




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__a							     =					(
    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
)

__a							     =					(
    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
)

__a							     =					[
    '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__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     BigBirdPegasusConfig(**_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     BigBirdPegasusForConditionalGeneration(_lowercase )
     UpperCAmelCase_     :				Union[str, Any]  =     torch_model.state_dict()
     UpperCAmelCase_     :				Dict  =     {}

     # separating decoder weights
     UpperCAmelCase_     :				Any  =     {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder''' )}
     UpperCAmelCase_     :				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''' ):
          UpperCAmelCase_     :				Optional[Any]  =     [k.endswith(_lowercase ) for ending in KEYS_TO_IGNORE]
          if any(_lowercase ):
               continue
          UpperCAmelCase_     :				Optional[int]  =     DECODER_PATTERNS
          UpperCAmelCase_     :				Union[str, Any]  =     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_     :				int  =     v.T
          UpperCAmelCase_     :				List[Any]  =     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_     :				Optional[int]  =     [k.endswith(_lowercase ) for ending in KEYS_TO_IGNORE]
          if any(_lowercase ):
               continue
          UpperCAmelCase_     :				str  =     REMAINING_PATTERNS
          UpperCAmelCase_     :				Union[str, Any]  =     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_     :				int  =     v.T
          UpperCAmelCase_     :				Tuple  =     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_     :				Tuple  =     mapping['''model.embed_positions.weight''']
     UpperCAmelCase_     :				str  =     mapping.pop('''model.embed_positions.weight''' )
     UpperCAmelCase_,       UpperCAmelCase_     :				List[Any]  =     torch_model.load_state_dict(_lowercase					,		strict=_lowercase )
     UpperCAmelCase_     :				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__  (     _lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     get_tf_weights_as_numpy(_lowercase )
     UpperCAmelCase_     :				Any  =     convert_bigbird_pegasus(_lowercase					,		_lowercase )
     torch_model.save_pretrained(_lowercase )


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






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__							:      Tuple         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      int         =    """▁"""

SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {"""vocab_file""": """spiece.model"""}

SCREAMING_SNAKE_CASE__							:      Optional[int]         =    {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    {
    """google/reformer-crime-and-punishment""": 5_2_4_2_8_8,
}







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    List[Any]  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Any  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Any  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    Tuple  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case="</s>"       ,      snake_case="<unk>"       ,      snake_case=[]       ,      snake_case = None       ,      **snake_case       ,      )					->	None:
      """simple docstring"""





      a__						:    Optional[Any]   		=	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=snake_case       ,      unk_token=snake_case       ,      additional_special_tokens=snake_case       ,      sp_model_kwargs=self.sp_model_kwargs       ,      **snake_case       ,      )

      a__						:    Union[str, Any]   		=	vocab_file
      a__						:    Optional[Any]   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(snake_case   )





   @property
   def 						_snake_case			(			self   )					->	str:
      """simple docstring"""





      return self.sp_model.get_piece_size()





   def 						_snake_case			(			self   )					->	Dict[str, int]:
      """simple docstring"""





      a__						:    Dict   		=	{self.convert_ids_to_tokens(snake_case   ): i for i in range(self.vocab_size   )}
      vocab.update(self.added_tokens_encoder   )
      return vocab





   def __getstate__(			self   )					->	Any:
      """simple docstring"""





      a__						:    Tuple   		=	self.__dict__.copy()
      a__						:    Optional[int]   		=	None
      return state





   def __setstate__(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      a__						:    List[Any]   		=	d

      # for backward compatibility
      if not hasattr(self       ,      "sp_model_kwargs"   ):
         a__						:    List[Any]   		=	{}

      a__						:    int   		=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
      self.sp_model.Load(self.vocab_file   )





   def 						_snake_case			(			self       ,      snake_case   )					->	List[str]:
      """simple docstring"""





      return self.sp_model.encode(snake_case       ,      out_type=snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





      return self.sp_model.piece_to_id(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      if index < self.sp_model.get_piece_size():
         a__						:    Optional[Any]   		=	self.sp_model.IdToPiece(snake_case   )
      return token





   def 						_snake_case			(			self       ,      snake_case   )					->	Any:
      """simple docstring"""





      a__						:    int   		=	[]
      a__						:    Dict   		=	""
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(snake_case   ) + token
            a__						:    Dict   		=	[]
         else:
            current_sub_tokens.append(snake_case   )
      out_string += self.sp_model.decode(snake_case   )
      return out_string.strip()





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





      if not os.path.isdir(snake_case   ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
         return
      a__						:    Tuple   		=	os.path.join(
          snake_case       ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(snake_case   ) and os.path.isfile(self.vocab_file   ):
         copyfile(self.vocab_file       ,      snake_case   )
      elif not os.path.isfile(self.vocab_file   ):
         with open(snake_case       ,      "wb"   ) as fi:
            a__						:    str   		=	self.sp_model.serialized_model_proto()
            fi.write(snake_case   )

      return (out_vocab_file,)

