



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()
a__      :				Tuple   	=			logging.get_logger(__name__)

a__      :				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",
}
a__      :				List[Any]   	=			[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      for attribute in key.split('.'	):
            lowercase__     =  getattr(A__						,  A__	)

      if weight_type is not None:
            lowercase__     =  getattr(A__						,  A__	).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   _lowerCAmelCase   (						A__						,  A__	):
      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(
                      A__						,  A__						,  A__						,  A__						,  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(A__	)[0].split('.'	)[-2]
                                    lowercase__     =  mapped_key.replace('*'						,  A__	)
                              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(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   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__	):
      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(A__	)

def   _lowerCAmelCase   (						A__	):
      lowercase__,	lowercase__     =  emb.weight.shape
      lowercase__     =  nn.Linear(A__						,  A__						,  bias=A__	)
      lowercase__     =  emb.weight.data
      return lin_layer

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

      lowercase__     =  len(A__	)

      lowercase__     =  {
          '<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   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  A__						,  ):
      lowercase__     =  WavaVecaConfig.from_pretrained(A__	)
      lowercase__     =  SpeechaTextaConfig.from_pretrained(
          A__						,  vocab_size=A__						,  decoder_layers=A__						,  do_stable_layer_norm=A__	)

      lowercase__     =  WavaVecaFeatureExtractor(
          feature_size=1						,  sampling_rate=16_000						,  padding_value=0						,  do_normalize=A__						,  return_attention_mask=A__						,  )

      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(A__	)
      lowercase__     =  recursively_load_weights_wavaveca(model.encoder						,  A__	)

      lowercase__     =  SpeechaTextaForCausalLM(A__	)
      lowercase__,	lowercase__     =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,  strict=A__	)

      # 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=A__						,  decoder=A__	)
      lowercase__     =  False

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

      lowercase__     =  create_vocab_dict(A__	)

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

      lowercase__     =  SpeechaTextaTokenizer(os.path.join(A__						,  'vocab.json'	)	)
      tokenizer.save_pretrained(A__	)

      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(A__	)

      hf_wavavec.save_pretrained(A__	)
      feature_extractor.save_pretrained(A__	)


if __name__ == "__main__":
       a__      :				Optional[int]   	=			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_02_24, type=int, help="Vocab size of decoder")
       parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")

       a__      :				Tuple   	=			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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       a__      :				Optional[Any]   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
