



from ...configuration_utils import PretrainedConfig


A__	      =       {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''tapas'''




				def __init__(							self						,				_snake_case=30522						,				_snake_case=768						,				_snake_case=12						,				_snake_case=12						,				_snake_case=3072						,				_snake_case="gelu"						,				_snake_case=0.1						,				_snake_case=0.1						,				_snake_case=1024						,				_snake_case=[3, 256, 256, 2, 256, 256, 10]						,				_snake_case=0.02						,				_snake_case=1e-12						,				_snake_case=0						,				_snake_case=10.0						,				_snake_case=0						,				_snake_case=1.0						,				_snake_case=None						,				_snake_case=1.0						,				_snake_case=False						,				_snake_case=None						,				_snake_case=1.0						,				_snake_case=1.0						,				_snake_case=False						,				_snake_case=False						,				_snake_case="ratio"						,				_snake_case=None						,				_snake_case=None						,				_snake_case=64						,				_snake_case=32						,				_snake_case=False						,				_snake_case=True						,				_snake_case=False						,				_snake_case=False						,				_snake_case=True						,				_snake_case=False						,				_snake_case=None						,				_snake_case=None						,				**_snake_case						,				):



									"""simple docstring"""





									super().__init__(pad_token_id=_snake_case						,				**_snake_case		)

									# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
									_lowerCAmelCase						=    vocab_size
									_lowerCAmelCase						=    hidden_size
									_lowerCAmelCase						=    num_hidden_layers
									_lowerCAmelCase						=    num_attention_heads
									_lowerCAmelCase						=    hidden_act
									_lowerCAmelCase						=    intermediate_size
									_lowerCAmelCase						=    hidden_dropout_prob
									_lowerCAmelCase						=    attention_probs_dropout_prob
									_lowerCAmelCase						=    max_position_embeddings
									_lowerCAmelCase						=    type_vocab_sizes
									_lowerCAmelCase						=    initializer_range
									_lowerCAmelCase						=    layer_norm_eps

									# Fine-tuning task hyperparameters
									_lowerCAmelCase						=    positive_label_weight
									_lowerCAmelCase						=    num_aggregation_labels
									_lowerCAmelCase						=    aggregation_loss_weight
									_lowerCAmelCase						=    use_answer_as_supervision
									_lowerCAmelCase						=    answer_loss_importance
									_lowerCAmelCase						=    use_normalized_answer_loss
									_lowerCAmelCase						=    huber_loss_delta
									_lowerCAmelCase						=    temperature
									_lowerCAmelCase						=    aggregation_temperature
									_lowerCAmelCase						=    use_gumbel_for_cells
									_lowerCAmelCase						=    use_gumbel_for_aggregation
									_lowerCAmelCase						=    average_approximation_function
									_lowerCAmelCase						=    cell_selection_preference
									_lowerCAmelCase						=    answer_loss_cutoff
									_lowerCAmelCase						=    max_num_rows
									_lowerCAmelCase						=    max_num_columns
									_lowerCAmelCase						=    average_logits_per_cell
									_lowerCAmelCase						=    select_one_column
									_lowerCAmelCase						=    allow_empty_column_selection
									_lowerCAmelCase						=    init_cell_selection_weights_to_zero
									_lowerCAmelCase						=    reset_position_index_per_cell
									_lowerCAmelCase						=    disable_per_token_loss

									# Aggregation hyperparameters
									_lowerCAmelCase						=    aggregation_labels
									_lowerCAmelCase						=    no_aggregation_label_index

									if isinstance(self.aggregation_labels						,				_snake_case		):
														_lowerCAmelCase						=    {int(_snake_case		): v for k, v in aggregation_labels.items()}





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       [
      """decoder.version""",
      """decoder.output_projection.weight""",
      """_float_tensor""",
      """decoder.embed_positions._float_tensor""",
  ]
  for k in ignore_keys:
    state_dict.pop(A_,     A_					)

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__     ,    __magic_name__				     =       emb.weight.shape
  __magic_name__				     =       nn.Linear(A_,     A_,     bias=A_					)
  __magic_name__				     =       emb.weight.data
  return lin_layer

def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       torch.load(A_,     map_location="""cpu"""					)
  __magic_name__				     =       Namespace(**checkpoint["""cfg"""]["""model"""]					)
  __magic_name__				     =       checkpoint["""model"""]
  remove_ignore_keys_(A_					)
  __magic_name__				     =       state_dict["""decoder.embed_tokens.weight"""].shape[0]

  __magic_name__				     =       {key.replace("""decoder""",     """model"""					): val for key, val in state_dict.items()}

  __magic_name__				     =       XGLMConfig(
      vocab_size=A_,     max_position_embeddings=args.max_target_positions,     num_layers=args.decoder_layers,     attention_heads=args.decoder_attention_heads,     ffn_dim=args.decoder_ffn_embed_dim,     d_model=args.decoder_embed_dim,     layerdrop=args.decoder_layerdrop,     dropout=args.dropout,     attention_dropout=args.attention_dropout,     activation_dropout=args.activation_dropout,     activation_function="""gelu""",     scale_embedding=not args.no_scale_embedding,     tie_word_embeddings=args.share_decoder_input_output_embed,     )

  __magic_name__				     =       XGLMForCausalLM(A_					)
  __magic_name__				     =       model.load_state_dict(A_,     strict=A_					)
  print(A_					)
  __magic_name__				     =       make_linear_from_emb(model.model.embed_tokens					)

  return model


if __name__ == "__main__":
    __lowerCAmelCase			:     Dict           =   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
    parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    __lowerCAmelCase			:     List[str]           =   parser.parse_args()
    __lowerCAmelCase			:     str           =   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
    model.save_pretrained(args.pytorch_dump_folder_path)

