






import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCamelCase__       		=  (
    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__       		=  (
    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__       		=  [
    """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	(							snake_case__  : Dict						,snake_case__  : Dict	):




     '''simple docstring'''






     for tf_name, hf_name in patterns:
          __snake_case      :Tuple 							=    k.replace(snake_case__						,snake_case__	)
     return k






def 	UpperCamelCase	(							snake_case__  : dict						,snake_case__  : dict	):




     '''simple docstring'''






     __snake_case      :Union[str, Any] 							=    BigBirdPegasusConfig(**snake_case__	)
     __snake_case      :str 							=    BigBirdPegasusForConditionalGeneration(snake_case__	)
     __snake_case      :List[str] 							=    torch_model.state_dict()
     __snake_case      :Dict 							=    {}

     # separating decoder weights
     __snake_case      :List[Any] 							=    {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""	)}
     __snake_case      :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"""	):
          __snake_case      :Optional[int] 							=    [k.endswith(snake_case__	) for ending in KEYS_TO_IGNORE]
          if any(snake_case__	):
               continue
          __snake_case      :Optional[Any] 							=    DECODER_PATTERNS
          __snake_case      :List[str] 							=    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"""]	):
               __snake_case      :int 							=    v.T
          __snake_case      :Tuple 							=    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"""	):
          __snake_case      :Optional[Any] 							=    [k.endswith(snake_case__	) for ending in KEYS_TO_IGNORE]
          if any(snake_case__	):
               continue
          __snake_case      :str 							=    REMAINING_PATTERNS
          __snake_case      :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"""]	):
               __snake_case      :int 							=    v.T
          __snake_case      :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}'''

     __snake_case      :Any 							=    mapping["""model.embed_positions.weight"""]
     __snake_case      :Tuple 							=    mapping.pop("""model.embed_positions.weight"""	)
     __snake_case							,			__snake_case      :Dict 							=    torch_model.load_state_dict(snake_case__						,strict=snake_case__	)
     __snake_case      :Any 							=    [
         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	(							snake_case__  : int	):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : str						,snake_case__  : str						,snake_case__  : dict	):




     '''simple docstring'''






     __snake_case      :Optional[Any] 							=    get_tf_weights_as_numpy(snake_case__	)
     __snake_case      :Any 							=    convert_bigbird_pegasus(snake_case__						,snake_case__	)
     torch_model.save_pretrained(snake_case__	)


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










# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import torch




class       snake_case__ (				TensorFormatter[Mapping, "torch.Tensor", Mapping]):

       '''simple docstring'''



       def __init__(				self      ,    a__=None      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            super().__init__(features=a__     )
            __snake_case      :int 							=    torch_tensor_kwargs
            import torch  # noqa import torch at initialization



       def        __lowercase    (				self      ,    a__     )			->   List[str]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    a__     ) and column:
                 if all(
                     isinstance(a__      ,    torch.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype
                     for x in column     ):
                      return torch.stack(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            import torch

            if isinstance(a__      ,    (str, bytes, type(a__     ))     ):
                 return value
            elif isinstance(a__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
                 return value.tolist()

            __snake_case      :Optional[int] 							=    {}

            if isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.integer     ):
                 __snake_case      :List[Any] 							=    {"""dtype""": torch.intaa}
            elif isinstance(a__      ,    (np.number, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.floating     ):
                 __snake_case      :Tuple 							=    {"""dtype""": torch.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(a__      ,    PIL.Image.Image     ):
                      __snake_case      :Union[str, Any] 							=    np.asarray(a__     )
            return torch.tensor(a__      ,    **{**default_dtype, **self.torch_tensor_kwargs}     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            import torch

            # support for torch, tf, jax etc.
            if hasattr(a__      ,    """__array__"""     ) and not isinstance(a__      ,    torch.Tensor     ):
                 __snake_case      :Optional[Any] 							=    data_struct.__array__()
            # support for nested types like struct of list of struct
            if isinstance(a__      ,    np.ndarray     ):
                 if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            elif isinstance(a__      ,    (list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(a__     ) for substruct in data_struct]     )
            return self._tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            return map_nested(self._recursive_tensorize      ,    a__      ,    map_list=a__     )



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.numpy_arrow_extractor().extract_row(a__     )
            __snake_case      :Any 							=    self.python_features_decoder.decode_row(a__     )
            return self.recursive_tensorize(a__     )



       def        __lowercase    (				self      ,    a__     )			->   "torch.Tensor":






            '''simple docstring'''





            __snake_case      :List[str] 							=    self.numpy_arrow_extractor().extract_column(a__     )
            __snake_case      :List[Any] 							=    self.python_features_decoder.decode_column(a__      ,    pa_table.column_names[0]     )
            __snake_case      :Optional[Any] 							=    self.recursive_tensorize(a__     )
            __snake_case      :Any 							=    self._consolidate(a__     )
            return column



       def        __lowercase    (				self      ,    a__     )			->   Mapping:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self.numpy_arrow_extractor().extract_batch(a__     )
            __snake_case      :Tuple 							=    self.python_features_decoder.decode_batch(a__     )
            __snake_case      :Tuple 							=    self.recursive_tensorize(a__     )
            for column_name in batch:
                 __snake_case      :str 							=    self._consolidate(batch[column_name]     )
            return batch


