


"""simple docstring"""





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      _snake_case						(						_snake_case				:			Any ,   _snake_case				:			List[Any] ,   _snake_case				:			List[Any]=None			):
      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      lowerCAmelCase					:	str       =   nn.Parameter(__lowerCAmelCase			)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            lowerCAmelCase					:	List[str]       =   nn.Parameter(__lowerCAmelCase			)


def      _snake_case						(						_snake_case				:			Union[str, Any] ,   _snake_case				:			int ,   _snake_case				:			Any			):
      lowerCAmelCase					:	Union[str, Any]       =   np.asarray(weights[0]			)
      lowerCAmelCase					:	Optional[int]       =   np.asarray(weights[1]			)
      lowerCAmelCase					:	Any       =   np.asarray(weights[2]			)

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


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			Tuple ,   _snake_case				:			Tuple			):
      lowerCAmelCase					:	Union[str, Any]       =   np.asarray(weights[0]			)
      lowerCAmelCase					:	str       =   np.asarray(weights[1]			)
      lowerCAmelCase					:	Optional[int]       =   np.asarray(weights[2]			)
      lowerCAmelCase					:	Optional[Any]       =   np.asarray(weights[3]			)

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


def      _snake_case						(						_snake_case				:			Optional[Any] ,   _snake_case				:			Optional[int] ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	Dict       =   weights[0][0][0]
      lowerCAmelCase					:	int       =   np.asarray(layer_norm_a[0]			)
      lowerCAmelCase					:	Dict       =   np.asarray(layer_norm_a[1]			)
      set_param(
          torch_block.attention.layer_norm ,   torch.tensor(__lowerCAmelCase			) ,   torch.tensor(__lowerCAmelCase			) ,   )

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

      # intermediate weighs
      lowerCAmelCase					:	Tuple       =   weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__lowerCAmelCase			) == 4:
            lowerCAmelCase					:	int       =   intermediate_weights[2]

      # layernorm 2
      lowerCAmelCase					:	Any       =   np.asarray(intermediate_weights[0][0]			)
      lowerCAmelCase					:	Union[str, Any]       =   np.asarray(intermediate_weights[0][1]			)
      set_param(
          torch_block.feed_forward.layer_norm ,   torch.tensor(__lowerCAmelCase			) ,   torch.tensor(__lowerCAmelCase			) ,   )

      # intermediate dense
      lowerCAmelCase					:	Union[str, Any]       =   np.asarray(intermediate_weights[1][0]			)
      lowerCAmelCase					:	Optional[Any]       =   np.asarray(intermediate_weights[1][1]			)
      set_param(
          torch_block.feed_forward.dense.dense ,   torch.tensor(__lowerCAmelCase			).transpose(0 ,   1			).contiguous() ,   torch.tensor(__lowerCAmelCase			) ,   )

      # intermediate out
      lowerCAmelCase					:	str       =   np.asarray(intermediate_weights[4][0]			)
      lowerCAmelCase					:	List[Any]       =   np.asarray(intermediate_weights[4][1]			)
      set_param(
          torch_block.feed_forward.output.dense ,   torch.tensor(__lowerCAmelCase			).transpose(0 ,   1			).contiguous() ,   torch.tensor(__lowerCAmelCase			) ,   )


def      _snake_case						(						_snake_case				:			Tuple ,   _snake_case				:			Dict ,   _snake_case				:			Dict			):
      lowerCAmelCase					:	str       =   torch_model.reformer

      # word embeds
      lowerCAmelCase					:	List[Any]       =   np.asarray(weights[1]			)
      set_param(
          torch_model_reformer.embeddings.word_embeddings ,   torch.tensor(__lowerCAmelCase			) ,   )

      if isinstance(weights[3] ,   __lowerCAmelCase			):
            lowerCAmelCase					:	Any       =   torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights			)			):
                  lowerCAmelCase					:	Tuple       =   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'''
                  lowerCAmelCase					:	Optional[Any]       =   nn.Parameter(torch.tensor(__lowerCAmelCase			)			)

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

      # output layer norm
      lowerCAmelCase					:	List[Any]       =   np.asarray(weights[7][0]			)
      lowerCAmelCase					:	str       =   np.asarray(weights[7][1]			)
      set_param(
          torch_model_reformer.encoder.layer_norm ,   torch.tensor(__lowerCAmelCase			) ,   torch.tensor(__lowerCAmelCase			) ,   )

