"""simple docstring"""
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowercase__ 	=     (
    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
)

lowercase__ 	=     (
    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
)

lowercase__ 	=     [
    '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       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  for tf_name, hf_name in patterns:
    a__:					str      							=     k.replace(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  return k







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->BigBirdPegasusForConditionalGeneration:
  a__:					List[str]      							=     BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     torch_model.state_dict()
  a__:					List[Any]      							=     {}

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

  a__:					List[Any]      							=     mapping['model.embed_positions.weight']
  a__:					Any      							=     mapping.pop('model.embed_positions.weight'       )
  a__							,   a__:					int      							=     torch_model.load_state_dict(_SCREAMING_SNAKE_CASE		,					strict=_SCREAMING_SNAKE_CASE       )
  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 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Dict:
  a__:					Dict      							=     tf.train.list_variables(_SCREAMING_SNAKE_CASE       )
  a__:					Dict      							=     {}
  a__:					Dict      							=     ['global_step']
  for name, shape in tqdm(_SCREAMING_SNAKE_CASE		,					desc='converting tf checkpoint to dict'       ):
    a__:					List[Any]      							=     any(pat in name for pat in ignore_name       )
    if skip_key:
      continue
    a__:					Any      							=     tf.train.load_variable(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
    a__:					str      							=     array
  return tf_weights







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[Any]:
  a__:					Any      							=     get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  torch_model.save_pretrained(_SCREAMING_SNAKE_CASE       )


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



"""simple docstring"""
def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  return credit_card_number.startswith(('34', '35', '37', '4', '5', '6')       )







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Any      							=     credit_card_number
  a__:					Tuple      							=     0
  a__:					List[str]      							=     len(_SCREAMING_SNAKE_CASE       ) - 2
  for i in range(_SCREAMING_SNAKE_CASE		,					-1		,					-2       ):
    #  double the value of every second digit
    a__:					Tuple      							=     int(cc_number[i]       )
    digit *= 2
    # If doubling of a number results in a two digit number
    # i.e greater than 9(e.g., 6 × 2 = 12),
    # then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
    # to get a single digit number.
    if digit > 9:
      digit %= 10
      digit += 1
    a__:					Optional[Any]      							=     cc_number[:i] + str(_SCREAMING_SNAKE_CASE       ) + cc_number[i + 1 :]
    total += digit

  # Sum up the remaining digits
  for i in range(len(_SCREAMING_SNAKE_CASE       ) - 1		,					-1		,					-2       ):
    total += int(cc_number[i]       )

  return total % 10 == 0







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->bool:
  a__:					Optional[int]      							=     F'{credit_card_number} is an invalid credit card number because'
  if not credit_card_number.isdigit():
    print(F'{error_message} it has nonnumerical characters.'       )
    return False

  if not 13 <= len(_SCREAMING_SNAKE_CASE       ) <= 16:
    print(F'{error_message} of its length.'       )
    return False

  if not validate_initial_digits(_SCREAMING_SNAKE_CASE       ):
    print(F'{error_message} of its first two digits.'       )
    return False

  if not luhn_validation(_SCREAMING_SNAKE_CASE       ):
    print(F'{error_message} it fails the Luhn check.'       )
    return False

  print(F'{credit_card_number} is a valid credit card number.'       )
  return True


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 validate_credit_card_number('4111111111111111')
 validate_credit_card_number('32323')


