


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case__				:	List[Any]					       =					logging.get_logger(__name__)

snake_case__				:	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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
snake_case__				:	List[str]					       =					[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def    __lowerCamelCase   (      A__						: Any						,						A__						: Dict						,						A__						: Union[str, Any]						,						A__						: List[Any]						,						A__						: Optional[int]						,						A__						: Any					)     ->    Optional[Any]:


   for attribute in key.split("""."""					):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            lowerCamelCase_						:   Dict		= """lm_head"""

      lowerCamelCase_						:   Any		= getattr(a__						,						a__					)

   if weight_type is not None:
      lowerCamelCase_						:   Optional[Any]		= getattr(a__						,						a__					).shape
   else:
      lowerCamelCase_						:   List[Any]		= 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":
      lowerCamelCase_						:   Any		= value
   elif weight_type == "weight_g":
      lowerCamelCase_						:   Optional[int]		= value
   elif weight_type == "weight_v":
      lowerCamelCase_						:   str		= value
   elif weight_type == "bias":
      lowerCamelCase_						:   str		= value
   else:
      lowerCamelCase_						:   Tuple		= value

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


def    __lowerCamelCase   (      A__						: int						,						A__						: Optional[Any]						,						A__						: List[Any]					)     ->    int:
   lowerCamelCase_						:   str		= []
   lowerCamelCase_						:   str		= fairseq_model.state_dict()

   lowerCamelCase_						:   Any		= hf_model.unispeech.feature_extractor

   for name, value in fairseq_dict.items():
      lowerCamelCase_						:   int		= False
      if "conv_layers" in name:
         load_conv_layer(
             a__						,						a__						,						a__						,						a__						,						hf_model.config.feat_extract_norm == """group"""						,						)
         lowerCamelCase_						:   Optional[Any]		= True
      else:
         for key, mapped_key in MAPPING.items():
            lowerCamelCase_						:   Optional[Any]		= """unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
            if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
               lowerCamelCase_						:   Any		= True
               if "*" in mapped_key:
                  lowerCamelCase_						:   int		= name.split(a__					)[0].split("""."""					)[-2]
                  lowerCamelCase_						:   Optional[Any]		= mapped_key.replace("""*"""						,						a__					)
               if "weight_g" in name:
                  lowerCamelCase_						:   Any		= """weight_g"""
               elif "weight_v" in name:
                  lowerCamelCase_						:   Tuple		= """weight_v"""
               elif "bias" in name:
                  lowerCamelCase_						:   str		= """bias"""
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  lowerCamelCase_						:   Any		= """weight"""
               else:
                  lowerCamelCase_						:   str		= None
               set_recursively(a__						,						a__						,						a__						,						a__						,						a__						,						a__					)
            continue
      if not is_used:
         unused_weights.append(a__					)

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


def    __lowerCamelCase   (      A__						: Any						,						A__						: List[Any]						,						A__						: Any						,						A__						: Optional[int]						,						A__						: int					)     ->    Dict:
   lowerCamelCase_						:   str		= full_name.split("""conv_layers."""					)[-1]
   lowerCamelCase_						:   Dict		= name.split("""."""					)
   lowerCamelCase_						:   Union[str, Any]		= int(items[0]					)
   lowerCamelCase_						:   str		= 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.'''
         )
         lowerCamelCase_						:   List[str]		= 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.'''
         )
         lowerCamelCase_						:   List[Any]		= 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."
         )
         lowerCamelCase_						:   Union[str, Any]		= 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.'''
         )
         lowerCamelCase_						:   Optional[int]		= value
         logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
   else:
      unused_weights.append(a__					)


