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_							=      logging.get_logger(__name__)

a_							=      {
    """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_							=      [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def       __lowerCAmelCase      (						A_   : Tuple						,	A_   : Optional[int]						,	A_   : List[str]						,	A_   : str						,	A_   : List[Any]					)    ->  Union[str, Any]:
  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       __lowerCAmelCase      (						A_   : Any						,	A_   : List[str]					)    ->  Dict:
  __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       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : str						,	A_   : Optional[int]						,	A_   : Optional[Any]						,	A_   : Union[str, Any]					)    ->  Union[str, Any]:
  __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       __lowerCAmelCase      (						A_   : int					)    ->  Tuple:
  __UpperCAmelCase    ,		__UpperCAmelCase						    =       emb.weight.shape
  __UpperCAmelCase						    =       nn.Linear(A_						,	A_						,	bias=A_					)
  __UpperCAmelCase						    =       emb.weight.data
  return lin_layer




def       __lowerCAmelCase      (						A_   : Union[str, Any]					)    ->  str:
  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       __lowerCAmelCase      (						A_   : Union[str, Any]						,	A_   : Optional[Any]						,	A_   : Union[str, Any]						,	A_   : Tuple						,	A_   : int						,	A_   : int						,	A_   : Any						,	)    ->  Optional[int]:
  __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__":
       a_							=      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=10224, type=int, help="""Vocab size of decoder""")
       parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

       a_							=      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


a_							=      logging.get_logger(__name__)

a_							=      {
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}


class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  lowerCAmelCase__		:	Dict    		= 'vit_mae'






  def __init__(				self:			List[Any]				,				__lowerCAmelCase:			Any=768				,				__lowerCAmelCase:			List[str]=12				,				__lowerCAmelCase:			Optional[int]=12				,				__lowerCAmelCase:			Tuple=3_072				,				__lowerCAmelCase:			List[Any]="gelu"				,				__lowerCAmelCase:			Dict=0.0				,				__lowerCAmelCase:			Tuple=0.0				,				__lowerCAmelCase:			Any=0.02				,				__lowerCAmelCase:			List[Any]=1E-12				,				__lowerCAmelCase:			List[str]=224				,				__lowerCAmelCase:			Optional[Any]=16				,				__lowerCAmelCase:			Union[str, Any]=3				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			Union[str, Any]=16				,				__lowerCAmelCase:			Optional[int]=512				,				__lowerCAmelCase:			int=8				,				__lowerCAmelCase:			int=2_048				,				__lowerCAmelCase:			str=0.75				,				__lowerCAmelCase:			Union[str, Any]=False				,				**__lowerCAmelCase:			List[Any]				,				) -> Optional[int]:




    '''simple docstring'''




    super().__init__(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       num_hidden_layers
    __UpperCAmelCase						    =       num_attention_heads
    __UpperCAmelCase						    =       intermediate_size
    __UpperCAmelCase						    =       hidden_act
    __UpperCAmelCase						    =       hidden_dropout_prob
    __UpperCAmelCase						    =       attention_probs_dropout_prob
    __UpperCAmelCase						    =       initializer_range
    __UpperCAmelCase						    =       layer_norm_eps
    __UpperCAmelCase						    =       image_size
    __UpperCAmelCase						    =       patch_size
    __UpperCAmelCase						    =       num_channels
    __UpperCAmelCase						    =       qkv_bias
    __UpperCAmelCase						    =       decoder_num_attention_heads
    __UpperCAmelCase						    =       decoder_hidden_size
    __UpperCAmelCase						    =       decoder_num_hidden_layers
    __UpperCAmelCase						    =       decoder_intermediate_size
    __UpperCAmelCase						    =       mask_ratio
    __UpperCAmelCase						    =       norm_pix_loss



