

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					{
    '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	List[Any]    =   '''marian'''
   lowerCamelCase__ :	Optional[int]    =   ['''past_key_values''']
   lowerCamelCase__ :	List[str]    =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



   def __init__(				self,    lowercase_=58101,    lowercase_=None,    lowercase_=1024,    lowercase_=12,    lowercase_=4096,    lowercase_=16,    lowercase_=12,    lowercase_=4096,    lowercase_=16,    lowercase_=0.0,    lowercase_=0.0,    lowercase_=True,    lowercase_=True,    lowercase_="gelu",    lowercase_=1024,    lowercase_=0.1,    lowercase_=0.0,    lowercase_=0.0,    lowercase_=0.02,    lowercase_=58100,    lowercase_=False,    lowercase_=58100,    lowercase_=0,    lowercase_=0,    lowercase_=True,    **lowercase_,    )							->    List[str]:






     """simple docstring"""






     a__  =vocab_size
     a__  =decoder_vocab_size or vocab_size
     a__  =max_position_embeddings
     a__  =d_model
     a__  =encoder_ffn_dim
     a__  =encoder_layers
     a__  =encoder_attention_heads
     a__  =decoder_ffn_dim
     a__  =decoder_layers
     a__  =decoder_attention_heads
     a__  =dropout
     a__  =attention_dropout
     a__  =activation_dropout
     a__  =activation_function
     a__  =init_std
     a__  =encoder_layerdrop
     a__  =decoder_layerdrop
     a__  =use_cache
     a__  =encoder_layers
     a__  =scale_embedding  # scale factor will be sqrt(d_model) if True
     a__  =share_encoder_decoder_embeddings
     super().__init__(
         pad_token_id=SCREAMING_SNAKE_CASE__,    eos_token_id=SCREAMING_SNAKE_CASE__,    is_encoder_decoder=SCREAMING_SNAKE_CASE__,    decoder_start_token_id=SCREAMING_SNAKE_CASE__,    forced_eos_token_id=SCREAMING_SNAKE_CASE__,    **SCREAMING_SNAKE_CASE__,    )







class 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def  _UpperCAmelCase (				self       )							->    Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]       )

       if self.use_past:
         a__  ={0: '''batch'''}
         a__  ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
       else:
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}
         a__  ={0: '''batch''', 1: '''decoder_sequence'''}

       if self.use_past:
         self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__,    direction='''inputs'''       )
     elif self.task == "causal-lm":
       # TODO: figure this case out.
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
           ]       )
       if self.use_past:
         a__  =self.num_layers
         for i in range(SCREAMING_SNAKE_CASE__       ):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     else:
       a__  =OrderedDict(
           [
               ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
           ]       )

     return common_inputs



   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def  _UpperCAmelCase (				self       )							->    Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =super().outputs
     else:
       a__  =super(SCREAMING_SNAKE_CASE__,    self       ).outputs
       if self.use_past:
         a__  =self.num_layers
         for i in range(SCREAMING_SNAKE_CASE__       ):
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
           a__  ={0: '''batch''', 2: '''past_sequence + sequence'''}
     return common_outputs



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = -1,    lowercase_ = -1,    lowercase_ = False,    lowercase_ = None,    )							->    Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_encoder_and_decoder(
         SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )

     # Generate decoder inputs
     a__  =seq_length if not self.use_past else 1
     a__  =self._generate_dummy_inputs_for_encoder_and_decoder(
         SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )
     a__  ={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     a__  =dict(**SCREAMING_SNAKE_CASE__,    **SCREAMING_SNAKE_CASE__       )

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
       else:
         import torch
       a__  =common_inputs['''input_ids'''].shape
       a__  =common_inputs['''decoder_input_ids'''].shape[1]
       a__  =self.num_attention_heads
       a__  =(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       a__  =decoder_seq_length + 3
       a__  =(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       a__  =torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )],    dim=1       )

       a__  =[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       a__  =self.num_layers
       a__  =min(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )
       a__  =max(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       ) - min_num_layers
       a__  ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

       for _ in range(SCREAMING_SNAKE_CASE__       ):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(SCREAMING_SNAKE_CASE__       ),
                 torch.zeros(SCREAMING_SNAKE_CASE__       ),
                 torch.zeros(SCREAMING_SNAKE_CASE__       ),
                 torch.zeros(SCREAMING_SNAKE_CASE__       ),
             )       )
       # TODO: test this.
       a__  =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
       for _ in range(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       ):
         common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__       ), torch.zeros(SCREAMING_SNAKE_CASE__       ))       )
     return common_inputs



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = -1,    lowercase_ = -1,    lowercase_ = False,    lowercase_ = None,    )							->    Mapping[str, Any]:






