"""simple docstring"""






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowerCAmelCase    :       Dict					     =(
    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
)

__lowerCAmelCase    :       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
)

__lowerCAmelCase    :       Dict					     =[
    """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 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]					,		lowerCAmelCase__     :Optional[Any]		) ->    Any:
      '''simple docstring'''


      for tf_name, hf_name in patterns:
            lowercase														=	k.replace(lowerCAmelCase__					,		lowerCAmelCase__		)
      return k


def 			UpperCAmelCase__       (				lowerCAmelCase__     :dict					,		lowerCAmelCase__     :dict		) ->    BigBirdPegasusForConditionalGeneration:
      '''simple docstring'''


      lowercase														=	BigBirdPegasusConfig(**lowerCAmelCase__		)
      lowercase														=	BigBirdPegasusForConditionalGeneration(lowerCAmelCase__		)
      lowercase														=	torch_model.state_dict()
      lowercase														=	{}

      # separating decoder weights
      lowercase														=	{k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""		)}
      lowercase														=	{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"""		):
            lowercase														=	[k.endswith(lowerCAmelCase__		) for ending in KEYS_TO_IGNORE]
            if any(lowerCAmelCase__		):
                  continue
            lowercase														=	DECODER_PATTERNS
            lowercase														=	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"""]		):
                  lowercase														=	v.T
            lowercase														=	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"""		):
            lowercase														=	[k.endswith(lowerCAmelCase__		) for ending in KEYS_TO_IGNORE]
            if any(lowerCAmelCase__		):
                  continue
            lowercase														=	REMAINING_PATTERNS
            lowercase														=	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"""]		):
                  lowercase														=	v.T
            lowercase														=	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}'

      lowercase														=	mapping["""model.embed_positions.weight"""]
      lowercase														=	mapping.pop("""model.embed_positions.weight"""		)
      lowercase							,							lowercase														=	torch_model.load_state_dict(lowerCAmelCase__					,		strict=lowerCAmelCase__		)
      lowercase														=	[
          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 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]		) ->    Dict:
      '''simple docstring'''


      lowercase														=	tf.train.list_variables(lowerCAmelCase__		)
      lowercase														=	{}
      lowercase														=	["""global_step"""]
      for name, shape in tqdm(lowerCAmelCase__					,		desc="""converting tf checkpoint to dict"""		):
            lowercase														=	any(pat in name for pat in ignore_name		)
            if skip_key:
                  continue
            lowercase														=	tf.train.load_variable(lowerCAmelCase__					,		lowerCAmelCase__		)
            lowercase														=	array
      return tf_weights


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :str					,		lowerCAmelCase__     :dict		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	get_tf_weights_as_numpy(lowerCAmelCase__		)
      lowercase														=	convert_bigbird_pegasus(lowerCAmelCase__					,		lowerCAmelCase__		)
      torch_model.save_pretrained(lowerCAmelCase__		)


if __name__ == "__main__":
       __lowerCAmelCase    :       int					     =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.""")
       __lowerCAmelCase    :       Optional[int]					     =parser.parse_args()
       __lowerCAmelCase    :       Optional[int]					     ={}
       convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




"""simple docstring"""






from ..utils import DummyObject, requires_backends





class  _A						(       metaclass=lowerCAmelCase	):
      snake_case__			:   Optional[int]							  =	['torch', 'torchsde']

      def __init__(						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(self		,       ["""torch""", """torchsde"""]						)

      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)







      @classmethod
      def 				A__       (						cls		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            requires_backends(cls		,       ["""torch""", """torchsde"""]						)



