




'''simple docstring'''





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCamelCase_						:      Optional[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_						:      Union[str, Any]       =						(
    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_						:      Optional[int]       =						[
    '''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     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Any   )						->			int:

     """simple docstring"""




     for tf_name, hf_name in patterns:
          _snake_case							=				k.replace(_UpperCamelCase ,						_UpperCamelCase   )
     return k





def      __a     (       _UpperCamelCase:   dict ,						_UpperCamelCase:   dict   )						->			BigBirdPegasusForConditionalGeneration:

     """simple docstring"""




     _snake_case							=				BigBirdPegasusConfig(**_UpperCamelCase   )
     _snake_case							=				BigBirdPegasusForConditionalGeneration(_UpperCamelCase   )
     _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(_UpperCamelCase   ) for ending in KEYS_TO_IGNORE]
          if any(_UpperCamelCase   ):
               continue
          _snake_case							=				DECODER_PATTERNS
          _snake_case							=				rename_state_dict_key(_UpperCamelCase ,						_UpperCamelCase   )
          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(_UpperCamelCase   )
          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(_UpperCamelCase   ) for ending in KEYS_TO_IGNORE]
          if any(_UpperCamelCase   ):
               continue
          _snake_case							=				REMAINING_PATTERNS
          _snake_case							=				rename_state_dict_key(_UpperCamelCase ,						_UpperCamelCase   )
          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(_UpperCamelCase   )
          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							, _snake_case							=				torch_model.load_state_dict(_UpperCamelCase ,						strict=_UpperCamelCase   )
     _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      __a     (       _UpperCamelCase:   int   )						->			Dict:

     """simple docstring"""




     _snake_case							=				tf.train.list_variables(_UpperCamelCase   )
     _snake_case							=				{}
     _snake_case							=				["global_step"]
     for name, shape in tqdm(_UpperCamelCase ,						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(_UpperCamelCase ,						_UpperCamelCase   )
          _snake_case							=				array
     return tf_weights





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   str ,						_UpperCamelCase:   dict   )						->			str:

     """simple docstring"""




     _snake_case							=				get_tf_weights_as_numpy(_UpperCamelCase   )
     _snake_case							=				convert_bigbird_pegasus(_UpperCamelCase ,						_UpperCamelCase   )
     torch_model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      Optional[Any]       =						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_						:      Optional[int]       =						parser.parse_args()
   UpperCamelCase_						:      int       =						{}
   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 Callable
from typing import Generic, TypeVar

UpperCamelCase_						:      int       =						TypeVar('''T''')
UpperCamelCase_						:      Dict       =						TypeVar('''U''')

class        _a    (						Generic[T, U]							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				key
          _snake_case							=				val
          _snake_case							=				None
          _snake_case							=				None






     def __repr__(			self   )							->      str:
          return (
              f"""Node: key: {self.key}, val: {self.val}, """
              f"""has next: {bool(self.next   )}, has prev: {bool(self.prev   )}"""
          )







class        _a    (						Generic[T, U]							):






     def __init__(			self   )							->      None:
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          _snake_case							, _snake_case							=				self.rear, self.head






     def __repr__(			self   )							->      str:
          _snake_case							=				["DoubleLinkedList"]
          _snake_case							=				self.head
          while node.next is not None:
               rep.append(str(_SCREAMING_SNAKE_CASE   )   )
               _snake_case							=				node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      None:
          _snake_case							=				self.rear.prev

          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None

          _snake_case							=				node
          _snake_case							=				previous
          _snake_case							=				node
          _snake_case							=				self.rear






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      DoubleLinkedListNode[T, U] | None:

          if node.prev is None or node.next is None:
               return None

          _snake_case							=				node.next
          _snake_case							=				node.prev
          _snake_case							=				None
          _snake_case							=				None
          return node







class        _a    (						Generic[T, U]							):
     SCREAMING_SNAKE_CASE_							:   dict[Callable[[T], U], LRUCache[T, U]]       =						{}






     def __init__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				DoubleLinkedList()
          _snake_case							=				capacity
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				0
          _snake_case							=				{}






     def __repr__(			self   )							->      str:
          return (
              f"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              f"""capacity={self.capacity}, current size={self.num_keys})"""
          )






     def __contains__(			self ,_SCREAMING_SNAKE_CASE   )							->      bool:
          return key in self.cache






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      U | None:
          # Note: pythonic interface would throw KeyError rather than return None

          if key in self.cache:
               self.hits += 1
               _snake_case							=				self.cache[key]
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node == value_node

               # node is guaranteed not None because it is in self.cache
               assert node is not None
               self.list.add(_SCREAMING_SNAKE_CASE   )
               return node.val
          self.miss += 1
          return None






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      None:

          if key not in self.cache:
               if self.num_keys >= self.capacity:
                    # delete first node (oldest) when over capacity
                    _snake_case							=				self.list.head.next

                    # guaranteed to have a non-None first node when num_keys > 0
                    # explain to type checker via assertions
                    assert first_node is not None
                    assert first_node.key is not None
                    assert (
                        self.list.remove(_SCREAMING_SNAKE_CASE   ) is not None
                    )  # node guaranteed to be in list assert node.key is not None

                    del self.cache[first_node.key]
                    self.num_keys -= 1
               _snake_case							=				DoubleLinkedListNode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
               self.list.add(self.cache[key]   )
               self.num_keys += 1

          else:
               # bump node to the end of the list, update value
               _snake_case							=				self.list.remove(self.cache[key]   )
               assert node is not None  # node guaranteed to be in list
               _snake_case							=				value
               self.list.add(_SCREAMING_SNAKE_CASE   )






     @classmethod
     def     _lowercase     (			cls ,_SCREAMING_SNAKE_CASE = 128   )							->      Callable[[Callable[[T], U]], Callable[..., U]]:

          def cache_decorator_inner(_SCREAMING_SNAKE_CASE   ) -> Callable[..., U]:
               def cache_decorator_wrapper(*_SCREAMING_SNAKE_CASE   ) -> U:
                    if func not in cls.decorator_function_to_instance_map:
                         _snake_case							=				LRUCache(_SCREAMING_SNAKE_CASE   )

                    _snake_case							=				cls.decorator_function_to_instance_map[func].get(args[0]   )
                    if result is None:
                         _snake_case							=				func(*_SCREAMING_SNAKE_CASE   )
                         cls.decorator_function_to_instance_map[func].put(args[0] ,_SCREAMING_SNAKE_CASE   )
                    return result

               def cache_info() -> LRUCache[T, U]:
                    return cls.decorator_function_to_instance_map[func]

               setattr(_SCREAMING_SNAKE_CASE ,"cache_info" ,_SCREAMING_SNAKE_CASE   )  # noqa: B010

               return cache_decorator_wrapper

          return cache_decorator_inner


if __name__ == "__main__":
   import doctest

   doctest.testmod()


