


"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__snake_case             =   (
    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
)

__snake_case             =   (
    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
)

__snake_case             =   [
    """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						(						lowercase  :  int						,							lowercase  :  int			)     ->						Optional[Any]:


    """simple docstring"""





    for tf_name, hf_name in patterns:
        snake_case   :				int				 =   k.replace(lowercase						,							lowercase			)
    return k







def   __lowerCAmelCase						(						lowercase  :  dict						,							lowercase  :  dict			)     ->						BigBirdPegasusForConditionalGeneration:


    """simple docstring"""





    snake_case   :				Tuple				 =   BigBirdPegasusConfig(**lowercase			)
    snake_case   :				Union[str, Any]				 =   BigBirdPegasusForConditionalGeneration(lowercase			)
    snake_case   :				Optional[int]				 =   torch_model.state_dict()
    snake_case   :				Optional[int]				 =   {}

    # separating decoder weights
    snake_case   :				str				 =   {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"			)}
    snake_case   :				Dict				 =   {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   :				Any				 =   [k.endswith(lowercase			) for ending in KEYS_TO_IGNORE]
        if any(lowercase			):
            continue
        snake_case   :				Tuple				 =   DECODER_PATTERNS
        snake_case   :				List[Any]				 =   rename_state_dict_key(lowercase						,							lowercase			)
        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   :				List[Any]				 =   v.T
        snake_case   :				Dict				 =   torch.from_numpy(lowercase			)
        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   :				Tuple				 =   [k.endswith(lowercase			) for ending in KEYS_TO_IGNORE]
        if any(lowercase			):
            continue
        snake_case   :				Any				 =   REMAINING_PATTERNS
        snake_case   :				Dict				 =   rename_state_dict_key(lowercase						,							lowercase			)
        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   :				Tuple				 =   v.T
        snake_case   :				int				 =   torch.from_numpy(lowercase			)
        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   :				Optional[Any]				 =   mapping["model.embed_positions.weight"]
    snake_case   :				Any				 =   mapping.pop("model.embed_positions.weight"			)
    snake_case ,snake_case   :				List[str]				 =   torch_model.load_state_dict(lowercase						,							strict=lowercase			)
    snake_case   :				List[str]				 =   [
        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						(						lowercase  :  Optional[Any]			)     ->						Dict:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  str						,							lowercase  :  str						,							lowercase  :  dict			)     ->						Optional[int]:


    """simple docstring"""





    snake_case   :				Dict				 =   get_tf_weights_as_numpy(lowercase			)
    snake_case   :				List[str]				 =   convert_bigbird_pegasus(lowercase						,							lowercase			)
    torch_model.save_pretrained(lowercase			)


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









"""simple docstring"""



import math
import sys







def   __lowerCAmelCase						(						lowercase  :  int			)     ->						int:


    """simple docstring"""





    if number != int(lowercase			):
        raise ValueError("the value of input must be a natural number"			)
    if number < 0:
        raise ValueError("the value of input must not be a negative number"			)
    if number == 0:
        return 1
    snake_case   :				Optional[Any]				 =   [-1] * (number + 1)
    snake_case   :				str				 =   0
    for i in range(1						,							number + 1			):
        snake_case   :				List[Any]				 =   sys.maxsize
        snake_case   :				Union[str, Any]				 =   int(math.sqrt(lowercase			)			)
        for j in range(1						,							root + 1			):
            snake_case   :				List[str]				 =   1 + answers[i - (j**2)]
            snake_case   :				Optional[Any]				 =   min(lowercase						,							lowercase			)
        snake_case   :				Any				 =   answer
    return answers[number]


if __name__ == "__main__":
  import doctest

  doctest.testmod()