     """simple docstring"""






     a__  =self._generate_dummy_inputs_for_encoder_and_decoder(
         SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''       )
       else:
         import torch
       a__  =common_inputs['''input_ids'''].shape
       # Not using the same length for past_key_values
       a__  =seqlen + 2
       a__  =self.num_layers
       a__  =self.num_attention_heads
       a__  =(
           batch,
           num_encoder_attention_heads,
           past_key_values_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )

       a__  =common_inputs['''attention_mask'''].dtype
       a__  =torch.cat(
           [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    dtype=SCREAMING_SNAKE_CASE__       )],    dim=1       )
       a__  =[
           (torch.zeros(SCREAMING_SNAKE_CASE__       ), torch.zeros(SCREAMING_SNAKE_CASE__       )) for _ in range(SCREAMING_SNAKE_CASE__       )
       ]
     return common_inputs



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = -1,    lowercase_ = -1,    lowercase_ = False,    lowercase_ = None,    )							->    Mapping[str, Any]:






     """simple docstring"""






     a__  =compute_effective_axis_dimension(
         SCREAMING_SNAKE_CASE__,    fixed_dimension=OnnxConfig.default_fixed_batch,    num_token_to_add=0       )

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     a__  =tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__       )
     a__  =compute_effective_axis_dimension(
         SCREAMING_SNAKE_CASE__,    fixed_dimension=OnnxConfig.default_fixed_sequence,    num_token_to_add=SCREAMING_SNAKE_CASE__       )

     # Generate dummy inputs according to compute batch and sequence
     a__  =[''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
     a__  =dict(tokenizer(SCREAMING_SNAKE_CASE__,    return_tensors=SCREAMING_SNAKE_CASE__       )       )
     return common_inputs



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_ = -1,    lowercase_ = -1,    lowercase_ = False,    lowercase_ = None,    )							->    Mapping[str, Any]:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
           SCREAMING_SNAKE_CASE__,    batch_size=SCREAMING_SNAKE_CASE__,    seq_length=SCREAMING_SNAKE_CASE__,    is_pair=SCREAMING_SNAKE_CASE__,    framework=SCREAMING_SNAKE_CASE__       )

     else:
       a__  =self._generate_dummy_inputs_for_causal_lm(
           SCREAMING_SNAKE_CASE__,    batch_size=SCREAMING_SNAKE_CASE__,    seq_length=SCREAMING_SNAKE_CASE__,    is_pair=SCREAMING_SNAKE_CASE__,    framework=SCREAMING_SNAKE_CASE__       )

     return common_inputs



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    Any:






     """simple docstring"""






     if self.task in ["default", "seq2seq-lm"]:
       a__  =super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )
     else:
       a__  =super(SCREAMING_SNAKE_CASE__,    self       )._flatten_past_key_values_(
           SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       )



   @property
   def  _UpperCAmelCase (				self       )							->    float:






     """simple docstring"""






     return 1E-4







"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_								=		logging.getLogger(__name__)



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  argparse.ArgumentParser(
          description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''							)
      parser.add_argument('''--file_path''' ,   type=a_ ,   default='''data/dump.txt''' ,   help='''The path to the data.'''							)
      parser.add_argument('''--tokenizer_type''' ,   type=a_ ,   default='''bert''' ,   choices=['''bert''', '''roberta''', '''gpt2''']							)
      parser.add_argument('''--tokenizer_name''' ,   type=a_ ,   default='''bert-base-uncased''' ,   help='''The tokenizer to use.'''							)
      parser.add_argument('''--dump_file''' ,   type=a_ ,   default='''data/dump''' ,   help='''The dump file prefix.'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()

      logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''							)
      if args.tokenizer_type == "bert":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            __SCREAMING_SNAKE_CASE   :str						=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''cls_token''']  # `<s>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''sep_token''']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

      logger.info(f'''Loading text from {args.file_path}'''							)
      with open(args.file_path ,   '''r''' ,   encoding='''utf8'''							) as fp:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  fp.readlines()

      logger.info('''Start encoding'''							)
      logger.info(f'''{len(a_							)} examples to process.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[str]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1_00_00
      __SCREAMING_SNAKE_CASE   :List[Any]						=  time.time()
      for text in data:
            __SCREAMING_SNAKE_CASE   :Any						=  f'''{bos} {text.strip()} {sep}'''
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer.encode(a_ ,   add_special_tokens=a_							)
            rslt.append(a_							)

            iter += 1
            if iter % interval == 0:
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
                  logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''							)
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
      logger.info('''Finished binarization'''							)
      logger.info(f'''{len(a_							)} examples processed.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
      __SCREAMING_SNAKE_CASE   :str						=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [np.uintaa(a_							) for d in rslt]
      else:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.intaa(a_							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f'''Dump to {dp_file}'''							)
      with open(a_ ,   '''wb'''							) as handle:
            pickle.dump(rslt_ ,   a_ ,   protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
  main()