





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def   a_   (			lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase__,     UpperCamelCase__     )

def   a_   (			lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     list(s_dict.keys()     )
   for key in keys:
      if "transformer_layers" in key:
         __lowerCAmelCase							     =     s_dict.pop(UpperCamelCase__     )
      elif "subsample" in key:
         __lowerCAmelCase							     =     s_dict.pop(UpperCamelCase__     )

def   a_   (			lowerCAmelCase_				:      str     ):
   __lowerCAmelCase							     =     emb.weight.shape
   __lowerCAmelCase							     =     nn.Linear(UpperCamelCase__,     UpperCamelCase__,     bias=UpperCamelCase__     )
   __lowerCAmelCase							     =     emb.weight.data
   return lin_layer

def   a_   (			lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Dict     ):
   __lowerCAmelCase							     =     torch.load(UpperCamelCase__,     map_location='cpu'     )
   __lowerCAmelCase							     =     mam_aaa['''args''']
   __lowerCAmelCase							     =     mam_aaa['''model''']
   __lowerCAmelCase							     =     state_dict['''decoder.output_projection.weight''']

   remove_ignore_keys_(UpperCamelCase__     )
   rename_keys(UpperCamelCase__     )

   __lowerCAmelCase							     =     state_dict['''decoder.embed_tokens.weight'''].shape[0]

   __lowerCAmelCase							     =     args.share_decoder_input_output_embed

   __lowerCAmelCase							     =     [int(UpperCamelCase__     ) for i in args.conv_kernel_sizes.split(','     )]
   __lowerCAmelCase							     =     SpeechaTextConfig(
       vocab_size=UpperCamelCase__,     max_source_positions=args.max_source_positions,     max_target_positions=args.max_target_positions,     encoder_layers=args.encoder_layers,     decoder_layers=args.decoder_layers,     encoder_attention_heads=args.encoder_attention_heads,     decoder_attention_heads=args.decoder_attention_heads,     encoder_ffn_dim=args.encoder_ffn_embed_dim,     decoder_ffn_dim=args.decoder_ffn_embed_dim,     d_model=args.encoder_embed_dim,     dropout=args.dropout,     attention_dropout=args.attention_dropout,     activation_dropout=args.activation_dropout,     activation_function='relu',     num_conv_layers=len(UpperCamelCase__     ),     conv_channels=args.conv_channels,     conv_kernel_sizes=UpperCamelCase__,     input_feat_per_channel=args.input_feat_per_channel,     input_channels=args.input_channels,     tie_word_embeddings=UpperCamelCase__,     num_beams=5,     max_length=200,     use_cache=UpperCamelCase__,     decoder_start_token_id=2,     early_stopping=UpperCamelCase__,     )

   __lowerCAmelCase							     =     SpeechaTextForConditionalGeneration(UpperCamelCase__     )
   __lowerCAmelCase							     =     model.model.load_state_dict(UpperCamelCase__,     strict=UpperCamelCase__     )
   if len(UpperCamelCase__     ) > 0 and not set(UpperCamelCase__     ) <= {
       "encoder.embed_positions.weights",
       "decoder.embed_positions.weights",
   }:
      raise ValueError(
          'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
          F""" but all the following weights are missing {missing}"""     )

   if tie_embeds:
      __lowerCAmelCase							     =     make_linear_from_emb(model.model.decoder.embed_tokens     )
   else:
      __lowerCAmelCase							     =     lm_head_weights

   model.save_pretrained(UpperCamelCase__     )


if __name__ == "__main__":
 _snake_case  :   Dict						   =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 _snake_case  :   Optional[int]						   =  parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)








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_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # Initialise PyTorch model.
   # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
   # TapasConfig to False.

   # initialize configuration from json file
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WTQ":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     True
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     0.66_4694
      __lowerCAmelCase							     =     0.20_7951
      __lowerCAmelCase							     =     0.12_1194
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     False
      __lowerCAmelCase							     =     0.035_2513

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      __lowerCAmelCase							     =     4
      __lowerCAmelCase							     =     False
      # hparam_utils.py hparams
      __lowerCAmelCase							     =     36.4519
      __lowerCAmelCase							     =     0.90_3421
      __lowerCAmelCase							     =     222.088
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     True
      __lowerCAmelCase							     =     0.76_3141

      __lowerCAmelCase							     =     TapasForQuestionAnswering(config=lowerCAmelCase_     )
   elif task == "TABFACT":
      __lowerCAmelCase							     =     TapasForSequenceClassification(config=lowerCAmelCase_     )
   elif task == "MLM":
      __lowerCAmelCase							     =     TapasForMaskedLM(config=lowerCAmelCase_     )
   elif task == "INTERMEDIATE_PRETRAINING":
      __lowerCAmelCase							     =     TapasModel(config=lowerCAmelCase_     )
   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(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )

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

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

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


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  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.'
     )
     _snake_case  :   Optional[Any]						   =  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,
     )

