



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A       =						(
    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
)

__A       =						(
    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
)

__A       =						[
    '''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 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : Union[str, Any]    )				->			Any:






						'''simple docstring'''



						for tf_name, hf_name in patterns:
												UpperCAmelCase_=       k.replace(lowerCAmelCase_      ,lowerCAmelCase_    )
						return k



def 	__a					(   lowerCAmelCase_       : dict      ,lowerCAmelCase_       : dict    )				->			BigBirdPegasusForConditionalGeneration:






						'''simple docstring'''



						UpperCAmelCase_=       BigBirdPegasusConfig(**lowerCAmelCase_    )
						UpperCAmelCase_=       BigBirdPegasusForConditionalGeneration(lowerCAmelCase_    )
						UpperCAmelCase_=       torch_model.state_dict()
						UpperCAmelCase_=       {}

						# separating decoder weights
						UpperCAmelCase_=       {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""    )}
						UpperCAmelCase_=       {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"""    ):
												UpperCAmelCase_=       [k.endswith(lowerCAmelCase_    ) for ending in KEYS_TO_IGNORE]
												if any(lowerCAmelCase_    ):
																		continue
												UpperCAmelCase_=       DECODER_PATTERNS
												UpperCAmelCase_=       rename_state_dict_key(lowerCAmelCase_      ,lowerCAmelCase_    )
												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"""]    ):
																		UpperCAmelCase_=       v.T
												UpperCAmelCase_=       torch.from_numpy(lowerCAmelCase_    )
												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"""    ):
												UpperCAmelCase_=       [k.endswith(lowerCAmelCase_    ) for ending in KEYS_TO_IGNORE]
												if any(lowerCAmelCase_    ):
																		continue
												UpperCAmelCase_=       REMAINING_PATTERNS
												UpperCAmelCase_=       rename_state_dict_key(lowerCAmelCase_      ,lowerCAmelCase_    )
												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"""]    ):
																		UpperCAmelCase_=       v.T
												UpperCAmelCase_=       torch.from_numpy(lowerCAmelCase_    )
												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}"""

						UpperCAmelCase_=       mapping["""model.embed_positions.weight"""]
						UpperCAmelCase_=       mapping.pop("""model.embed_positions.weight"""    )
						UpperCAmelCase_,       UpperCAmelCase_=       torch_model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						UpperCAmelCase_=       [
						    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 	__a					(   lowerCAmelCase_       : Any    )				->			Dict:






						'''simple docstring'''



						UpperCAmelCase_=       tf.train.list_variables(lowerCAmelCase_    )
						UpperCAmelCase_=       {}
						UpperCAmelCase_=       ["""global_step"""]
						for name, shape in tqdm(lowerCAmelCase_      ,desc="""converting tf checkpoint to dict"""    ):
												UpperCAmelCase_=       any(pat in name for pat in ignore_name    )
												if skip_key:
																		continue
												UpperCAmelCase_=       tf.train.load_variable(lowerCAmelCase_      ,lowerCAmelCase_    )
												UpperCAmelCase_=       array
						return tf_weights



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : dict    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       get_tf_weights_as_numpy(lowerCAmelCase_    )
						UpperCAmelCase_=       convert_bigbird_pegasus(lowerCAmelCase_      ,lowerCAmelCase_    )
						torch_model.save_pretrained(lowerCAmelCase_    )


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









import warnings
from functools import wraps
from typing import Callable



def 	__a					(   lowerCAmelCase_       : Callable    )				->			Callable:






						'''simple docstring'''



						@wraps(lowerCAmelCase_    )
						def _inner_fn(*lowerCAmelCase_       : List[Any]      ,**lowerCAmelCase_       : Tuple    ):
												warnings.warn(
												    (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")      ,lowerCAmelCase_      ,)
												return fn(*lowerCAmelCase_      ,**lowerCAmelCase_    )

						return _inner_fn




