

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase   :      Optional[Any]     =     (
    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   :      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
)

lowerCAmelCase   :      Union[str, 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_     (							a    ,      a     ):

      """simple docstring"""



      for tf_name, hf_name in patterns:
            SCREAMING_SNAKE_CASE_ :     List[str]							=   k.replace(__lowerCAmelCase    ,      __lowerCAmelCase     )
      return k

def       A_     (							a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     List[str]							=   BigBirdPegasusConfig(**__lowerCAmelCase     )
      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   BigBirdPegasusForConditionalGeneration(__lowerCAmelCase     )
      SCREAMING_SNAKE_CASE_ :     Tuple							=   torch_model.state_dict()
      SCREAMING_SNAKE_CASE_ :     int							=   {}

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

      SCREAMING_SNAKE_CASE_ :     Tuple							=   mapping['model.embed_positions.weight']
      SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   mapping.pop('model.embed_positions.weight'     )
      SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_ :     int							=   torch_model.load_state_dict(__lowerCAmelCase    ,      strict=__lowerCAmelCase     )
      SCREAMING_SNAKE_CASE_ :     Dict							=   [
          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_     (							a     ):

      """simple docstring"""



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

def       A_     (							a    ,      a    ,      a     ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     Tuple							=   get_tf_weights_as_numpy(__lowerCAmelCase     )
      SCREAMING_SNAKE_CASE_ :     Tuple							=   convert_bigbird_pegasus(__lowerCAmelCase    ,      __lowerCAmelCase     )
      torch_model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
    lowerCAmelCase   :      Dict     =     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   :      Dict     =     parser.parse_args()
    lowerCAmelCase   :      int     =     {}
    convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


snake_case							:       Dict     			=      logging.get_logger(__name__)

snake_case							:       Dict     			=      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case							:       List[Any]     			=      {
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case							:       int     			=      {
    '''distilbert-base-uncased''': 5_12,
    '''distilbert-base-uncased-distilled-squad''': 5_12,
    '''distilbert-base-cased''': 5_12,
    '''distilbert-base-cased-distilled-squad''': 5_12,
    '''distilbert-base-german-cased''': 5_12,
    '''distilbert-base-multilingual-cased''': 5_12,
}


snake_case							:       Union[str, Any]     			=      {
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Any          =			VOCAB_FILES_NAMES
					UpperCAmelCase__					:    str          =			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__					:    int          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__					:    Tuple          =			PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__					:    Optional[Any]          =			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__					:    Optional[int]          =			DistilBertTokenizer
					def __init__(      self   :Dict			,__snake_case   :Dict=None			,__snake_case   :Optional[Any]=None			,__snake_case   :Optional[Any]=True			,__snake_case   :List[Any]="[UNK]"			,__snake_case   :str="[SEP]"			,__snake_case   :List[Any]="[PAD]"			,__snake_case   :Tuple="[CLS]"			,__snake_case   :Optional[int]="[MASK]"			,__snake_case   :Dict=True			,__snake_case   :Dict=None			,**__snake_case   :List[Any]			,)					->    Optional[int]:
												super().__init__(
												    __snake_case			,tokenizer_file=__snake_case			,do_lower_case=__snake_case			,unk_token=__snake_case			,sep_token=__snake_case			,pad_token=__snake_case			,cls_token=__snake_case			,mask_token=__snake_case			,tokenize_chinese_chars=__snake_case			,strip_accents=__snake_case			,**__snake_case			,)

												a__             =     json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
												if (
												    normalizer_state.get('lowercase'			,__snake_case      ) != do_lower_case
												    or normalizer_state.get('strip_accents'			,__snake_case      ) != strip_accents
												    or normalizer_state.get('handle_chinese_chars'			,__snake_case      ) != tokenize_chinese_chars
												):
																			a__             =     getattr(__snake_case			,normalizer_state.pop('type'      )      )
																			a__             =     do_lower_case
																			a__             =     strip_accents
																			a__             =     tokenize_chinese_chars
																			a__             =     normalizer_class(**__snake_case      )

												a__             =     do_lower_case
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[str]			,__snake_case   :int=None      )					->    Dict:
												a__             =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output
					def  lowerCamelCase__(      self   :List[str]			,__snake_case   :List[int]			,__snake_case   :Optional[List[int]] = None      )					->    List[int]:
												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      ) * [0] + len(token_ids_a + sep      ) * [1]



					def  lowerCamelCase__(      self   :Union[str, Any]			,__snake_case   :str			,__snake_case   :Optional[str] = None      )					->    Tuple[str]:
												a__             =     self._tokenizer.model.save(__snake_case			,name=__snake_case      )
												return tuple(__snake_case      )




