





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__lowercase	: Tuple       =(
    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
)

__lowercase	: Dict       =(
    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
)

__lowercase	: Tuple       =[
    """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__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   for tf_name, hf_name in patterns:
      UpperCAmelCase_           =k.replace(lowercase__	,     lowercase__	)
   return k




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =BigBirdPegasusConfig(**lowercase__	)
   UpperCAmelCase_           =BigBirdPegasusForConditionalGeneration(lowercase__	)
   UpperCAmelCase_           =torch_model.state_dict()
   UpperCAmelCase_           ={}

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

   UpperCAmelCase_           =mapping["model.embed_positions.weight"]
   UpperCAmelCase_           =mapping.pop("model.embed_positions.weight"	)
   UpperCAmelCase_    ,							UpperCAmelCase_           =torch_model.load_state_dict(lowercase__	,     strict=lowercase__	)
   UpperCAmelCase_           =[
       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__			(       lowercase__	):






   '''simple docstring'''






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




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =get_tf_weights_as_numpy(lowercase__	)
   UpperCAmelCase_           =convert_bigbird_pegasus(lowercase__	,     lowercase__	)
   torch_model.save_pretrained(lowercase__	)


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







from __future__ import annotations

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							text, pattern
              __snake_case	,    __snake_case       	=							len(_UpperCamelCase   ), len(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if char == self.pattern[i]:
                            return i
              return -1


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


              for i in range(self.patLen - 1      , -1      , -1   ):
                     if self.pattern[i] != self.text[current_pos + i]:
                            return current_pos + i
              return -1






       def 			a		(		self   )      ->							list[int]:






              """simple docstring"""


              __snake_case       	=							[]
              for i in range(self.textLen - self.patLen + 1   ):
                     __snake_case       	=							self.mismatch_in_text(_UpperCamelCase   )
                     if mismatch_index == -1:
                            positions.append(_UpperCamelCase   )
                     else:
                            __snake_case       	=							self.match_in_pattern(self.text[mismatch_index]   )
                            __snake_case       	=							(
                                mismatch_index - match_index
                            )  # shifting index lgtm [py/multiple-definition]
              return positions






UpperCamelCase__		 =     '''ABAABA'''
UpperCamelCase__		 =     '''AB'''
UpperCamelCase__		 =     BoyerMooreSearch(text, pattern)
UpperCamelCase__		 =     bms.bad_character_heuristic()

if len(positions) == 0:
    print('''No match found''')
else:
    print('''Pattern found in following positions: ''')
    print(positions)