@torch.no_grad()
def    __lowerCamelCase   (      A__						: Union[str, Any]						,						A__						: Optional[int]						,						A__						: Optional[Any]=None						,						A__						: Optional[int]=None						,						A__						: List[str]=True					)     ->    List[str]:


   if config_path is not None:
      lowerCamelCase_						:   Any		= UniSpeechConfig.from_pretrained(a__					)
   else:
      lowerCamelCase_						:   int		= UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         lowerCamelCase_						:   Optional[int]		= Dictionary.load_from_json(a__					)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         lowerCamelCase_						:   str		= target_dict.pad_index
         lowerCamelCase_						:   Tuple		= target_dict.bos_index
         lowerCamelCase_						:   int		= target_dict.eos_index
         lowerCamelCase_						:   Optional[int]		= len(target_dict.symbols					)
         lowerCamelCase_						:   Optional[Any]		= os.path.join(a__						,						"""vocab.json"""					)
         if not os.path.isdir(a__					):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(a__					)					)
            return
         os.makedirs(a__						,						exist_ok=a__					)
         lowerCamelCase_						:   int		= target_dict.indices

         # fairseq has the <pad> and <s> switched
         lowerCamelCase_						:   Tuple		= 42
         lowerCamelCase_						:   Tuple		= 43
         with open(a__						,						"""w"""						,						encoding="""utf-8"""					) as vocab_handle:
            json.dump(a__						,						a__					)
         lowerCamelCase_						:   Union[str, Any]		= WavaVecaPhonemeCTCTokenizer(
             a__						,						unk_token=target_dict.unk_word						,						pad_token=target_dict.pad_word						,						bos_token=target_dict.bos_word						,						eos_token=target_dict.eos_word						,						word_delimiter_token="""|"""						,						do_lower_case=a__						,						)
         lowerCamelCase_						:   Tuple		= True if config.feat_extract_norm == """layer""" else False
         lowerCamelCase_						:   Dict		= WavaVecaFeatureExtractor(
             feature_size=1						,						sampling_rate=1_6000						,						padding_value=0						,						do_normalize=a__						,						return_attention_mask=a__						,						)
         lowerCamelCase_						:   int		= WavaVecaProcessor(feature_extractor=a__						,						tokenizer=a__					)
         processor.save_pretrained(a__					)

      lowerCamelCase_						:   List[Any]		= UniSpeechForCTC(a__					)
   else:
      lowerCamelCase_						:   Optional[Any]		= UniSpeechForPreTraining(a__					)

   if is_finetuned:
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Tuple		= fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,						arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					), """w2v_path""": checkpoint_path}					)
   else:
      lowerCamelCase_,	lowerCamelCase_,	lowerCamelCase_						:   Union[str, Any]		= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

   lowerCamelCase_						:   Dict		= model[0].eval()

   recursively_load_weights(a__						,						a__						,						a__					)

   hf_unispeech.save_pretrained(a__					)


if __name__ == "__main__":
      snake_case__				:	List[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_path', default=None, type=str, help='Path to hf config.json of model to convert')
      parser.add_argument(
          '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
      )
      snake_case__				:	Optional[int]					       =					parser.parse_args()
      convert_unispeech_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )








'''simple docstring'''



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
  raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
  raise Exception('requires fairseq < v2')

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

UpperCAmelCase					:			Tuple       =	'Hello, World!'
UpperCAmelCase					:			Any       =	'en_XX'







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = Path("""data_bin"""		)
     __SCREAMING_SNAKE_CASE      = FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(a__		).parent		) ,       checkpoint_file=Path(a__		).name ,       _name="""xmod_base""" ,       arch="""xmod_base""" ,       task="""multilingual_masked_lm""" ,       data_name_or_path=str(a__		) ,       bpe="""sentencepiece""" ,       sentencepiece_model=str(Path(a__		).parent / """sentencepiece.bpe.model"""		) ,       src_dict=str(data_dir / """dict.txt"""		) ,       )
     xmod.eval()  # disable dropout
     print(a__		)

