

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowercase_             =     logging.get_logger(__name__)

lowercase_             =     {
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowercase_             =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]
def   a   (    A__			:     List[str]  )					->   int:





      """simple docstring"""



      _lowercase			 ={}
      with open(__lowerCAmelCase   ,     'r'  ) as file:
            for line_number, line in enumerate(__lowerCAmelCase  ):
                  _lowercase			 =line.strip()
                  if line:
                        _lowercase			 =line.split()
                        _lowercase			 =line_number
                        _lowercase			 =words[0]
                        _lowercase			 =value
      return result
def   a   (    A__			:     Optional[Any]   ,     A__			:     int   ,     A__			:     List[str]   ,     A__			:     str   ,     A__			:     List[Any]  )					->   Dict:





      """simple docstring"""



      for attribute in key.split('.'  ):
            _lowercase			 =getattr(__lowerCAmelCase   ,     __lowerCAmelCase  )

      _lowercase			 =None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(__lowerCAmelCase  ):
                  _lowercase			 =PARAM_MAPPING[full_name.split('.'  )[-1]]
                  _lowercase			 ='param'

      if weight_type is not None and weight_type != "param":
            _lowercase			 =getattr(__lowerCAmelCase   ,     __lowerCAmelCase  ).shape
      elif weight_type is not None and weight_type == "param":
            _lowercase			 =hf_pointer
            for attribute in hf_param_name.split('.'  ):
                  _lowercase			 =getattr(__lowerCAmelCase   ,     __lowerCAmelCase  )
            _lowercase			 =shape_pointer.shape

            # let's reduce dimension
            _lowercase			 =value[0]
      else:
            _lowercase			 =hf_pointer.shape

      if hf_shape != value.shape:
            raise ValueError(
                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
      elif weight_type == "param":
            for attribute in hf_param_name.split('.'  ):
                  _lowercase			 =getattr(__lowerCAmelCase   ,     __lowerCAmelCase  )
            _lowercase			 =value
      else:
            _lowercase			 =value

      logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''  )
def   a   (    A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     int   ,     A__			:     int   ,     A__			:     int  )					->   Any:





      """simple docstring"""



      _lowercase			 =None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(__lowerCAmelCase  ):
                  _lowercase			 =PARAM_MAPPING[full_name.split('.'  )[-1]]
                  _lowercase			 ='param'

      if weight_type is not None and weight_type != "param":
            _lowercase			 ='.'.join([key, weight_type]  )
      elif weight_type is not None and weight_type == "param":
            _lowercase			 ='.'.join([key, hf_param_name]  )
      else:
            _lowercase			 =key

      _lowercase			 =value if 'lm_head' in full_key else value[0]


lowercase_             =     {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}
def   a   (    A__			:     Tuple   ,     A__			:     Any   ,     A__			:     str=None   ,     A__			:     Optional[int]=None  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase			 =False
      for key, mapped_key in MAPPING.items():
            _lowercase			 ='wav2vec2.' + 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]:
                  _lowercase			 =True
                  if "*" in mapped_key:
                        _lowercase			 =name.split(__lowerCAmelCase  )[0].split('.'  )[-2]
                        _lowercase			 =mapped_key.replace('*'   ,     __lowerCAmelCase  )
                  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:
                        # TODO: don't match quantizer.weight_proj
                        _lowercase			 ='weight'
                  else:
                        _lowercase			 =None
                  if hf_dict is not None:
                        rename_dict(__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase  )
                  else:
                        set_recursively(__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase  )
                  return is_used
      return is_used
def   a   (    A__			:     Tuple   ,     A__			:     List[Any]   ,     A__			:     int  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase			 =[]
      _lowercase			 =fairseq_model.state_dict()

      _lowercase			 =hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            _lowercase			 =False
            if "conv_layers" in name:
                  load_conv_layer(
                      __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase   ,     hf_model.config.feat_extract_norm == 'group'   ,     )
                  _lowercase			 =True
            else:
                  _lowercase			 =load_wavaveca_layer(__lowerCAmelCase   ,     __lowerCAmelCase   ,     __lowerCAmelCase  )
            if not is_used:
                  unused_weights.append(__lowerCAmelCase  )

      logger.warning(F'''Unused weights: {unused_weights}'''  )
def   a   (    A__			:     Any   ,     A__			:     List[Any]   ,     A__			:     Optional[int]   ,     A__			:     Tuple   ,     A__			:     Union[str, Any]  )					->   int:





      """simple docstring"""



      _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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F'''{full_name} has size {value.shape}, but'''
                            F''' {feature_extractor.conv_layers[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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F'''{full_name} has size {value.shape}, but'''
                            F''' {feature_extractor.conv_layers[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(__lowerCAmelCase  )
@torch.no_grad()
def   a   (    A__			:     Optional[Any]   ,     A__			:     Dict   ,     A__			:     Optional[int]=None   ,     A__			:     Any=None   ,     A__			:     Any=True   ,     A__			:     str=False  )					->   str:





