






'''simple docstring'''
import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__	=     logging.get_logger(__name__)

UpperCamelCase__	=     {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__	=     {
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

UpperCamelCase__	=     {
    'microsoft/biogpt': 1024,
}




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      int          =       set()
   lowercase_		:      str          =       word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)							)
      lowercase_		:      List[str]          =       char
   return pairs





class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				Any						    =      VOCAB_FILES_NAMES
    __lowerCamelCase:				List[str]						    =      PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase:				Union[str, Any]						    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase:				List[str]						    =      ['input_ids', 'attention_mask']




    def __init__(     self					:    Tuple					,       a					:    List[Any]					,       a					:    Optional[int]					,       a					:    int="<unk>"					,       a					:    int="<s>"					,       a					:    Optional[int]="</s>"					,       a					:    Dict="</s>"					,       a					:    str="<pad>"					,       **a					:    int					,       ):






       '''simple docstring'''






       super().__init__(
           bos_token=a					,       eos_token=a					,       sep_token=a					,       unk_token=a					,       pad_token=a					,       **a					,       )

       try:
          import sacremoses
       except ImportError:
          raise ImportError(
              "You need to install sacremoses to use BioGptTokenizer. "
              "See https://pypi.org/project/sacremoses/ for installation."     )

       lowercase_		:      List[Any]          =       "en"
       lowercase_		:      Any          =       sacremoses
       # cache of sm.MosesTokenizer instance
       lowercase_		:      List[Any]          =       {}
       lowercase_		:      List[str]          =       {}
       with open(a					,       encoding="utf-8"     ) as vocab_handle:
          lowercase_		:      List[str]          =       json.load(a     )
       lowercase_		:      Dict          =       {v: k for k, v in self.encoder.items()}
       with open(a					,       encoding="utf-8"     ) as merges_handle:
          lowercase_		:      List[str]          =       merges_handle.read().split("\n"     )[:-1]
       lowercase_		:      Tuple          =       [tuple(merge.split()[:2]     ) for merge in merges]
       lowercase_		:      Any          =       dict(zip(a					,       range(len(a     )     )     )     )
       lowercase_		:      Dict          =       {}




    @property
    def 			lowerCAmelCase__			(     self					:    Union[str, Any]     ):






       '''simple docstring'''






       return len(self.encoder     )




    def 			lowerCAmelCase__			(     self					:    str     ):






       '''simple docstring'''






       return dict(self.encoder					,       **self.added_tokens_encoder     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    Tuple					,       a					:    Dict     ):






       '''simple docstring'''






       if lang not in self.cache_moses_tokenizer:
          lowercase_		:      Tuple          =       self.sm.MosesTokenizer(lang=a     )
          lowercase_		:      Optional[Any]          =       moses_tokenizer
       return self.cache_moses_tokenizer[lang].tokenize(
           a					,       aggressive_dash_splits=a					,       return_str=a					,       escape=a     )




    def 			lowerCAmelCase__			(     self					:    Optional[Any]					,       a					:    int					,       a					:    Tuple     ):






       '''simple docstring'''






       if lang not in self.cache_moses_detokenizer:
          lowercase_		:      Optional[int]          =       self.sm.MosesDetokenizer(lang=a     )
          lowercase_		:      List[str]          =       moses_detokenizer
       return self.cache_moses_detokenizer[lang].detokenize(a     )




    def 			lowerCAmelCase__			(     self					:    List[str]					,       a					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       tuple(token[:-1]     ) + (token[-1] + "</w>",)
       if token in self.cache:
          return self.cache[token]
       lowercase_		:      Union[str, Any]          =       get_pairs(a     )

       if not pairs:
          return token + "</w>"

       while True:
          lowercase_		:      int          =       min(a					,       key=lambda a     : self.bpe_ranks.get(a					,       float("inf"     )     )     )
          if bigram not in self.bpe_ranks:
             break
          lowercase_      ,						lowercase_		:      int          =       bigram
          lowercase_		:      Dict          =       []
          lowercase_		:      int          =       0
          while i < len(a     ):
             try:
                lowercase_		:      Dict          =       word.index(a					,       a     )
             except ValueError:
                new_word.extend(word[i:]     )
                break
             else:
                new_word.extend(word[i:j]     )
                lowercase_		:      Optional[int]          =       j

             if word[i] == first and i < len(a     ) - 1 and word[i + 1] == second:
                new_word.append(first + second     )
                i += 2
             else:
                new_word.append(word[i]     )
                i += 1
          lowercase_		:      Tuple          =       tuple(a     )
          lowercase_		:      Optional[Any]          =       new_word
          if len(a     ) == 1:
             break
          else:
             lowercase_		:      Optional[Any]          =       get_pairs(a     )
       lowercase_		:      List[str]          =       " ".join(a     )
       if word == "\n  </w>":
          lowercase_		:      List[Any]          =       "\n</w>"
       lowercase_		:      Optional[int]          =       word
       return word




    def 			lowerCAmelCase__			(     self					:    Tuple					,       a					:    Tuple					,       a					:    str=False     ):






