





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						(
    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
)

SCREAMING_SNAKE_CASE__       :			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
)

SCREAMING_SNAKE_CASE__       :			Optional[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							, lowerCamelCase   ):
   for tf_name, hf_name in patterns:
      a__	:						Dict      		=  k.replace(lowerCamelCase							, lowerCamelCase   )
   return k






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Optional[int]      		=  BigBirdPegasusConfig(**lowerCamelCase   )
   a__	:						int      		=  BigBirdPegasusForConditionalGeneration(lowerCamelCase   )
   a__	:						List[Any]      		=  torch_model.state_dict()
   a__	:						List[Any]      		=  {}

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

   a__	:						int      		=  mapping["model.embed_positions.weight"]
   a__	:						List[str]      		=  mapping.pop("model.embed_positions.weight"   )
   a__	:						int      		=  torch_model.load_state_dict(lowerCamelCase							, strict=lowerCamelCase   )
   a__	:						Optional[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       _A  (      lowerCamelCase   ):
   a__	:						List[Any]      		=  tf.train.list_variables(lowerCamelCase   )
   a__	:						str      		=  {}
   a__	:						Any      		=  ["global_step"]
   for name, shape in tqdm(lowerCamelCase							, desc="converting tf checkpoint to dict"   ):
      a__	:						List[str]      		=  any(pat in name for pat in ignore_name   )
      if skip_key:
         continue
      a__	:						List[str]      		=  tf.train.load_variable(lowerCamelCase							, lowerCamelCase   )
      a__	:						List[Any]      		=  array
   return tf_weights






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						Union[str, Any]      		=  get_tf_weights_as_numpy(lowerCamelCase   )
   a__	:						Union[str, Any]      		=  convert_bigbird_pegasus(lowerCamelCase							, lowerCamelCase   )
   torch_model.save_pretrained(lowerCamelCase   )


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









# Lint as: python3
import itertools
import os
import re


SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__       :			List[str]			      =						re.compile(R"""([a-z\d])([A-Z])""")

SCREAMING_SNAKE_CASE__       :			Tuple			      =						re.compile(R"""(?<!_)_(?!_)""")
SCREAMING_SNAKE_CASE__       :			Dict			      =						re.compile(R"""(_{2,})""")

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						R"""^\w+(\.\w+)*$"""

SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						R"""<>:/\|?*"""






def       _A  (      lowerCamelCase   ):
   a__	:						List[str]      		=  _uppercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   a__	:						Dict      		=  _lowercase_uppercase_re.sub(r"\1_\2"							, lowerCamelCase   )
   return name.lower()






def       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  _single_underscore_re.split(lowerCamelCase   )
   a__	:						Any      		=  [_multiple_underscores_re.split(lowerCamelCase   ) for n in name]
   return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase   ) if n != ""   )






def       _A  (      lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   return camelcase_to_snakecase(lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if os.path.basename(lowerCamelCase   ) != name:
      raise ValueError(F"""Should be a dataset name, not a path: {name}"""   )
   if not re.match(_split_re							, lowerCamelCase   ):
      raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'."""   )
   return F"""{filename_prefix_for_name(lowerCamelCase   )}-{split}"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None   ):
   a__	:						Union[str, Any]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   if filetype_suffix:
      prefix += F""".{filetype_suffix}"""
   a__	:						Any      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )
   return F"""{filepath}*"""






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase=None							, lowerCamelCase=None   ):
   a__	:						List[str]      		=  filename_prefix_for_split(lowerCamelCase							, lowerCamelCase   )
   a__	:						Tuple      		=  os.path.join(lowerCamelCase							, lowerCamelCase   )

   if shard_lengths:
      a__	:						List[str]      		=  len(lowerCamelCase   )
      a__	:						str      		=  [F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(lowerCamelCase   )]
      if filetype_suffix:
         a__	:						Optional[Any]      		=  [filename + F""".{filetype_suffix}""" for filename in filenames]
      return filenames
   else:
      a__	:						Optional[int]      		=  prefix
      if filetype_suffix:
         filename += F""".{filetype_suffix}"""
      return [filename]