      """simple docstring"""



      if config_path is not None:
            _lowercase			 =WavaVecaConfig.from_pretrained(__lowerCAmelCase  )
      else:
            _lowercase			 =WavaVecaConfig()

      if is_seq_class:
            _lowercase			 =read_txt_into_dict(__lowerCAmelCase  )
            _lowercase			 =idalabel
            _lowercase			 =WavaVecaForSequenceClassification(__lowerCAmelCase  )
            _lowercase			 =WavaVecaFeatureExtractor(
                feature_size=1   ,     sampling_rate=16000   ,     padding_value=0   ,     do_normalize=__lowerCAmelCase   ,     return_attention_mask=__lowerCAmelCase   ,     )
            feature_extractor.save_pretrained(__lowerCAmelCase  )

      elif is_finetuned:
            if dict_path:
                  _lowercase			 =Dictionary.load(__lowerCAmelCase  )

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

                  # fairseq has the <pad> and <s> switched
                  _lowercase			 =0
                  _lowercase			 =1
                  with open(__lowerCAmelCase   ,     'w'   ,     encoding='utf-8'  ) as vocab_handle:
                        json.dump(__lowerCAmelCase   ,     __lowerCAmelCase  )
                  _lowercase			 =WavaVecaCTCTokenizer(
                      __lowerCAmelCase   ,     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=__lowerCAmelCase   ,     )
                  _lowercase			 =True if config.feat_extract_norm == 'layer' else False
                  _lowercase			 =WavaVecaFeatureExtractor(
                      feature_size=1   ,     sampling_rate=16000   ,     padding_value=0   ,     do_normalize=__lowerCAmelCase   ,     return_attention_mask=__lowerCAmelCase   ,     )
                  _lowercase			 =WavaVecaProcessor(feature_extractor=__lowerCAmelCase   ,     tokenizer=__lowerCAmelCase  )
                  processor.save_pretrained(__lowerCAmelCase  )

            _lowercase			 =WavaVecaForCTC(__lowerCAmelCase  )
      else:
            _lowercase			 =WavaVecaForPreTraining(__lowerCAmelCase  )

      if is_finetuned or is_seq_class:
            _lowercase , _lowercase , _lowercase			 =fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]   ,     arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  )}  )
      else:
            _lowercase			 =argparse.Namespace(task='audio_pretraining'  )
            _lowercase			 =fairseq.tasks.setup_task(__lowerCAmelCase  )

            _lowercase , _lowercase , _lowercase			 =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   ,     task=__lowerCAmelCase  )

      _lowercase			 =model[0].eval()

      recursively_load_weights(__lowerCAmelCase   ,     __lowerCAmelCase   ,     not is_finetuned  )

      hf_wavavec.save_pretrained(__lowerCAmelCase  )


if __name__ == "__main__":
     lowercase_             =     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'
     )
     parser.add_argument(
         '--is_seq_class',
         action='store_true',
         help='Whether the model to convert is a fine-tuned sequence classification model or not',
     )
     lowercase_             =     parser.parse_args()

     lowercase_             =     not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )





import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WTQ":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =True
            # hparam_utils.py hparams
            _lowercase			 =0.664694
            _lowercase			 =0.207951
            _lowercase			 =0.121194
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =False
            _lowercase			 =0.0352513

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            _lowercase			 =4
            _lowercase			 =False
            # hparam_utils.py hparams
            _lowercase			 =36.4519
            _lowercase			 =0.903421
            _lowercase			 =222.088
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =True
            _lowercase			 =0.763141

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =TapasModel(config=A__  )
      else:
            raise ValueError(F'''Task {task} not supported.'''  )

      print(F'''Building PyTorch model from configuration: {config}'''  )
      # Load weights from tf checkpoint
      load_tf_weights_in_tapas(A__   ,     A__   ,     A__  )

      # Save pytorch-model (weights and configuration)
      print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
      model.save_pretrained(A__  )

      # Save tokenizer files
      print(F'''Save tokenizer files to {pytorch_dump_path}'''  )
      _lowercase			 =TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'   ,     model_max_length=512  )
      tokenizer.save_pretrained(A__  )

      print('Used relative position embeddings:'   ,     model.config.reset_position_index_per_cell  )


if __name__ == "__main__":
  lowercase_             =     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
  )
  parser.add_argument(
      '--reset_position_index_per_cell',
      default=False,
      action='store_true',
      help='Whether to use relative position embeddings or not. Defaults to True.',
  )
  parser.add_argument(
      '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
  )
  parser.add_argument(
      '--tapas_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained TAPAS model. \n'
          'This specifies the model architecture.'
      ),
  )
  parser.add_argument(
      '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
  )
  lowercase_             =     parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )


