


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE     :			List[Any]        =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :			Optional[Any]        =							'▁'

_SCREAMING_SNAKE_CASE     :			Tuple        =							{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_SCREAMING_SNAKE_CASE     :			Tuple        =							{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

_SCREAMING_SNAKE_CASE     :			Optional[int]        =							{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
_SCREAMING_SNAKE_CASE     :			List[Any]        =							{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}



class        A     (		_a	):







     '''simple docstring'''







     lowerCamelCase :						str            =   VOCAB_FILES_NAMES
     lowerCamelCase :						Dict            =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase :						int            =   PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase :						Union[str, Any]            =   ["""input_ids""", """attention_mask"""]

     lowerCamelCase :						List[int]            =   []
     lowerCamelCase :						List[int]            =   []

     def __init__(				self    :							Optional[Any]			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Dict			,				_UpperCamelCase    :							str=None			,				_UpperCamelCase    :							Dict=None			,				_UpperCamelCase    :							str="<s>"			,				_UpperCamelCase    :							Any="</s>"			,				_UpperCamelCase    :							List[Any]="</s>"			,				_UpperCamelCase    :							List[str]="<pad>"			,				_UpperCamelCase    :							Optional[int]="<unk>"			,				_UpperCamelCase    :							str="m2m100"			,				_UpperCamelCase    :							Optional[Dict[str, Any]] = None			,				_UpperCamelCase    :							Tuple=8			,				**_UpperCamelCase    :							Dict			,				):
      _lowercase:       Optional[Any]						=				{} if sp_model_kwargs is None else sp_model_kwargs

      _lowercase:       Optional[Any]						=				language_codes
      _lowercase:       Dict						=				FAIRSEQ_LANGUAGE_CODES[language_codes]
      _lowercase:       Tuple						=				{lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}

      _lowercase:       Tuple						=				kwargs.get("additional_special_tokens"			,				[])
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(A_)
          for lang_code in fairseq_language_code
          if self.get_lang_token(A_) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=A_			,				tgt_lang=A_			,				bos_token=A_			,				eos_token=A_			,				sep_token=A_			,				unk_token=A_			,				pad_token=A_			,				language_codes=A_			,				sp_model_kwargs=self.sp_model_kwargs			,				num_madeup_words=A_			,				**A_			,				)

      _lowercase:       Tuple						=				vocab_file
      _lowercase:       Optional[Any]						=				load_json(A_)
      _lowercase:       Union[str, Any]						=				{v: k for k, v in self.encoder.items()}
      _lowercase:       Optional[int]						=				spm_file
      _lowercase:       List[str]						=				load_spm(A_			,				self.sp_model_kwargs)

      _lowercase:       Optional[int]						=				len(self.encoder)

      _lowercase:       Tuple						=				{
          self.get_lang_token(A_): self.encoder_size + i for i, lang_code in enumerate(A_)
      }
      _lowercase:       Tuple						=				{lang_code: self.encoder_size + i for i, lang_code in enumerate(A_)}
      _lowercase:       List[str]						=				{v: k for k, v in self.lang_token_to_id.items()}

      _lowercase:       str						=				src_lang if src_lang is not None else "en"
      _lowercase:       Dict						=				tgt_lang
      _lowercase:       Dict						=				self.get_lang_id(self._src_lang)
      self.set_src_lang_special_tokens(self._src_lang)

      _lowercase:       Optional[int]						=				num_madeup_words

     @property
     def 					UpperCAmelCase__       (				self    :							Union[str, Any]):
      return len(self.encoder) + len(self.lang_token_to_id)

     @property
     def 					UpperCAmelCase__       (				self    :							Dict):
      return self._src_lang

     @src_lang.setter
     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							str):
      _lowercase:       Dict						=				new_src_lang
      self.set_src_lang_special_tokens(self._src_lang)

     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							str):
      return self.sp_model.encode(A_			,				out_type=A_)

     def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							str):


      if token in self.lang_token_to_id:
       return self.lang_token_to_id[token]
      return self.encoder.get(A_			,				self.encoder[self.unk_token])

     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							int):


      if index in self.id_to_lang_token:
       return self.id_to_lang_token[index]
      return self.decoder.get(A_			,				self.unk_token)

     def 					UpperCAmelCase__       (				self    :							Tuple			,				_UpperCamelCase    :							Union[str, Any]):
      _lowercase:       Dict						=				[]
      _lowercase:       Tuple						=				""
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        out_string += self.sp_model.decode(A_) + token
        _lowercase:       int						=				[]
       else:
        current_sub_tokens.append(A_)
      out_string += self.sp_model.decode(A_)
      return out_string.strip()

     def 					UpperCAmelCase__       (				self    :							List[str]			,				_UpperCamelCase    :							List[int]			,				_UpperCamelCase    :							Optional[List[int]] = None			,				_UpperCamelCase    :							bool = False):