     __SCREAMING_SNAKE_CASE      = xmod.model.encoder.sentence_encoder
     __SCREAMING_SNAKE_CASE      = XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,       hidden_size=xmod.cfg.model.encoder_embed_dim ,       num_hidden_layers=xmod.cfg.model.encoder_layers ,       num_attention_heads=xmod.cfg.model.encoder_attention_heads ,       intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,       max_position_embeddings=5_14 ,       type_vocab_size=1 ,       layer_norm_eps=1E-5 ,       pre_norm=xmod.cfg.model.encoder_normalize_before ,       adapter_reduction_factor=getattr(xmod.cfg.model ,       """bottleneck""" ,       2		) ,       adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,       adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,       ln_before_adapter=xmod.cfg.model.ln_before_adapter ,       languages=xmod.cfg.model.languages ,       )
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

     print("""Our X-MOD config:""" ,       a__		)

     __SCREAMING_SNAKE_CASE      = XmodForSequenceClassification(a__		) if classification_head else XmodForMaskedLM(a__		)
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_tokens.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.embed_positions.weight
     __SCREAMING_SNAKE_CASE      = torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight		)  # just zero them out b/c xmod doesn't use them.

     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.weight
     __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layernorm_embedding.bias

     for i in range(config.num_hidden_layers		):
          # Encoder: start of layer
          __SCREAMING_SNAKE_CASE      = model.roberta.encoder.layer[i]
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layers[i]

          # self attention
          __SCREAMING_SNAKE_CASE      = layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)		)
          ):
               raise AssertionError("""Dimensions of self-attention weights do not match."""		)

          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.q_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.k_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.v_proj.bias

          # self-attention output
          __SCREAMING_SNAKE_CASE      = layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError("""Dimensions of self-attention output weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn.out_proj.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.self_attn_layer_norm.bias

          # intermediate
          __SCREAMING_SNAKE_CASE      = layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of intermediate weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias

          # output
          __SCREAMING_SNAKE_CASE      = layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError("""Dimensions of feed-forward weights do not match."""		)
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.fca.bias
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.weight
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()		) != sorted(xmod_layer.adapter_modules.keys()		):
               raise AssertionError("""Lists of language adapters do not match."""		)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               __SCREAMING_SNAKE_CASE      = bert_output.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = xmod_layer.adapter_modules[lang_code]
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.weight
               __SCREAMING_SNAKE_CASE      = from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod_sent_encoder.layer_norm.bias

     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""].out_proj.bias
     else:
          # LM Head
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.dense.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.layer_norm.bias
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.weight
          __SCREAMING_SNAKE_CASE      = xmod.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     __SCREAMING_SNAKE_CASE      = xmod.encode(a__		).unsqueeze(0		)  # batch of size 1
     model.roberta.set_default_language(a__		)

     __SCREAMING_SNAKE_CASE      = model(a__		)[0]
     if classification_head:
          __SCREAMING_SNAKE_CASE      = xmod.model.classification_heads["""mnli"""](xmod.extract_features(a__		)		)
     else:
          __SCREAMING_SNAKE_CASE      = xmod.model(a__ ,       lang_id=[SAMPLE_LANGUAGE]		)[0]
     print(our_output.shape ,       their_output.shape		)
     __SCREAMING_SNAKE_CASE      = torch.max(torch.abs(our_output - their_output		)		).item()
     print(F'max_absolute_diff = {max_absolute_diff}'		)  # ~ 1e-7
     __SCREAMING_SNAKE_CASE      = torch.allclose(a__ ,       a__ ,       atol=1E-3		)
     print("""Do both models output the same tensors?""" ,       """🔥""" if success else """💩"""		)
     if not success:
          raise Exception("""Something went wRoNg"""		)

     Path(a__		).mkdir(parents=a__ ,       exist_ok=a__		)
     print(F'Saving model to {pytorch_dump_folder_path}'		)
     model.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			Tuple       =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  parser.add_argument(
      '--classification_head', action='store_true', help='Whether to convert a final classification head.'
  )
  UpperCAmelCase					:			Optional[int]       =	parser.parse_args()
  convert_xmod_checkpoint_to_pytorch(
      args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )






