
"""simple docstring"""



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCAmelCase__          = (
    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
)

UpperCAmelCase__          = (
    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
)

UpperCAmelCase__          = [
    '''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   :       str ,					__lowerCamelCase   :       List[str]		)     ->					Optional[int]:
  for tf_name, hf_name in patterns:
    _snake_case					     =       k.replace(_lowerCamelCase ,					_lowerCamelCase		)
  return k
def 					_UpperCAmelCase      ( __lowerCamelCase   :       dict ,					__lowerCamelCase   :       dict		)     ->					Dict:
  _snake_case					     =       BigBirdPegasusConfig(**_lowerCamelCase		)
  _snake_case					     =       BigBirdPegasusForConditionalGeneration(_lowerCamelCase		)
  _snake_case					     =       torch_model.state_dict()
  _snake_case					     =       {}

  # separating decoder weights
  _snake_case					     =       {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''		)}
  _snake_case					     =       {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					     =       [k.endswith(_lowerCamelCase		) for ending in KEYS_TO_IGNORE]
    if any(_lowerCamelCase		):
      continue
    _snake_case					     =       DECODER_PATTERNS
    _snake_case					     =       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''']		):
      _snake_case					     =       v.T
    _snake_case					     =       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'''		):
    _snake_case					     =       [k.endswith(_lowerCamelCase		) for ending in KEYS_TO_IGNORE]
    if any(_lowerCamelCase		):
      continue
    _snake_case					     =       REMAINING_PATTERNS
    _snake_case					     =       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''']		):
      _snake_case					     =       v.T
    _snake_case					     =       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}'''

  _snake_case					     =       mapping["""model.embed_positions.weight"""]
  _snake_case					     =       mapping.pop('''model.embed_positions.weight'''		)
  _snake_case					     =       torch_model.load_state_dict(_lowerCamelCase ,					strict=_lowerCamelCase		)
  _snake_case					     =       [
      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   :       Union[str, Any]		)     ->					Optional[int]:
  _snake_case					     =       tf.train.list_variables(_lowerCamelCase		)
  _snake_case					     =       {}
  _snake_case					     =       ["""global_step"""]
  for name, shape in tqdm(_lowerCamelCase ,					desc='''converting tf checkpoint to dict'''		):
    _snake_case					     =       any(pat in name for pat in ignore_name		)
    if skip_key:
      continue
    _snake_case					     =       tf.train.load_variable(_lowerCamelCase ,					_lowerCamelCase		)
    _snake_case					     =       array
  return tf_weights
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       str ,					__lowerCamelCase   :       dict		)     ->					int:
  _snake_case					     =       get_tf_weights_as_numpy(_lowerCamelCase		)
  _snake_case					     =       convert_bigbird_pegasus(_lowerCamelCase ,					_lowerCamelCase		)
  torch_model.save_pretrained(_lowerCamelCase		)


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








'''simple docstring'''




UpperCamelCase__       :	Optional[Any]    = [
    (10_00, '''M'''),
    (9_00, '''CM'''),
    (5_00, '''D'''),
    (4_00, '''CD'''),
    (1_00, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]


def 				lowerCAmelCase_   (	_lowerCamelCase:   str					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
		__SCREAMING_SNAKE_CASE       :		Tuple           =		0
		__SCREAMING_SNAKE_CASE       :		str           =		0
		while place < len(_lowerCamelCase					):
				if (place + 1 < len(_lowerCamelCase					)) and (vals[roman[place]] < vals[roman[place + 1]]):
						total += vals[roman[place + 1]] - vals[roman[place]]
						place += 2
				else:
						total += vals[roman[place]]
						place += 1
		return total


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		Any           =		[]
		for arabic, roman in ROMAN:
				((__SCREAMING_SNAKE_CASE)       ,						(__SCREAMING_SNAKE_CASE))       :		str           =		divmod(_lowerCamelCase       ,      _lowerCamelCase					)
				result.append(roman * factor					)
				if number == 0:
						break
		return "".join(_lowerCamelCase					)


if __name__ == "__main__":
						import doctest

						doctest.testmod()