    =			downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCAmelCase	    =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       UpperCAmelCase	    =			downstream_dict["""objective.W"""]
       return model






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)

       UpperCAmelCase	    =			checkpoint["""Downstream"""]

       UpperCAmelCase	    =			UniSpeechSatConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(
           lowerCAmelCase	,      return_attention_mask=lowerCAmelCase	,      do_normalize=lowerCAmelCase		)

       UpperCAmelCase	    =			hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""		):
              UpperCAmelCase	    =			convert_classification(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForAudioFrameClassification"""		):
              UpperCAmelCase	    =			convert_diarization(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForXVector"""		):
              UpperCAmelCase	    =			convert_xvector(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       else:
              raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''		)

       if hf_config.use_weighted_layer_sum:
              UpperCAmelCase	    =			checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(lowerCAmelCase		)
       hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    List[Any]            =      argparse.ArgumentParser()
 parser.add_argument(
     '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
 )
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
 parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
 parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
 lowerCAmelCase_	:    str            =      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