       '''simple docstring'''






       if bypass_tokenizer:
          lowercase_		:      Union[str, Any]          =       text.split()
       else:
          lowercase_		:      Any          =       self.moses_tokenize(a					,       self.lang     )

       lowercase_		:      Optional[Any]          =       []
       for token in text:
          if token:
             split_tokens.extend(list(self.bpe(a     ).split(" "     )     )     )

       return split_tokens




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    int     ):






       '''simple docstring'''






       return self.encoder.get(a					,       self.encoder.get(self.unk_token     )     )




    def 			lowerCAmelCase__			(     self					:    List[str]					,       a					:    str     ):






       '''simple docstring'''






       return self.decoder.get(a					,       self.unk_token     )




    def 			lowerCAmelCase__			(     self					:    str					,       a					:    Union[str, Any]     ):






       '''simple docstring'''






       lowercase_		:      Union[str, Any]          =       [t.replace(" "					,       ""     ).replace("</w>"					,       " "     ) for t in tokens]
       lowercase_		:      str          =       "".join(a     ).split()
       # detokenize
       lowercase_		:      Optional[Any]          =       self.moses_detokenize(a					,       self.lang     )
       return text




    def 			lowerCAmelCase__			(     self					:    List[Any]					,       a					:    List[int]					,       a					:    Optional[List[int]] = None     ):






       '''simple docstring'''






       if token_ids_a is None:
          return [self.sep_token_id] + token_ids_a
       lowercase_		:      Tuple          =       [self.sep_token_id]
       return sep + token_ids_a + sep + token_ids_a




    def 			lowerCAmelCase__			(     self					:    Dict					,       a					:    List[int]					,       a					:    Optional[List[int]] = None					,       a					:    bool = False     ):






       '''simple docstring'''






       if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=a					,       token_ids_a=a					,       already_has_special_tokens=a     )
       # no bos used in fairseq
       if token_ids_a is not None:
          return [1] + ([0] * len(a     )) + [1] + ([0] * len(a     ))
       return [1] + ([0] * len(a     ))




    def 			lowerCAmelCase__			(     self					:    int					,       a					:    List[int]					,       a					:    Optional[List[int]] = None     ):






       '''simple docstring'''






       lowercase_		:      int          =       [self.sep_token_id]

       # no bos used in fairseq
       if token_ids_a is None:
          return len(token_ids_a + sep     ) * [0]
       return len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]




    def 			lowerCAmelCase__			(     self					:    Union[str, Any]					,       a					:    str					,       a					:    Optional[str] = None     ):






       '''simple docstring'''






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

       with open(a					,       "w"					,       encoding="utf-8"     ) as f:
          f.write(json.dumps(self.encoder					,       indent=2					,       sort_keys=a					,       ensure_ascii=a     ) + "\n"     )

       lowercase_		:      int          =       0
       with open(a					,       "w"					,       encoding="utf-8"     ) as writer:
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()					,       key=lambda a     : kv[1]     ):
             if index != token_index:
                logger.warning(
                    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                    " Please check that the tokenizer is not corrupted!"     )
                lowercase_		:      Optional[Any]          =       token_index
             writer.write(" ".join(a     ) + "\n"     )
             index += 1

       return vocab_file, merge_file




    def __getstate__(     self					:    Tuple     ):






       '''simple docstring'''






       lowercase_		:      Dict          =       self.__dict__.copy()
       lowercase_		:      Union[str, Any]          =       None
       return state





    def __setstate__(     self					:    str					,       a					:    int     ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       d

       try:
          import sacremoses
       except ImportError:
          raise ImportError(
              "You need to install sacremoses to use XLMTokenizer. "
              "See https://pypi.org/project/sacremoses/ for installation."     )

       lowercase_		:      List[Any]          =       sacremoses












'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


UpperCamelCase__	=     logging.get_logger(__name__)

UpperCamelCase__	=     {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}


class     _UpperCAmelCase  (						snake_case     ):
    __lowerCamelCase:				List[Any]						    =      'm2m_100'
    __lowerCamelCase:				Union[str, Any]						    =      ['past_key_values']
    __lowerCamelCase:				Union[str, Any]						    =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




    def __init__(     self					:    str					,       a					:    Optional[Any]=1_2_8_1_1_2					,       a					:    Optional[int]=1_0_2_4					,       a					:    Any=1_2					,       a					:    Tuple=4_0_9_6					,       a					:    Any=1_6					,       a					:    Any=1_2					,       a					:    Dict=4_0_9_6					,       a					:    List[str]=1_6					,       a					:    List[Any]=0.05					,       a					:    Optional[int]=0.05					,       a					:    List[Any]=True					,       a					:    List[Any]=True					,       a					:    Union[str, Any]="relu"					,       a					:    Optional[Any]=1_0_2_4					,       a					:    List[str]=0.1					,       a					:    Union[str, Any]=0.1					,       a					:    int=0.0					,       a					:    List[str]=0.02					,       a					:    Optional[int]=2					,       a					:    Tuple=True					,       a					:    List[str]=1					,       a					:    int=0					,       a					:    int=2					,       **a					:    Tuple					,       ):






