



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE_:Tuple             =						(
    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
)

SCREAMING_SNAKE_CASE_:Union[str, Any]             =						(
    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
)

SCREAMING_SNAKE_CASE_: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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Dict:



    """simple docstring"""






    for tf_name, hf_name in patterns:
        A :       List[Any]          =   k.replace(_lowerCAmelCase      ,     _lowerCAmelCase		)
    return k




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  BigBirdPegasusForConditionalGeneration:



    """simple docstring"""






    A :       int          =   BigBirdPegasusConfig(**_lowerCAmelCase		)
    A :       Tuple          =   BigBirdPegasusForConditionalGeneration(_lowerCAmelCase		)
    A :       int          =   torch_model.state_dict()
    A :       Any          =   {}

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

    A :       Dict          =   mapping["""model.embed_positions.weight"""]
    A :       List[Any]          =   mapping.pop("""model.embed_positions.weight"""		)
    A     ,							A :       int          =   torch_model.load_state_dict(_lowerCAmelCase      ,     strict=_lowerCAmelCase		)
    A :       Tuple          =   [
        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 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  Dict:



    """simple docstring"""






    A :       Any          =   tf.train.list_variables(_lowerCAmelCase		)
    A :       Union[str, Any]          =   {}
    A :       List[str]          =   ["""global_step"""]
    for name, shape in tqdm(_lowerCAmelCase      ,     desc="""converting tf checkpoint to dict"""		):
        A :       Tuple          =   any(pat in name for pat in ignore_name		)
        if skip_key:
            continue
        A :       Union[str, Any]          =   tf.train.load_variable(_lowerCAmelCase      ,     _lowerCAmelCase		)
        A :       Dict          =   array
    return tf_weights




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Union[str, Any]:



    """simple docstring"""






    A :       Any          =   get_tf_weights_as_numpy(_lowerCAmelCase		)
    A :       Optional[Any]          =   convert_bigbird_pegasus(_lowerCAmelCase      ,     _lowerCAmelCase		)
    torch_model.save_pretrained(_lowerCAmelCase		)


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







def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  list:



    """simple docstring"""






    return [
        txt[:a] + txt[a].upper() + txt[a + 1 :]
        for a in range(len(_lowerCAmelCase		)		)
        if txt[a].isalpha()
    ]


if __name__ == "__main__":
     __import__("""doctest""").testmod()


