
'''simple docstring'''






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCAmelCase_       :   List[Any]   =       (
    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_       :   Optional[int]   =       (
    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_       :   Tuple   =       [
    '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   :    str				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      for tf_name, hf_name in patterns:
            _lowerCamelCase				:   int								=   k.replace(_lowerCAmelCase				,	_lowerCAmelCase							)
      return k







def 		A_       (	_lowerCAmelCase   :    dict				,	_lowerCAmelCase   :    dict							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   BigBirdPegasusConfig(**_lowerCAmelCase							)
      _lowerCamelCase				:   Tuple								=   BigBirdPegasusForConditionalGeneration(_lowerCAmelCase							)
      _lowerCamelCase				:   Optional[Any]								=   torch_model.state_dict()
      _lowerCamelCase				:   Optional[int]								=   {}

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

      _lowerCamelCase				:   Dict								=   mapping["model.embed_positions.weight"]
      _lowerCamelCase				:   Union[str, Any]								=   mapping.pop("model.embed_positions.weight"							)
      _lowerCamelCase						,		_lowerCamelCase				:   List[str]								=   torch_model.load_state_dict(_lowerCAmelCase				,	strict=_lowerCAmelCase							)
      _lowerCamelCase				:   Optional[Any]								=   [
          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   :    int							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    dict							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   get_tf_weights_as_numpy(_lowerCAmelCase							)
      _lowerCamelCase				:   Tuple								=   convert_bigbird_pegasus(_lowerCAmelCase				,	_lowerCAmelCase							)
      torch_model.save_pretrained(_lowerCAmelCase							)


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

'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

UpperCAmelCase_       :   str   =       (3, 9, -11, 0, 7, 5, 1, -1)
UpperCAmelCase_       :   int   =       (4, 6, 2, 0, 8, 10, 3, -2)

@dataclass
class  UpperCAmelCase__						:
    lowerCAmelCase_				    =		42
    lowerCAmelCase_				    =		42


class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[int],__A      :       Iterable[int]     ):
          _lowerCamelCase				:   Node | None								=   None
          for i in sorted(__A,reverse=__A     ):
                _lowerCamelCase				:   Dict								=   Node(__A,self.head     )



    def __iter__(	self      :       str     ):
          _lowerCamelCase				:   Dict								=   self.head
          while node:
                yield node.data
                _lowerCamelCase				:   Optional[Any]								=   node.next_node



    def __len__(	self      :       str     ):
          return sum(1 for _ in self     )



    def __str__(	self      :       str     ):
          return " -> ".join([str(__A     ) for node in self]     )







def 		A_       (	_lowerCAmelCase   :    SortedLinkedList				,	_lowerCAmelCase   :    SortedLinkedList							):



      """simple docstring"""




      return SortedLinkedList(list(_lowerCAmelCase							) + list(_lowerCAmelCase							)							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 UpperCAmelCase_       :   List[str]   =       SortedLinkedList
 print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))