





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCamelCase	:					str			  =					(
    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	:					List[str]			  =					(
    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	:					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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 for tf_name, hf_name in patterns:
  UpperCamelCase     : Tuple	     =       k.replace(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_					)
 return k



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			BigBirdPegasusForConditionalGeneration:
 UpperCamelCase     : Optional[Any]	     =       BigBirdPegasusConfig(**SCREAMING_SNAKE_CASE_					)
 UpperCamelCase     : int	     =       BigBirdPegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_					)
 UpperCamelCase     : Union[str, Any]	     =       torch_model.state_dict()
 UpperCamelCase     : int	     =       {}

 # separating decoder weights
 UpperCamelCase     : Union[str, Any]	     =       {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"					)}
 UpperCamelCase     : 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"					):
  UpperCamelCase     : Any	     =       [k.endswith(SCREAMING_SNAKE_CASE_					) for ending in KEYS_TO_IGNORE]
  if any(SCREAMING_SNAKE_CASE_					):
   continue
  UpperCamelCase     : Optional[int]	     =       DECODER_PATTERNS
  UpperCamelCase     : Tuple	     =       rename_state_dict_key(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_					)
  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     : Optional[Any]	     =       v.T
  UpperCamelCase     : int	     =       torch.from_numpy(SCREAMING_SNAKE_CASE_					)
  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     : Dict	     =       [k.endswith(SCREAMING_SNAKE_CASE_					) for ending in KEYS_TO_IGNORE]
  if any(SCREAMING_SNAKE_CASE_					):
   continue
  UpperCamelCase     : Optional[int]	     =       REMAINING_PATTERNS
  UpperCamelCase     : List[Any]	     =       rename_state_dict_key(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_					)
  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     : Tuple	     =       v.T
  UpperCamelCase     : List[str]	     =       torch.from_numpy(SCREAMING_SNAKE_CASE_					)
  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     : str	     =       mapping["model.embed_positions.weight"]
 UpperCamelCase     : int	     =       mapping.pop("model.embed_positions.weight"					)
 UpperCamelCase     ,   UpperCamelCase     : Dict	     =       torch_model.load_state_dict(SCREAMING_SNAKE_CASE_							,       strict=SCREAMING_SNAKE_CASE_					)
 UpperCamelCase     : int	     =       [
     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					)						->			Dict:
 UpperCamelCase     : List[Any]	     =       tf.train.list_variables(SCREAMING_SNAKE_CASE_					)
 UpperCamelCase     : List[Any]	     =       {}
 UpperCamelCase     : Tuple	     =       ["global_step"]
 for name, shape in tqdm(SCREAMING_SNAKE_CASE_							,       desc="converting tf checkpoint to dict"					):
  UpperCamelCase     : Optional[Any]	     =       any(pat in name for pat in ignore_name					)
  if skip_key:
   continue
  UpperCamelCase     : int	     =       tf.train.load_variable(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_					)
  UpperCamelCase     : Tuple	     =       array
 return tf_weights



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[Any]:
 UpperCamelCase     : Any	     =       get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_					)
 UpperCamelCase     : List[Any]	     =       convert_bigbird_pegasus(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_					)
 torch_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


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







__lowerCamelCase	:					Tuple			  =					"""ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure the supplied data is a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : str	     =       F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
  raise TypeError(_lowerCAmelCase					)

 UpperCamelCase     : Optional[int]	     =       "".join(bin(_lowerCAmelCase					)[2:].zfill(8					) for byte in data					)

 UpperCamelCase     : str	     =       len(_lowerCAmelCase					) % 6 != 0

 if padding_needed:
  # The padding that will be added later
  UpperCamelCase     : Optional[Any]	     =       b"=" * ((6 - len(_lowerCAmelCase					) % 6) // 2)

  # Append binary_stream with arbitrary binary digits (0's by default) to make its
  # length a multiple of 6.
  binary_stream += "0" * (6 - len(_lowerCAmelCase					) % 6)
 else:
  UpperCamelCase     : List[Any]	     =       b""

 # Encode every 6 binary digits to their corresponding Base64 character
 return (
     "".join(
         B64_CHARSET[int(binary_stream[index : index + 6]							,       2					)]
         for index in range(0							,       len(_lowerCAmelCase					)							,       6					)					).encode()
     + padding
 )



def 		A_    (					_lowerCAmelCase					)						->			bytes:
 # Make sure encoded_data is either a string or a bytes-like object
 if not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  UpperCamelCase     : List[Any]	     =       (
      "argument should be a bytes-like object or ASCII string, "
      F"""not '{encoded_data.__class__.__name__}'"""
  )
  raise TypeError(_lowerCAmelCase					)

 # In case encoded_data is a bytes-like object, make sure it contains only
 # ASCII characters so we convert it to a string object
 if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					):
  try:
   UpperCamelCase     : Any	     =       encoded_data.decode("utf-8"					)
  except UnicodeDecodeError:
   raise ValueError("base64 encoded data should only contain ASCII characters"					)

 UpperCamelCase     : Union[str, Any]	     =       encoded_data.count("="					)

 # Check if the encoded string contains non base64 characters
 if padding:
  assert all(
      char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
 else:
  assert all(
      char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

 # Check the padding
 assert len(_lowerCAmelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

 if padding:
  # Remove padding if there is one
  UpperCamelCase     : List[str]	     =       encoded_data[:-padding]

  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
 else:
  UpperCamelCase     : List[Any]	     =       "".join(
      bin(B64_CHARSET.index(_lowerCAmelCase					)					)[2:].zfill(6					) for char in encoded_data					)

 UpperCamelCase     : Any	     =       [
     int(binary_stream[index : index + 8]							,       2					)
     for index in range(0							,       len(_lowerCAmelCase					)							,       8					)
 ]

 return bytes(_lowerCAmelCase					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
