





'''simple docstring'''




import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


lowercase__       : int         =       [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]     )	->	int:
     for pegasus_name, hf_name in PATTERNS:
          __A		:					Optional[Any]						=			k.replace(__snake_case					,				__snake_case     )
     return k






def 			_lowerCAmelCase     (    __snake_case    :					dict					,				__snake_case    :					dict     )	->	PegasusForConditionalGeneration:
     __A		:					Dict						=			DEFAULTS.copy()
     cfg_kwargs.update(__snake_case     )
     __A		:					List[Any]						=			PegasusConfig(**__snake_case     )
     __A		:					Dict						=			PegasusForConditionalGeneration(__snake_case     )
     __A		:					Tuple						=			torch_model.model.state_dict()
     __A		:					int						=			{}
     for k, v in tf_weights.items():
          __A		:					int						=			rename_state_dict_key(__snake_case     )
          if new_k not in sd:
               raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})'     )

          if "dense" in k or "proj" in new_k:
               __A		:					int						=			v.T
          __A		:					Optional[int]						=			torch.tensor(__snake_case					,				dtype=sd[new_k].dtype     )
          assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
     # make sure embedding.padding_idx is respected
     __A		:					Optional[Any]						=			torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]     )
     __A		:					Tuple						=			mapping['shared.weight']
     __A		:					Dict						=			mapping['shared.weight']
     __A		:					int						=			{k: torch.zeros_like(__snake_case     ) for k, v in sd.items() if k.endswith('bias'     ) and k not in mapping}
     mapping.update(**__snake_case     )
     __A					,__A		:					List[Any]						=			torch_model.model.load_state_dict(__snake_case					,				strict=__snake_case     )
     __A		:					int						=			[
         k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
     ]
     assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], f'no matches found for the following tf keys {extra}'
     return torch_model






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]="./ckpt/aeslc/model.ckpt-32000"     )	->	Dict:
     __A		:					str						=			tf.train.list_variables(__snake_case     )
     __A		:					List[Any]						=			{}
     __A		:					str						=			['Adafactor', 'global_step']
     for name, shape in tqdm(__snake_case					,				desc='converting tf checkpoint to dict'     ):
          __A		:					Optional[int]						=			any(pat in name for pat in ignore_name     )
          if skip_key:
               continue
          __A		:					Tuple						=			tf.train.load_variable(__snake_case					,				__snake_case     )
          __A		:					Dict						=			array
     return tf_weights






def 			_lowerCAmelCase     (    __snake_case    :					str					,				__snake_case    :					str     )	->	Tuple:
     # save tokenizer first
     __A		:					int						=			Path(__snake_case     ).parent.name
     __A		:					int						=			task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
     __A		:					str						=			PegasusTokenizer.from_pretrained('sshleifer/pegasus'					,				model_max_length=__snake_case     )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(__snake_case     )

     # convert model
     __A		:					Dict						=			get_tf_weights_as_numpy(__snake_case     )
     __A		:					List[str]						=			task_specific_params[f'summarization_{dataset}']
     if dataset == "large":
          __A		:					str						=			task_specific_params
     __A		:					Any						=			convert_pegasus(__snake_case					,				__snake_case     )
     torch_model.save_pretrained(__snake_case     )
     __A		:					Optional[int]						=			torch_model.state_dict()
     sd.pop('model.decoder.embed_positions.weight'     )
     sd.pop('model.encoder.embed_positions.weight'     )
     torch.save(__snake_case					,				Path(__snake_case     ) / 'pytorch_model.bin'     )


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
  parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
  lowercase__       : Any         =       parser.parse_args()
  if args.save_dir is None:
    lowercase__       : Tuple         =       Path(args.tf_ckpt_path).parent.name
    lowercase__       : Any         =       os.path.join('''pegasus''', dataset)
  convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)






'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''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 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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

          # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
          __A		:					Dict						=			vocab_size
          __A		:					Tuple						=			hidden_size
          __A		:					Any						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					Tuple						=			intermediate_size
          __A		:					List[Any]						=			hidden_dropout_prob
          __A		:					int						=			attention_probs_dropout_prob
          __A		:					List[str]						=			max_position_embeddings
          __A		:					Optional[int]						=			type_vocab_sizes
          __A		:					str						=			initializer_range
          __A		:					List[str]						=			layer_norm_eps

          # Fine-tuning task hyperparameters
          __A		:					List[str]						=			positive_label_weight
          __A		:					List[Any]						=			num_aggregation_labels
          __A		:					Optional[Any]						=			aggregation_loss_weight
          __A		:					Tuple						=			use_answer_as_supervision
          __A		:					List[str]						=			answer_loss_importance
          __A		:					Any						=			use_normalized_answer_loss
          __A		:					Any						=			huber_loss_delta
          __A		:					Union[str, Any]						=			temperature
          __A		:					Tuple						=			aggregation_temperature
          __A		:					Optional[Any]						=			use_gumbel_for_cells
          __A		:					List[str]						=			use_gumbel_for_aggregation
          __A		:					Tuple						=			average_approximation_function
          __A		:					List[str]						=			cell_selection_preference
          __A		:					Dict						=			answer_loss_cutoff
          __A		:					Union[str, Any]						=			max_num_rows
          __A		:					Optional[Any]						=			max_num_columns
          __A		:					int						=			average_logits_per_cell
          __A		:					Optional[Any]						=			select_one_column
          __A		:					int						=			allow_empty_column_selection
          __A		:					List[Any]						=			init_cell_selection_weights_to_zero
          __A		:					int						=			reset_position_index_per_cell
          __A		:					Union[str, Any]						=			disable_per_token_loss

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}