





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Tuple			,							snake_case__  :Optional[Any]			,							snake_case__  :List[str]			,							snake_case__  :Any     )      ->						Optional[Any]:
 for attribute in key.split('.'     ):
  _lowercase							= getattr(snake_case__			,							snake_case__     )

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

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

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]			,							snake_case__  :Union[str, Any]     )      ->						Union[str, Any]:
 _lowercase							= []
 _lowercase							= fairseq_model.state_dict()

 _lowercase							= hf_model.feature_extractor

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

 for name, value in fairseq_dict.items():
  _lowercase							= False
  if "conv_layers" in name:
   load_conv_layer(
       snake_case__			,							snake_case__			,							snake_case__			,							snake_case__			,							hf_model.config.feat_extract_norm == 'group'			,							)
   _lowercase							= True
  elif name.split('.'     )[0] == "proj":
   _lowercase							= fairseq_model.proj
   _lowercase							= True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
     _lowercase							= True
     if "*" in mapped_key:
      _lowercase							= name.split(snake_case__     )[0].split('.'     )[-2]
      _lowercase							= mapped_key.replace('*'			,							snake_case__     )
     if "weight_g" in name:
      _lowercase							= 'weight_g'
     elif "weight_v" in name:
      _lowercase							= 'weight_v'
     elif "bias" in name:
      _lowercase							= 'bias'
     elif "weight" in name:
      _lowercase							= 'weight'
     else:
      _lowercase							= None
     set_recursively(snake_case__			,							snake_case__			,							snake_case__			,							snake_case__			,							snake_case__     )
    continue
  if not is_used:
   unused_weights.append(snake_case__     )

 logger.warning(F"""Unused weights: {unused_weights}"""     )

 return proj_weight

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Any			,							snake_case__  :Union[str, Any]			,							snake_case__  :int			,							snake_case__  :Tuple			,							snake_case__  :Any     )      ->						str:
 _lowercase							= full_name.split('conv_layers.'     )[-1]
 _lowercase							= name.split('.'     )
 _lowercase							= int(items[0]     )
 _lowercase							= 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."""
   )
   _lowercase							= 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."""
   )
   _lowercase							= 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."
   )
   _lowercase							= 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."""
   )
   _lowercase							= value
   logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
 else:
  unused_weights.append(snake_case__     )

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						Optional[Any]:
 _lowercase						,		_lowercase							= emb.weight.shape
 _lowercase							= nn.Linear(snake_case__			,							snake_case__			,							bias=snake_case__     )
 _lowercase							= emb.weight.data
 return lin_layer

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int     )      ->						List[Any]:
 with open(snake_case__			,							'r'			,							encoding='utf-8'     ) as f:
  _lowercase							= f.readlines()
  _lowercase							= [line.split(' '     )[0] for line in lines]

 _lowercase							= len(snake_case__     )

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

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

@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Tuple			,							snake_case__  :Dict			,							snake_case__  :List[Any]			,							snake_case__  :int			,							snake_case__  :Optional[int]			,							snake_case__  :List[str]			,							snake_case__  :List[str]			,							)      ->						List[Any]:
 _lowercase							= WavaVecaConfig.from_pretrained(snake_case__     )
 _lowercase							= SpeechaTextaConfig.from_pretrained(
     snake_case__			,							vocab_size=snake_case__			,							decoder_layers=snake_case__			,							do_stable_layer_norm=snake_case__     )

 _lowercase							= WavaVecaFeatureExtractor(
     feature_size=1			,							sampling_rate=1_6000			,							padding_value=0			,							do_normalize=snake_case__			,							return_attention_mask=snake_case__			,							)

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

 # set weights for wav2vec2 encoder
 _lowercase							= WavaVecaModel(snake_case__     )
 _lowercase							= recursively_load_weights_wavaveca(model.encoder			,							snake_case__     )

 _lowercase							= SpeechaTextaForCausalLM(snake_case__     )
 _lowercase						,		_lowercase							= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,							strict=snake_case__     )

 # set output linear layer
 unexpected_keys.remove('embed_out'     )
 _lowercase							= 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}"""     )

 _lowercase							= SpeechEncoderDecoderModel(encoder=snake_case__			,							decoder=snake_case__     )
 _lowercase							= False

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

 _lowercase							= create_vocab_dict(snake_case__     )

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

 _lowercase							= SpeechaTextaTokenizer(os.path.join(snake_case__			,							'vocab.json'     )     )
 tokenizer.save_pretrained(snake_case__     )

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

 _lowercase							= SpeechEncoderDecoderConfig.from_dict(snake_case__     )

 hf_wavavec.save_pretrained(snake_case__     )
 feature_extractor.save_pretrained(snake_case__     )


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,
      )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""BertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)