      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=A_			,				token_ids_a=A_			,				already_has_special_tokens=A_)

      _lowercase:       str						=				[1] * len(self.prefix_tokens)
      _lowercase:       Optional[int]						=				[1] * len(self.suffix_tokens)
      if token_ids_a is None:
       return prefix_ones + ([0] * len(A_)) + suffix_ones
      return prefix_ones + ([0] * len(A_)) + ([0] * len(A_)) + suffix_ones

     def 					UpperCAmelCase__       (				self    :							Any			,				_UpperCamelCase    :							List[int]			,				_UpperCamelCase    :							Optional[List[int]] = None):


      if token_ids_a is None:
       return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

     def 					UpperCAmelCase__       (				self    :							List[str]):
      _lowercase:       Tuple						=				{self.convert_ids_to_tokens(A_): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

     def __getstate__(				self    :							List[str]):
      _lowercase:       Dict						=				self.__dict__.copy()
      _lowercase:       List[str]						=				None
      return state

     def __setstate__(				self    :							Optional[Any]			,				_UpperCamelCase    :							Dict):
      _lowercase:       Union[str, Any]						=				d

      # for backward compatibility
      if not hasattr(self			,				"sp_model_kwargs"):
       _lowercase:       int						=				{}

      _lowercase:       Optional[Any]						=				load_spm(self.spm_file			,				self.sp_model_kwargs)

     def 					UpperCAmelCase__       (				self    :							Tuple			,				_UpperCamelCase    :							str			,				_UpperCamelCase    :							Optional[str] = None):
      _lowercase:       str						=				Path(A_)
      if not save_dir.is_dir():
       raise OSError(f"{save_directory} should be a directory")
      _lowercase:       List[Any]						=				save_dir / (
          (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
      )
      _lowercase:       Any						=				save_dir / (
          (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
      )

      save_json(self.encoder			,				A_)

      if os.path.abspath(self.spm_file) != os.path.abspath(A_) and os.path.isfile(self.spm_file):
       copyfile(self.spm_file			,				A_)
      elif not os.path.isfile(self.spm_file):
       with open(A_			,				"wb") as fi:
        _lowercase:       List[Any]						=				self.sp_model.serialized_model_proto()
        fi.write(A_)

      return (str(A_), str(A_))

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							List[str]			,				_UpperCamelCase    :							str = "en"			,				_UpperCamelCase    :							Optional[List[str]] = None			,				_UpperCamelCase    :							str = "ro"			,				**_UpperCamelCase    :							List[Any]			,				):
      _lowercase:       Union[str, Any]						=				src_lang
      _lowercase:       Any						=				tgt_lang
      self.set_src_lang_special_tokens(self.src_lang)
      return super().prepare_seqaseq_batch(A_			,				A_			,				**A_)

     def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							List[Any]			,				_UpperCamelCase    :							Optional[str]			,				_UpperCamelCase    :							Optional[str]			,				**_UpperCamelCase    :							Optional[Any]):


      if src_lang is None or tgt_lang is None:
       raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
      _lowercase:       Union[str, Any]						=				src_lang
      _lowercase:       Any						=				self(A_			,				add_special_tokens=A_			,				**A_)
      _lowercase:       str						=				self.get_lang_id(A_)
      _lowercase:       Optional[int]						=				tgt_lang_id
      return inputs

     def 					UpperCAmelCase__       (				self    :							List[str]):
      self.set_src_lang_special_tokens(self.src_lang)

     def 					UpperCAmelCase__       (				self    :							Optional[int]):
      self.set_tgt_lang_special_tokens(self.tgt_lang)

     def 					UpperCAmelCase__       (				self    :							Optional[Any]			,				_UpperCamelCase    :							str):
      _lowercase:       Dict						=				self.get_lang_token(A_)
      _lowercase:       List[str]						=				self.lang_token_to_id[lang_token]
      _lowercase:       List[Any]						=				[self.cur_lang_id]
      _lowercase:       Tuple						=				[self.eos_token_id]

     def 					UpperCAmelCase__       (				self    :							Optional[int]			,				_UpperCamelCase    :							str):
      _lowercase:       List[Any]						=				self.get_lang_token(A_)
      _lowercase:       Tuple						=				self.lang_token_to_id[lang_token]
      _lowercase:       str						=				[self.cur_lang_id]
      _lowercase:       Union[str, Any]						=				[self.eos_token_id]

