





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase_    =    (
    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
)

lowerCAmelCase_    =    (
    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
)

lowerCAmelCase_    =    [
    '''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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				for tf_name, hf_name in patterns:
								snake_case_						:   Any           =  k.replace(_UpperCamelCase			,    _UpperCamelCase			)
				return k






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						BigBirdPegasusForConditionalGeneration:






				"""simple docstring"""


				snake_case_						:   int           =  BigBirdPegasusConfig(**_UpperCamelCase			)
				snake_case_						:   Any           =  BigBirdPegasusForConditionalGeneration(_UpperCamelCase			)
				snake_case_						:   Any           =  torch_model.state_dict()
				snake_case_						:   Optional[int]           =  {}

				# separating decoder weights
				snake_case_						:   int           =  {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''			)}
				snake_case_						:   Union[str, Any]           =  {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'''			):
								snake_case_						:   int           =  [k.endswith(_UpperCamelCase			) for ending in KEYS_TO_IGNORE]
								if any(_UpperCamelCase			):
												continue
								snake_case_						:   Tuple           =  DECODER_PATTERNS
								snake_case_						:   List[str]           =  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''']			):
												snake_case_						:   int           =  v.T
								snake_case_						:   Optional[int]           =  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'''			):
								snake_case_						:   Union[str, Any]           =  [k.endswith(_UpperCamelCase			) for ending in KEYS_TO_IGNORE]
								if any(_UpperCamelCase			):
												continue
								snake_case_						:   Union[str, Any]           =  REMAINING_PATTERNS
								snake_case_						:   Union[str, Any]           =  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''']			):
												snake_case_						:   List[Any]           =  v.T
								snake_case_						:   Union[str, Any]           =  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}'''

				snake_case_						:   Tuple           =  mapping['''model.embed_positions.weight''']
				snake_case_						:   Any           =  mapping.pop('''model.embed_positions.weight'''			)
				snake_case_						:   List[str]           =  torch_model.load_state_dict(_UpperCamelCase			,    strict=_UpperCamelCase			)
				snake_case_						:   Union[str, Any]           =  [
				    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 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   List[Any]           =  tf.train.list_variables(_UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  {}
				snake_case_						:   List[Any]           =  ['''global_step''']
				for name, shape in tqdm(_UpperCamelCase			,    desc='''converting tf checkpoint to dict'''			):
								snake_case_						:   List[str]           =  any(pat in name for pat in ignore_name			)
								if skip_key:
												continue
								snake_case_						:   int           =  tf.train.load_variable(_UpperCamelCase			,    _UpperCamelCase			)
								snake_case_						:   Union[str, Any]           =  array
				return tf_weights






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  get_tf_weights_as_numpy(_UpperCamelCase			)
				snake_case_						:   Dict           =  convert_bigbird_pegasus(_UpperCamelCase			,    _UpperCamelCase			)
				torch_model.save_pretrained(_UpperCamelCase			)


if __name__ == "__main__":
		lowerCAmelCase_    =    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.''')
		lowerCAmelCase_    =    parser.parse_args()
		lowerCAmelCase_    =    {}
		convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowerCAmelCase_    =    TypeVar('''KEY''')
lowerCAmelCase_    =    TypeVar('''VAL''')



@dataclass(frozen=_a,					slots=_a					)
class  __lowerCAmelCase	(      Generic[KEY, VAL]					):
			lowerCamelCase_					:					KEY
			lowerCamelCase_					:					VAL




class  __lowerCAmelCase	(      _Item					):






			def __init__(self   )					->       None:


							'''simple docstring'''



							super().__init__(__magic_name__ ,							__magic_name__   )






			def __bool__(self   )					->       bool:


							'''simple docstring'''



							return False




lowerCAmelCase_    =    _DeletedItem()



class  __lowerCAmelCase	(      MutableMapping[KEY, VAL]					):






			def __init__(self ,							__magic_name__ = 8 ,							__magic_name__ = 0.75   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  initial_block_size
							snake_case_						:   list[_Item | None]           =  [None] * initial_block_size
							assert 0.0 < capacity_factor < 1.0
							snake_case_						:   List[str]           =  capacity_factor
							snake_case_						:   int           =  0






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return hash(__magic_name__   ) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return (ind + 1) % len(self._buckets   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self._buckets[ind]
							if not stored:
											snake_case_						:   Optional[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											self._len += 1
											return True
							elif stored.key == key:
											snake_case_						:   List[Any]           =  _Item(__magic_name__ ,							__magic_name__   )
											return True
							else:
											return False






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							snake_case_						:   Any           =  len(self._buckets   ) * self._capacity_factor
							return len(self   ) >= int(__magic_name__   )






			def       lowerCamelCase  (self   )					->       bool:


							'''simple docstring'''



							if len(self._buckets   ) <= self._initial_block_size:
											return False
							snake_case_						:   int           =  len(self._buckets   ) * self._capacity_factor / 2
							return len(self   ) < limit






			def       lowerCamelCase  (self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self._buckets
							snake_case_						:   int           =  [None] * new_size
							snake_case_						:   Optional[int]           =  0
							for item in old_buckets:
											if item:
															self._add_item(item.key ,							item.val   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) * 2   )






			def       lowerCamelCase  (self   )					->       None:


							'''simple docstring'''



							self._resize(len(self._buckets   ) // 2   )






			def       lowerCamelCase  (self ,							__magic_name__   )					->       Iterator[int]:


							'''simple docstring'''



							snake_case_						:   Dict           =  self._get_bucket_index(__magic_name__   )
							for _ in range(len(self._buckets   )   ):
											yield ind
											snake_case_						:   Tuple           =  self._get_next_ind(__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											if self._try_set(__magic_name__ ,							__magic_name__ ,							__magic_name__   ):
															break






			def __setitem__(self ,							__magic_name__ ,							__magic_name__   )					->       None:


							'''simple docstring'''



							if self._is_full():
											self._size_up()

							self._add_item(__magic_name__ ,							__magic_name__   )






			def __delitem__(self ,							__magic_name__   )					->       None:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   Optional[Any]           =  self._buckets[ind]
											if item is None:
															raise KeyError(__magic_name__   )
											if item is _deleted:
															continue
											if item.key == key:
															snake_case_						:   Union[str, Any]           =  _deleted
															self._len -= 1
															break
							if self._is_sparse():
											self._size_down()






			def __getitem__(self ,							__magic_name__   )					->       VAL:


							'''simple docstring'''



							for ind in self._iterate_buckets(__magic_name__   ):
											snake_case_						:   List[Any]           =  self._buckets[ind]
											if item is None:
															break
											if item is _deleted:
															continue
											if item.key == key:
															return item.val
							raise KeyError(__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self._len






			def __iter__(self   )					->       Iterator[KEY]:


							'''simple docstring'''



							yield from (item.key for item in self._buckets if item)






			def __repr__(self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[str]           =  ''' ,'''.join(
							    F'''{item.key}: {item.val}''' for item in self._buckets if item   )
							return F'''HashMap({val_string})'''
