

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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_big_bird import BigBirdTokenizer
else:
   _lowerCamelCase      :					Optional[int]					    =      None

_lowerCamelCase      :					Optional[Any]					    =      logging.get_logger(__name__)
_lowerCamelCase      :					List[Any]					    =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase      :					List[str]					    =      {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCamelCase      :					Tuple					    =      {
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}


_lowerCamelCase      :					List[str]					    =      '''▁'''





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    VOCAB_FILES_NAMES
       lowercase_						=    PRETRAINED_VOCAB_FILES_MAP
       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_						=    BigBirdTokenizer
       lowercase_						=    ["""input_ids""", """attention_mask"""]
       lowercase_						=    []

       def __init__(							self ,   lowercase__=None ,   lowercase__=None ,   lowercase__="<unk>" ,   lowercase__="<s>" ,   lowercase__="</s>" ,   lowercase__="<pad>" ,   lowercase__="[SEP]" ,   lowercase__="[MASK]" ,   lowercase__="[CLS]" ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else unk_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token

             # Mask token behave like a normal word, i.e. include the space before it
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else mask_token

             super().__init__(
                 lowercase__ ,   tokenizer_file=lowercase__ ,   bos_token=lowercase__ ,   eos_token=lowercase__ ,   unk_token=lowercase__ ,   sep_token=lowercase__ ,   pad_token=lowercase__ ,   cls_token=lowercase__ ,   mask_token=lowercase__ ,   **lowercase__ ,   )

             __A    =vocab_file
             __A    =False if not self.vocab_file else True

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]
             if token_ids_a is None:
                   return cls + token_ids_a + sep
             return cls + token_ids_a + sep + token_ids_a + sep

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   if token_ids_a is not None:
                         raise ValueError(
                             '''You should not supply a second sequence if the provided sequence of '''
                             '''ids is already formatted with special tokens for the model.'''     )
                   return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

             if token_ids_a is None:
                   return [1] + ([0] * len(lowercase__     )) + [1]
             return [1] + ([0] * len(lowercase__     )) + [1] + ([0] * len(lowercase__     )) + [1]

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = 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     ) * [0] + len(token_ids_a + sep     ) * [1]


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                       '''tokenizer.'''     )

             if not os.path.isdir(lowercase__     ):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
                   return
             __A    =os.path.join(
                 lowercase__ ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )

             if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowercase__     ):
                   copyfile(self.vocab_file ,   lowercase__     )

             return (out_vocab_file,)





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      __A    =params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__A			):
                  # Block i, layer 0 (Self Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''self_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__A     ,				__A     ,				__A     ,				__A			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      model.save_pretrained(__A			)

      # Verify that we can load the checkpoint.
      model.from_pretrained(__A			)
      print('''Done'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


