




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[Any]    =		BigBirdPegasusConfig(**snake_case_  )
      _A							:  Tuple    =		BigBirdPegasusForConditionalGeneration(snake_case_  )
      _A							:  Union[str, Any]    =		torch_model.state_dict()
      _A							:  List[str]    =		{}

      # separating decoder weights
      _A							:  Any    =		{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""  )}
      _A							:  Union[str, Any]    =		{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							:  Tuple    =		[k.endswith(snake_case_  ) for ending in KEYS_TO_IGNORE]
            if any(snake_case_  ):
                  continue
            _A							:  Optional[int]    =		DECODER_PATTERNS
            _A							:  List[Any]    =		rename_state_dict_key(snake_case_,snake_case_  )
            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							:  List[Any]    =		v.T
            _A							:  Dict    =		torch.from_numpy(snake_case_  )
            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							:  Dict    =		[k.endswith(snake_case_  ) for ending in KEYS_TO_IGNORE]
            if any(snake_case_  ):
                  continue
            _A							:  Any    =		REMAINING_PATTERNS
            _A							:  List[Any]    =		rename_state_dict_key(snake_case_,snake_case_  )
            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							:  List[Any]    =		v.T
            _A							:  Tuple    =		torch.from_numpy(snake_case_  )
            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							:  List[Any]    =		mapping["""model.embed_positions.weight"""]
      _A							:  List[str]    =		mapping.pop("""model.embed_positions.weight"""  )
      _A							:  Dict    =		torch_model.load_state_dict(snake_case_,strict=snake_case_  )
      _A							:  List[str]    =		[
          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_			(  snake_case_  ):
      _A							:  Union[str, Any]    =		tf.train.list_variables(snake_case_  )
      _A							:  Any    =		{}
      _A							:  Optional[Any]    =		["""global_step"""]
      for name, shape in tqdm(snake_case_,desc="""converting tf checkpoint to dict"""  ):
            _A							:  Union[str, Any]    =		any(pat in name for pat in ignore_name  )
            if skip_key:
                  continue
            _A							:  List[str]    =		tf.train.load_variable(snake_case_,snake_case_  )
            _A							:  Optional[int]    =		array
      return tf_weights







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  int    =		get_tf_weights_as_numpy(snake_case_  )
      _A							:  Optional[Any]    =		convert_bigbird_pegasus(snake_case_,snake_case_  )
      torch_model.save_pretrained(snake_case_  )


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







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