      # output embeddings
      lowerCAmelCase					:	Optional[Any]       =   np.asarray(weights[9][0]			)
      lowerCAmelCase					:	Optional[int]       =   np.asarray(weights[9][1]			)
      set_param(
          torch_model.lm_head.decoder ,   torch.tensor(__lowerCAmelCase			).transpose(0 ,   1			).contiguous() ,   torch.tensor(__lowerCAmelCase			) ,   )


def      _snake_case						(						_snake_case				:			List[str] ,   _snake_case				:			List[Any] ,   _snake_case				:			Optional[int]			):
      lowerCAmelCase					:	int       =   ReformerConfig.from_json_file(__lowerCAmelCase			)
      print(f'''Building PyTorch model from configuration: {config}'''			)
      lowerCAmelCase					:	Tuple       =   ReformerModelWithLMHead(__lowerCAmelCase			)

      with open(__lowerCAmelCase ,   '''rb'''			) as f:
            lowerCAmelCase					:	Optional[int]       =   pickle.load(__lowerCAmelCase			)['''weights''']

      set_model_weights_in_torch(__lowerCAmelCase ,   __lowerCAmelCase ,   config.hidden_size			)

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


if __name__ == "__main__":
 snake_case__		:     Dict       				=				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.'''
 )
 snake_case__		:     Optional[Any]       				=				parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






"""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_big_bird import BigBirdTokenizer
else:
 snake_case__		:     Optional[Any]    				=				None

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

snake_case__		:     Any    				=				{
    '''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'''
        ),
    },
}

snake_case__		:     int    				=				{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


snake_case__		:     Optional[Any]    				=				'''▁'''



class 		snake_case_(       a__			):
     __UpperCamelCase  =					VOCAB_FILES_NAMES
     __UpperCamelCase  =					PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase  =					BigBirdTokenizer
     __UpperCamelCase  =					['''input_ids''', '''attention_mask''']
     __UpperCamelCase  =					[]






     def __init__(	self  :					Union[str, Any]					,				UpperCamelCase_  :					str=None					,				UpperCamelCase_  :					Any=None					,				UpperCamelCase_  :					str="<unk>"					,				UpperCamelCase_  :					str="<s>"					,				UpperCamelCase_  :					str="</s>"					,				UpperCamelCase_  :					int="<pad>"					,				UpperCamelCase_  :					List[Any]="[SEP]"					,				UpperCamelCase_  :					Dict="[MASK]"					,				UpperCamelCase_  :					Any="[CLS]"					,				**UpperCamelCase_  :					Any					,				):
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else bos_token
           lowerCAmelCase					:	int       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else eos_token
           lowerCAmelCase					:	List[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else unk_token
           lowerCAmelCase					:	List[str]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else pad_token
           lowerCAmelCase					:	Any       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else cls_token
           lowerCAmelCase					:	Tuple       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowerCAmelCase					:	Optional[Any]       =   AddedToken(UpperCamelCase_					,				lstrip=UpperCamelCase_					,				rstrip=UpperCamelCase_				) if isinstance(UpperCamelCase_					,				UpperCamelCase_				) else mask_token

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

           lowerCAmelCase					:	Optional[int]       =   vocab_file
           lowerCAmelCase					:	Optional[int]       =   False if not self.vocab_file else True






     def      lowerCamelCase__     (	self  :					Union[str, Any]					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	str       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [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      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None					,				UpperCamelCase_  :					bool = False				):

           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(UpperCamelCase_				)) + [1]
           return [1] + ([0] * len(UpperCamelCase_				)) + [1] + ([0] * len(UpperCamelCase_				)) + [1]






     def      lowerCamelCase__     (	self  :					Dict					,				UpperCamelCase_  :					List[int]					,				UpperCamelCase_  :					Optional[List[int]] = None				):
           lowerCAmelCase					:	Tuple       =   [self.sep_token_id]
           lowerCAmelCase					:	Tuple       =   [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  :					Optional[int]					,				UpperCamelCase_  :					str					,				UpperCamelCase_  :					Optional[str] = None				):
           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(UpperCamelCase_				):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
                 return
           lowerCAmelCase					:	Optional[int]       =   os.path.join(
               UpperCamelCase_					,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)

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

           return (out_vocab_file,)


