


'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all MVP models at https://huggingface.co/models?filter=mvp
SCREAMING_SNAKE_CASE_      =			{
    'vocab_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json',
    },
    'added_tokens.json': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json',
    },
    'merges_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_      =			{
    'RUCAIBox/mvp': 10_24,
}


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :List[Any]												=VOCAB_FILES_NAMES
  a_       :Union[str, Any]												=PRETRAINED_VOCAB_FILES_MAP
  a_       :Tuple												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_       :str												=["""input_ids""", """attention_mask"""]
  a_       :List[str]												=MvpTokenizer

  def __init__(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							int=None				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=None				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None				,							SCREAMING_SNAKE_CASE__     :							Optional[int]="replace"				,							SCREAMING_SNAKE_CASE__     :							int="<s>"				,							SCREAMING_SNAKE_CASE__     :							Dict="</s>"				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]="</s>"				,							SCREAMING_SNAKE_CASE__     :							int="<s>"				,							SCREAMING_SNAKE_CASE__     :							int="<unk>"				,							SCREAMING_SNAKE_CASE__     :							List[str]="<pad>"				,							SCREAMING_SNAKE_CASE__     :							List[str]="<mask>"				,							SCREAMING_SNAKE_CASE__     :							Tuple=False				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=True				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





       '''simple docstring'''

       super().__init__(
           SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__				,							tokenizer_file=SCREAMING_SNAKE_CASE__				,							errors=SCREAMING_SNAKE_CASE__				,							bos_token=SCREAMING_SNAKE_CASE__				,							eos_token=SCREAMING_SNAKE_CASE__				,							sep_token=SCREAMING_SNAKE_CASE__				,							cls_token=SCREAMING_SNAKE_CASE__				,							unk_token=SCREAMING_SNAKE_CASE__				,							pad_token=SCREAMING_SNAKE_CASE__				,							mask_token=SCREAMING_SNAKE_CASE__				,							add_prefix_space=SCREAMING_SNAKE_CASE__				,							trim_offsets=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__				,							)

       __a       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
       if pre_tok_state.get("""add_prefix_space"""				,							SCREAMING_SNAKE_CASE__						) != add_prefix_space:
            __a       =   getattr(SCREAMING_SNAKE_CASE__				,							pre_tok_state.pop("""type"""						)						)
            __a       =   add_prefix_space
            __a       =   pre_tok_class(**SCREAMING_SNAKE_CASE__						)

       __a       =   add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       __a       =   """post_processor"""
       __a       =   getattr(self.backend_tokenizer				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)
       if tokenizer_component_instance:
            __a       =   json.loads(tokenizer_component_instance.__getstate__()						)

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                 __a       =   tuple(state["""sep"""]						)
            if "cls" in state:
                 __a       =   tuple(state["""cls"""]						)

            __a       =   False

            if state.get("""add_prefix_space"""				,							SCREAMING_SNAKE_CASE__						) != add_prefix_space:
                 __a       =   add_prefix_space
                 __a       =   True

            if state.get("""trim_offsets"""				,							SCREAMING_SNAKE_CASE__						) != trim_offsets:
                 __a       =   trim_offsets
                 __a       =   True

            if changes_to_apply:
                 __a       =   getattr(SCREAMING_SNAKE_CASE__				,							state.pop("""type"""						)						)
                 __a       =   component_class(**SCREAMING_SNAKE_CASE__						)
                 setattr(self.backend_tokenizer				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						)

  @property
  def __a						(					self     :							str						):





       '''simple docstring'''

       if self._mask_token is None:
            if self.verbose:
                 logger.error("""Using mask_token, but it is not set yet."""						)
            return None
       return str(self._mask_token						)

  @mask_token.setter
  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       __a       =   AddedToken(SCREAMING_SNAKE_CASE__				,							lstrip=SCREAMING_SNAKE_CASE__				,							rstrip=SCREAMING_SNAKE_CASE__						) if isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__						) else value
       __a       =   value

  def __a						(					self     :							Any				,							*SCREAMING_SNAKE_CASE__     :							str				,							**SCREAMING_SNAKE_CASE__     :							Any						):





       '''simple docstring'''

       __a       =   kwargs.get("""is_split_into_words"""				,							SCREAMING_SNAKE_CASE__						)

       if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                """to use it with pretokenized inputs."""						)

       return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							str				,							*SCREAMING_SNAKE_CASE__     :							List[Any]				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       __a       =   kwargs.get("""is_split_into_words"""				,							SCREAMING_SNAKE_CASE__						)

       if is_split_into_words and not self.add_prefix_space:
            raise ValueError(
                f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                """to use it with pretokenized inputs."""						)

       return super()._encode_plus(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = None						):





       '''simple docstring'''

       __a       =   self._tokenizer.model.save(SCREAMING_SNAKE_CASE__				,							name=SCREAMING_SNAKE_CASE__						)
       return tuple(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							List[Any]=None						):





       '''simple docstring'''

       __a       =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
            return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None						):





       '''simple docstring'''

       __a       =   [self.sep_token_id]
       __a       =   [self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]










'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE_      =			(
    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_      =			(
    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_      =			[
    '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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     for tf_name, hf_name in patterns:
          __a       =   k.replace(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     return k




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					BigBirdPegasusForConditionalGeneration:
     """simple docstring"""




     __a       =   BigBirdPegasusConfig(**__SCREAMING_SNAKE_CASE		)
     __a       =   BigBirdPegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE		)
     __a       =   torch_model.state_dict()
     __a       =   {}

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

     __a       =   mapping["""model.embed_positions.weight"""]
     __a       =   mapping.pop("""model.embed_positions.weight"""		)
     __a ,						__a       =   torch_model.load_state_dict(__SCREAMING_SNAKE_CASE				,  strict=__SCREAMING_SNAKE_CASE		)
     __a       =   [
         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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     __a       =   get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE		)
     __a       =   convert_bigbird_pegasus(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     torch_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


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






