
"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

a       :						Union[str, Any]       		=		(
    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       :						List[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
)

a       :						str       		=		[
    '''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						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    for tf_name, hf_name in patterns:
        _UpperCAmelCase        = k.replace(A__       ,   A__   )
    return k


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = BigBirdPegasusConfig(**A__   )
    _UpperCAmelCase        = BigBirdPegasusForConditionalGeneration(A__   )
    _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(A__   ) for ending in KEYS_TO_IGNORE]
        if any(A__   ):
            continue
        _UpperCAmelCase        = DECODER_PATTERNS
        _UpperCAmelCase        = rename_state_dict_key(A__       ,   A__   )
        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(A__   )
        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(A__   ) for ending in KEYS_TO_IGNORE]
        if any(A__   ):
            continue
        _UpperCAmelCase        = REMAINING_PATTERNS
        _UpperCAmelCase        = rename_state_dict_key(A__       ,   A__   )
        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(A__   )
        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        = torch_model.load_state_dict(A__       ,   strict=A__   )
    _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 			_UpperCamelCase						(    _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = tf.train.list_variables(A__   )
    _UpperCAmelCase        = {}
    _UpperCAmelCase        = ["global_step"]
    for name, shape in tqdm(A__       ,   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(A__       ,   A__   )
        _UpperCAmelCase        = array
    return tf_weights


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = get_tf_weights_as_numpy(A__   )
    _UpperCAmelCase        = convert_bigbird_pegasus(A__       ,   A__   )
    torch_model.save_pretrained(A__   )


if __name__ == "__main__":
 a       :						List[str]       		=		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       :						List[Any]       		=		parser.parse_args()
 a       :						List[str]       		=		{}
 convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowerCamelCase_							      = TypeVar('''T''')

lowerCamelCase_							      = Union[List[T], Tuple[T, ...]]
lowerCamelCase_							      = Union[T, List[T], Dict[str, T]]
lowerCamelCase_							      = Union[str, bytes, os.PathLike]



