





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_snake_case  :   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
)

_snake_case  :   List[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
)

_snake_case  :   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   a_   (			lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Union[str, Any]     ):
   for tf_name, hf_name in patterns:
      __lowerCAmelCase							     =     k.replace(lowerCAmelCase_,     lowerCAmelCase_     )
   return k

def   a_   (			lowerCAmelCase_				:      dict,     lowerCAmelCase_				:      dict     ):
   __lowerCAmelCase							     =     BigBirdPegasusConfig(**lowerCAmelCase_     )
   __lowerCAmelCase							     =     BigBirdPegasusForConditionalGeneration(lowerCAmelCase_     )
   __lowerCAmelCase							     =     torch_model.state_dict()
   __lowerCAmelCase							     =     {}

   # separating decoder weights
   __lowerCAmelCase							     =     {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'     )}
   __lowerCAmelCase							     =     {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							     =     [k.endswith(lowerCAmelCase_     ) for ending in KEYS_TO_IGNORE]
      if any(lowerCAmelCase_     ):
         continue
      __lowerCAmelCase							     =     DECODER_PATTERNS
      __lowerCAmelCase							     =     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							     =     v.T
      __lowerCAmelCase							     =     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							     =     [k.endswith(lowerCAmelCase_     ) for ending in KEYS_TO_IGNORE]
      if any(lowerCAmelCase_     ):
         continue
      __lowerCAmelCase							     =     REMAINING_PATTERNS
      __lowerCAmelCase							     =     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							     =     v.T
      __lowerCAmelCase							     =     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							     =     mapping['model.embed_positions.weight']
   __lowerCAmelCase							     =     mapping.pop('model.embed_positions.weight'     )
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     torch_model.load_state_dict(lowerCAmelCase_,     strict=lowerCAmelCase_     )
   __lowerCAmelCase							     =     [
       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_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     tf.train.list_variables(lowerCAmelCase_     )
   __lowerCAmelCase							     =     {}
   __lowerCAmelCase							     =     ['global_step']
   for name, shape in tqdm(lowerCAmelCase_,     desc='converting tf checkpoint to dict'     ):
      __lowerCAmelCase							     =     any(pat in name for pat in ignore_name     )
      if skip_key:
         continue
      __lowerCAmelCase							     =     tf.train.load_variable(lowerCAmelCase_,     lowerCAmelCase_     )
      __lowerCAmelCase							     =     array
   return tf_weights

def   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      str,     lowerCAmelCase_				:      dict     ):
   __lowerCAmelCase							     =     get_tf_weights_as_numpy(lowerCAmelCase_     )
   __lowerCAmelCase							     =     convert_bigbird_pegasus(lowerCAmelCase_,     lowerCAmelCase_     )
   torch_model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
     _snake_case  :   List[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.')
     _snake_case  :   Union[str, Any]						   =  parser.parse_args()
     _snake_case  :   Any						   =  {}
     convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     __lowerCAmelCase							     =     name
     __lowerCAmelCase							     =     value
     __lowerCAmelCase							     =     weight






  def __repr__(    self			:		Union[str, Any]					)							->						List[str]:
     return f"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""






  def     lowercase		(    self			:		int					)							->						Optional[int]:
     return self.value






  def     lowercase		(    self			:		Optional[Any]					)							->						Union[str, Any]:
     return self.name






  def     lowercase		(    self			:		List[Any]					)							->						Tuple:
     return self.weight






  def     lowercase		(    self			:		int					)							->						Dict:
     return self.value / self.weight





def   a_   (			lowerCAmelCase_				:      Dict,     lowerCAmelCase_				:      int,     lowerCAmelCase_				:      Optional[int]     ):
   __lowerCAmelCase							     =     []
   for i in range(len(lowerCAmelCase_     )     ):
      menu.append(Things(name[i],     value[i],     weight[i]     )     )
   return menu

def   a_   (			lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   __lowerCAmelCase							     =     sorted(lowerCAmelCase_,     key=lowerCAmelCase_,     reverse=lowerCAmelCase_     )
   __lowerCAmelCase							     =     []
   __lowerCAmelCase     ,     __lowerCAmelCase							     =     0.0, 0.0
   for i in range(len(lowerCAmelCase_     )     ):
      if (total_cost + items_copy[i].get_weight()) <= max_cost:
         result.append(items_copy[i]     )
         total_cost += items_copy[i].get_weight()
         total_value += items_copy[i].get_value()
   return (result, total_value)

def   a_   (			):
   pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

