




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCAmelCase	:       Tuple     =	(
    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	:       Tuple     =	(
    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	:       List[Any]     =	[
    '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     __magic_name__       (				A	:      Any,						A	:      Optional[int]     ):






   '''simple docstring'''







   for tf_name, hf_name in patterns:
      a					=    k.replace(A,						A     )
   return k




def     __magic_name__       (				A	:      dict,						A	:      dict     ):






   '''simple docstring'''







   a					=    BigBirdPegasusConfig(**A     )
   a					=    BigBirdPegasusForConditionalGeneration(A     )
   a					=    torch_model.state_dict()
   a					=    {}

   # separating decoder weights
   a					=    {k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"     )}
   a					=    {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					=    [k.endswith(A     ) for ending in KEYS_TO_IGNORE]
      if any(A     ):
         continue
      a					=    DECODER_PATTERNS
      a					=    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"]     ):
         a					=    v.T
      a					=    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"     ):
      a					=    [k.endswith(A     ) for ending in KEYS_TO_IGNORE]
      if any(A     ):
         continue
      a					=    REMAINING_PATTERNS
      a					=    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"]     ):
         a					=    v.T
      a					=    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}"""

   a					=    mapping["model.embed_positions.weight"]
   a					=    mapping.pop("model.embed_positions.weight"     )
   a					,	a					=    torch_model.load_state_dict(A,						strict=A     )
   a					=    [
       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     __magic_name__       (				A	:      Dict     ):






   '''simple docstring'''







   a					=    tf.train.list_variables(A     )
   a					=    {}
   a					=    ["global_step"]
   for name, shape in tqdm(A,						desc="converting tf checkpoint to dict"     ):
      a					=    any(pat in name for pat in ignore_name     )
      if skip_key:
         continue
      a					=    tf.train.load_variable(A,						A     )
      a					=    array
   return tf_weights




def     __magic_name__       (				A	:      str,						A	:      str,						A	:      dict     ):






   '''simple docstring'''







   a					=    get_tf_weights_as_numpy(A     )
   a					=    convert_bigbird_pegasus(A,						A     )
   torch_model.save_pretrained(A     )


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












def     __magic_name__       (				A	:      int,						A	:      int,						A	:      int     ):






			'''simple docstring'''







			if exponent == 1:
						return base
			if exponent % 2 == 0:
						a					=    _modexpt(A,						exponent // 2,						A     ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(A,						exponent - 1,						A     )) % modulo_value




def     __magic_name__       (				A	:      int = 1777,						A	:      int = 1855,						A	:      int = 8     ):






			'''simple docstring'''







			a					=    base
			for _ in range(1,						A     ):
						a					=    _modexpt(A,						A,						10**digits     )

			return result


if __name__ == "__main__":
		print(F'''{solution() = }''')






