





"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


__UpperCamelCase     :						Optional[int]						       =  [
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
__UpperCamelCase     :						str						       =  [
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

__UpperCamelCase     :						str						       =  (
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

__UpperCamelCase     :						List[str]						       =  (
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

__UpperCamelCase     :						Union[str, Any]						       =  [
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Optional[int]  ,     _UpperCAmelCase					:    List[Any]			):
 for tf_name, hf_name in patterns:
  lowerCAmelCase    				= k.replace(_UpperCAmelCase  ,     _UpperCAmelCase			)
 return k

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    dict  ,     _UpperCAmelCase					:    dict			):
 lowerCAmelCase    				= BigBirdPegasusConfig(**_UpperCAmelCase			)
 lowerCAmelCase    				= BigBirdPegasusForConditionalGeneration(_UpperCAmelCase			)
 lowerCAmelCase    				= torch_model.state_dict()
 lowerCAmelCase    				= {}

 # separating decoder weights
 lowerCAmelCase    				= {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'			)}
 lowerCAmelCase    				= {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'			)}

 for k, v in tqdm(decoder_weights.items()  ,     'tf -> hf conversion'			):
  lowerCAmelCase    				= [k.endswith(_UpperCAmelCase			) for ending in KEYS_TO_IGNORE]
  if any(_UpperCAmelCase			):
   continue
  lowerCAmelCase    				= DECODER_PATTERNS
  lowerCAmelCase    				= rename_state_dict_key(_UpperCAmelCase  ,     _UpperCAmelCase			)
  if new_k not in state_dict:
   raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'			)
  if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']			):
   lowerCAmelCase    				= v.T
  lowerCAmelCase    				= torch.from_numpy(_UpperCAmelCase			)
  assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

 for k, v in tqdm(remaining_weights.items()  ,     'tf -> hf conversion'			):
  lowerCAmelCase    				= [k.endswith(_UpperCAmelCase			) for ending in KEYS_TO_IGNORE]
  if any(_UpperCAmelCase			):
   continue
  lowerCAmelCase    				= REMAINING_PATTERNS
  lowerCAmelCase    				= rename_state_dict_key(_UpperCAmelCase  ,     _UpperCAmelCase			)
  if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
   raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'			)
  if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']			):
   lowerCAmelCase    				= v.T
  lowerCAmelCase    				= torch.from_numpy(_UpperCAmelCase			)
  if k != "pegasus/embeddings/position_embeddings":
   assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

 lowerCAmelCase    				= mapping['model.embed_positions.weight']
 lowerCAmelCase    				= mapping.pop('model.embed_positions.weight'			)
 lowerCAmelCase				,lowerCAmelCase    				= torch_model.load_state_dict(_UpperCAmelCase  ,     strict=_UpperCAmelCase			)
 lowerCAmelCase    				= [
     k
     for k in missing
     if k
     not in [
         'final_logits_bias',
         'model.encoder.embed_tokens.weight',
         'model.decoder.embed_tokens.weight',
         'lm_head.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 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]			):
 lowerCAmelCase    				= tf.train.list_variables(_UpperCAmelCase			)
 lowerCAmelCase    				= {}
 lowerCAmelCase    				= ['global_step']
 for name, shape in tqdm(_UpperCAmelCase  ,     desc='converting tf checkpoint to dict'			):
  lowerCAmelCase    				= any(pat in name for pat in ignore_name			)
  if skip_key:
   continue
  lowerCAmelCase    				= tf.train.load_variable(_UpperCAmelCase  ,     _UpperCAmelCase			)
  lowerCAmelCase    				= array
 return tf_weights

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    str  ,     _UpperCAmelCase					:    dict			):
 lowerCAmelCase    				= get_tf_weights_as_numpy(_UpperCAmelCase			)
 lowerCAmelCase    				= convert_bigbird_pegasus(_UpperCAmelCase  ,     _UpperCAmelCase			)
 torch_model.save_pretrained(_UpperCAmelCase			)


if __name__ == "__main__":
       __UpperCamelCase     :						Optional[Any]						       =  argparse.ArgumentParser()
       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.''')
       __UpperCamelCase     :						Optional[int]						       =  parser.parse_args()
       __UpperCamelCase     :						Optional[Any]						       =  {}
       convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)












"""simple docstring"""
def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    str			):
 return " ".join(input_str.split()[::-1]			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