       '''simple docstring'''






       lowercase_		:      List[str]          =       vocab_size
       lowercase_		:      Tuple          =       max_position_embeddings
       lowercase_		:      int          =       d_model
       lowercase_		:      Optional[int]          =       encoder_ffn_dim
       lowercase_		:      Any          =       encoder_layers
       lowercase_		:      Optional[Any]          =       encoder_attention_heads
       lowercase_		:      List[Any]          =       decoder_ffn_dim
       lowercase_		:      Union[str, Any]          =       decoder_layers
       lowercase_		:      Optional[Any]          =       decoder_attention_heads
       lowercase_		:      Optional[int]          =       dropout
       lowercase_		:      Optional[Any]          =       attention_dropout
       lowercase_		:      Optional[int]          =       activation_dropout
       lowercase_		:      Any          =       activation_function
       lowercase_		:      Optional[Any]          =       init_std
       lowercase_		:      Tuple          =       encoder_layerdrop
       lowercase_		:      Optional[Any]          =       decoder_layerdrop
       lowercase_		:      List[Any]          =       use_cache
       lowercase_		:      Dict          =       encoder_layers
       lowercase_		:      Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=a					,       bos_token_id=a					,       eos_token_id=a					,       is_encoder_decoder=a					,       decoder_start_token_id=a					,       **a					,       )


class     _UpperCAmelCase  (						snake_case     ):




    @property
    def 			lowerCAmelCase__			(     self					:    Optional[Any]     ):






       '''simple docstring'''






       lowercase_		:      List[Any]          =       OrderedDict(
           [
               ("input_ids", {0: "batch", 1: "encoder_sequence"}),
               ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
           ]     )

       if self.use_past:
          lowercase_		:      Optional[int]          =       {0: "batch"}
          lowercase_		:      str          =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
          lowercase_		:      Optional[Any]          =       {0: "batch", 1: "decoder_sequence"}
          lowercase_		:      Union[str, Any]          =       {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
          self.fill_with_past_key_values_(a					,       direction="inputs"     )
       return common_inputs




    def 			lowerCAmelCase__			(     self					:    Dict					,       a					:    PreTrainedTokenizer					,       a					:    int = -1					,       a					:    int = -1					,       a					:    bool = False					,       a					:    Optional[TensorType] = None					,       ):






       '''simple docstring'''






       lowercase_		:      int          =       compute_effective_axis_dimension(
           a					,       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
       lowercase_		:      Dict          =       tokenizer.num_special_tokens_to_add(a     )
       lowercase_		:      str          =       compute_effective_axis_dimension(
           a					,       fixed_dimension=OnnxConfig.default_fixed_sequence					,       num_token_to_add=a     )

       # Generate dummy inputs according to compute batch and sequence
       lowercase_		:      Union[str, Any]          =       [" ".join([tokenizer.unk_token]     ) * seq_length] * batch_size
       lowercase_		:      Tuple          =       dict(tokenizer(a					,       return_tensors=a     )     )
       return common_inputs




    def 			lowerCAmelCase__			(     self					:    Any					,       a					:    PreTrainedTokenizer					,       a					:    int = -1					,       a					:    int = -1					,       a					:    bool = False					,       a					:    Optional[TensorType] = None					,       ):






       '''simple docstring'''






       lowercase_		:      Optional[Any]          =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a					,       a					,       a					,       a					,       a     )

       # Generate decoder inputs
       lowercase_		:      List[str]          =       seq_length if not self.use_past else 1
       lowercase_		:      Dict          =       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           a					,       a					,       a					,       a					,       a     )
       lowercase_		:      Union[str, Any]          =       {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       lowercase_		:      Union[str, Any]          =       dict(**a					,       **a     )

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

          lowercase_		:      Dict          =       torch.cat(
              [common_inputs["decoder_attention_mask"], torch.ones(a					,       a     )]					,       dim=1     )

          lowercase_		:      Tuple          =       []
          # If the number of encoder and decoder layers are present in the model configuration, both are considered
          lowercase_      ,						lowercase_		:      Tuple          =       self.num_layers
          lowercase_		:      Optional[Any]          =       min(a					,       a     )
          lowercase_		:      Tuple          =       max(a					,       a     ) - min_num_layers
          lowercase_		:      Optional[int]          =       "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

          for _ in range(a     ):
             common_inputs["past_key_values"].append(
                 (
                     torch.zeros(a     ),
                     torch.zeros(a     ),
                     torch.zeros(a     ),
                     torch.zeros(a     ),
                 )     )
          # TODO: test this.
          lowercase_		:      Union[str, Any]          =       encoder_shape if remaining_side_name == "encoder" else decoder_shape
          for _ in range(a					,       a     ):
             common_inputs["past_key_values"].append((torch.zeros(a     ), torch.zeros(a     ))     )
       return common_inputs





    __lowerCamelCase:				str						    =      _generate_dummy_inputs_for_default_and_seqaseq_lm




