





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A									=				(
    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
)

__A									=				(
    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
)

__A									=				[
    "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      lowerCAmelCase_ (       __a	,    __a		)     ->		Union[str, Any]:







  """simple docstring"""





  for tf_name, hf_name in patterns:
    lowerCamelCase__:      Union[str, Any]			  =k.replace(__a	,    __a		)
  return k





def      lowerCAmelCase_ (       __a	,    __a		)     ->		BigBirdPegasusForConditionalGeneration:







  """simple docstring"""





  lowerCamelCase__:      Tuple			  =BigBirdPegasusConfig(**__a		)
  lowerCamelCase__:      Any			  =BigBirdPegasusForConditionalGeneration(__a		)
  lowerCamelCase__:      List[str]			  =torch_model.state_dict()
  lowerCamelCase__:      Any			  ={}

  # separating decoder weights
  lowerCamelCase__:      Union[str, Any]			  ={k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"		)}
  lowerCamelCase__:      List[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"		):
    lowerCamelCase__:      Dict			  =[k.endswith(__a		) for ending in KEYS_TO_IGNORE]
    if any(__a		):
      continue
    lowerCamelCase__:      List[Any]			  =DECODER_PATTERNS
    lowerCamelCase__:      Union[str, Any]			  =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"]		):
      lowerCamelCase__:      Optional[Any]			  =v.T
    lowerCamelCase__:      List[Any]			  =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"		):
    lowerCamelCase__:      int			  =[k.endswith(__a		) for ending in KEYS_TO_IGNORE]
    if any(__a		):
      continue
    lowerCamelCase__:      str			  =REMAINING_PATTERNS
    lowerCamelCase__:      List[Any]			  =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"]		):
      lowerCamelCase__:      List[Any]			  =v.T
    lowerCamelCase__:      Union[str, Any]			  =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}"""

  lowerCamelCase__:      List[Any]			  =mapping["model.embed_positions.weight"]
  lowerCamelCase__:      List[Any]			  =mapping.pop("model.embed_positions.weight"		)
  lowerCamelCase__  ,					lowerCamelCase__:      Union[str, Any]			  =torch_model.load_state_dict(__a	,    strict=__a		)
  lowerCamelCase__:      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      lowerCAmelCase_ (       __a		)     ->		Dict:







  """simple docstring"""





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





def      lowerCAmelCase_ (       __a	,    __a	,    __a		)     ->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:      str			  =get_tf_weights_as_numpy(__a		)
  lowerCamelCase__:      str			  =convert_bigbird_pegasus(__a	,    __a		)
  torch_model.save_pretrained(__a		)


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








class    _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self				:   Optional[Any]       , UpperCAmelCase_				:   int)       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =n
   lowerCamelCase__:      Tuple			  =[None] * self.n
   lowerCamelCase__:      str			  =0  # index of the first element
   lowerCamelCase__:      Tuple			  =0
   lowerCamelCase__:      Optional[Any]			  =0

 def __len__(self				:   str)       ->int:



   '''simple docstring'''



   return self.size

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->bool:



   '''simple docstring'''



   return self.size == 0

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->str:



   '''simple docstring'''



   return False if self.is_empty() else self.array[self.front]

 def 							SCREAMING_SNAKE_CASE_					(self				:   int       , UpperCAmelCase_				:   Optional[int])       ->str:



   '''simple docstring'''



   if self.size >= self.n:
     raise Exception("QUEUE IS FULL")

   lowerCamelCase__:      List[Any]			  =data
   lowerCamelCase__:      Dict			  =(self.rear + 1) % self.n
   self.size += 1
   return self







 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Tuple:



   '''simple docstring'''



   if self.size == 0:
     raise Exception("UNDERFLOW")

   lowerCamelCase__:      Optional[Any]			  =self.array[self.front]
   lowerCamelCase__:      Optional[int]			  =None
   lowerCamelCase__:      Dict			  =(self.front + 1) % self.n
   self.size -= 1
   return temp

