



'''simple docstring'''





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_albert import AlbertTokenizer
else:
 SCREAMING_SNAKE_CASE__          =  None

SCREAMING_SNAKE_CASE__          =  logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__          =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__          =  {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__          =  {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

SCREAMING_SNAKE_CASE__          =  '''▁'''


class 					snake_case			(UpperCamelCase ):
       lowerCAmelCase__	:Dict     =       VOCAB_FILES_NAMES
       lowerCAmelCase__	:Union[str, Any]     =       PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase__	:Dict     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase__	:Optional[int]     =       AlbertTokenizer



       def __init__(					self  ,UpperCAmelCase_=None  ,UpperCAmelCase_=None  ,UpperCAmelCase_=True  ,UpperCAmelCase_=True  ,UpperCAmelCase_=False  ,UpperCAmelCase_="[CLS]"  ,UpperCAmelCase_="[SEP]"  ,UpperCAmelCase_="<unk>"  ,UpperCAmelCase_="[SEP]"  ,UpperCAmelCase_="<pad>"  ,UpperCAmelCase_="[CLS]"  ,UpperCAmelCase_="[MASK]"  ,**UpperCAmelCase_  ,)	->				int:
           lowercase__									=    (
               AddedToken(lowercase__  ,lstrip=lowercase__  ,rstrip=lowercase__  ,normalized=lowercase__   )
               if isinstance(lowercase__  ,lowercase__   )
               else mask_token
           )

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

           lowercase__									=    do_lower_case
           lowercase__									=    remove_space
           lowercase__									=    keep_accents
           lowercase__									=    vocab_file
           lowercase__									=    False if not self.vocab_file else True



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				Optional[int]:
           lowercase__									=    [self.sep_token_id]
           lowercase__									=    [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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				List[Any]:
           lowercase__									=    [self.sep_token_id]
           lowercase__									=    [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 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_ = None   )	->				Tuple:



           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
           lowercase__									=    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 pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						Any     ,				__A  :						Dict     ,				__A  :						Optional[int]=None			)					->Tuple:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
      __A    =nn.Parameter(__A			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
            __A    =nn.Parameter(__A			)





def      A__					(			__A  :						List[Any]     ,				__A  :						Tuple     ,				__A  :						List[Any]			)					->Dict:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)

      set_param(
          torch_layer.self_attention.query_key     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.value     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.output.dense     ,				torch.tensor(__A			).view(-1     ,				__A			).contiguous().transpose(0     ,				1			)     ,				)





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Tuple     ,				__A  :						Optional[Any]			)					->int:
      # set torch weights for 1-to-1 comparison
      __A    =np.asarray(weights[0]			)
      __A    =np.asarray(weights[1]			)
      __A    =np.asarray(weights[2]			)
      __A    =np.asarray(weights[3]			)

      set_param(
          torch_layer.self_attention.query     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.key     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.self_attention.value     ,				torch.tensor(__A			).transpose(1     ,				2			).contiguous().view(-1     ,				__A			)     ,				)
      set_param(
          torch_layer.output.dense     ,				torch.tensor(__A			).view(-1     ,				__A			).contiguous().transpose(0     ,				1			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						List[str]     ,				__A  :						Optional[Any]			)					->Any:
      # layernorm 1
      __A    =weights[0][0][0]
      __A    =np.asarray(layer_norm_a[0]			)
      __A    =np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # lsh weights + output
      __A    =weights[0][1]
      if len(__A			) < 4:
            set_layer_weights_in_torch_lsh(__A     ,				torch_block.attention     ,				__A			)
      else:
            set_layer_weights_in_torch_local(__A     ,				torch_block.attention     ,				__A			)

      # intermediate weighs
      __A    =weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__A			) == 4:
            __A    =intermediate_weights[2]

      # layernorm 2
      __A    =np.asarray(intermediate_weights[0][0]			)
      __A    =np.asarray(intermediate_weights[0][1]			)
      set_param(
          torch_block.feed_forward.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # intermediate dense
      __A    =np.asarray(intermediate_weights[1][0]			)
      __A    =np.asarray(intermediate_weights[1][1]			)
      set_param(
          torch_block.feed_forward.dense.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)

      # intermediate out
      __A    =np.asarray(intermediate_weights[4][0]			)
      __A    =np.asarray(intermediate_weights[4][1]			)
      set_param(
          torch_block.feed_forward.output.dense     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						Tuple     ,				__A  :						List[Any]     ,				__A  :						Optional[int]			)					->List[Any]:
      # reformer model
      __A    =torch_model.reformer

      # word embeds
      __A    =np.asarray(weights[1]			)
      set_param(
          torch_model_reformer.embeddings.word_embeddings     ,				torch.tensor(__A			)     ,				)

      if isinstance(weights[3]     ,				__A			):
            __A    =torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  __A    =np.asarray(weights[3][emb_idx][0]			)
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), F'''{position_embeddings[emb_idx]} emb does not match'''
                  __A    =nn.Parameter(torch.tensor(__A			)			)

      __A    =weights[5]
      assert len(torch_model_reformer.encoder.layers			) * 4 == len(
          __A			), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers			):
            __A    =trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(__A     ,				__A     ,				__A			)

      # output layer norm
      __A    =np.asarray(weights[7][0]			)
      __A    =np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm     ,				torch.tensor(__A			)     ,				torch.tensor(__A			)     ,				)

      # output embeddings
      __A    =np.asarray(weights[9][0]			)
      __A    =np.asarray(weights[9][1]			)
      set_param(
          torch_model.lm_head.decoder     ,				torch.tensor(__A			).transpose(0     ,				1			).contiguous()     ,				torch.tensor(__A			)     ,				)





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Tuple			)					->Union[str, Any]:
      # Initialise PyTorch model
      __A    =ReformerConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      __A    =ReformerModelWithLMHead(__A			)

      with open(__A     ,				'''rb'''			) as f:
            __A    =pickle.load(__A			)['''weights''']

      set_model_weights_in_torch(__A     ,				__A     ,				config.hidden_size			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      torch.save(model.state_dict()     ,				__A			)


if __name__ == "__main__":
   _lowerCamelCase      :					Union[str, Any]					    =      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained Reformer model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   _lowerCamelCase      :					Tuple					    =      parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)