     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							str):
      return self.lang_code_to_token[lang]






     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							str):
      _lowercase:       Optional[Any]						=				self.get_lang_token(A_)
      return self.lang_token_to_id[lang_token]






def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):
 _lowercase:       Optional[int]						=				sentencepiece.SentencePieceProcessor(**UpperCamelCase__      )
 spm.Load(str(UpperCamelCase__      )      )
 return spm


def 							__lowerCAmelCase      (			__magic_name__      ):







 with open(UpperCamelCase__ ,   "r"      ) as f:
  return json.load(UpperCamelCase__      )





def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):







 with open(UpperCamelCase__ ,   "w"      ) as f:
  json.dump(UpperCamelCase__ ,   UpperCamelCase__ ,   indent=2      )





"""simple docstring"""

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase__					=logging.get_logger(__name__)

UpperCAmelCase__					={
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}





class  lowerCamelCase__    (		_a     ):
      a			:  Tuple           =		"""gptj"""
      a			:  Union[str, Any]           =		{
          """max_position_embeddings""": """n_positions""",
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }
      def __init__(		self						:						int   ,							A_						:						Optional[int]=5_0_4_0_0   ,							A_						:						Optional[Any]=2_0_4_8   ,							A_						:						Optional[Any]=4_0_9_6   ,							A_						:						Any=2_8   ,							A_						:						Union[str, Any]=1_6   ,							A_						:						int=6_4   ,							A_						:						int=None   ,							A_						:						str="gelu_new"   ,							A_						:						str=0.0   ,							A_						:						Optional[Any]=0.0   ,							A_						:						Dict=0.0   ,							A_						:						Dict=1e-5   ,							A_						:						Optional[int]=0.02   ,							A_						:						List[str]=True   ,							A_						:						List[Any]=5_0_2_5_6   ,							A_						:						Optional[int]=5_0_2_5_6   ,							A_						:						List[Any]=False   ,							**A_						:						int   ,							):






       '''simple docstring'''


       __lowercase						=						vocab_size
       __lowercase						=						n_positions
       __lowercase						=						n_embd
       __lowercase						=						n_layer
       __lowercase						=						n_head
       __lowercase						=						n_inner
       __lowercase						=						rotary_dim
       __lowercase						=						activation_function
       __lowercase						=						resid_pdrop
       __lowercase						=						embd_pdrop
       __lowercase						=						attn_pdrop
       __lowercase						=						layer_norm_epsilon
       __lowercase						=						initializer_range
       __lowercase						=						use_cache

       __lowercase						=						bos_token_id
       __lowercase						=						eos_token_id

       super().__init__(
           bos_token_id=A_   ,							eos_token_id=A_   ,							tie_word_embeddings=A_   ,							**A_				)






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						PretrainedConfig   ,							A_						:						str = "default"   ,							A_						:						List[PatchingSpec] = None   ,							A_						:						bool = False   ,							):






       '''simple docstring'''


       super().__init__(A_   ,							task=A_   ,							patching_specs=A_   ,							use_past=A_				)
       if not getattr(self._config   ,							"""pad_token_id"""   ,							A_				):
        # TODO: how to do that better?
        __lowercase						=						0
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
       if self.use_past:
        self.fill_with_past_key_values_(A_   ,							direction="""inputs"""				)
        __lowercase						=						{0: """batch""", 1: """past_sequence + sequence"""}
       else:
        __lowercase						=						{0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       return self._config.n_layer
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return self._config.n_head
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						PreTrainedTokenizer   ,							A_						:						int = -1   ,							A_						:						int = -1   ,							A_						:						bool = False   ,							A_						:						Optional[TensorType] = None   ,							):






       '''simple docstring'''


       __lowercase						=						super(A_   ,							self				).generate_dummy_inputs(
           A_   ,							batch_size=A_   ,							seq_length=A_   ,							is_pair=A_   ,							framework=A_				)

       # We need to order the input in the way they appears in the forward()
       __lowercase						=						OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}				)

       # Need to add the past_keys
       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						=						common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         __lowercase						=						seqlen + 2
         __lowercase						=						(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __lowercase						=						[
             (torch.zeros(A_				), torch.zeros(A_				)) for _ in range(self.num_layers				)
         ]

       __lowercase						=						common_inputs["""attention_mask"""]
       if self.use_past:
        __lowercase						=						ordered_inputs["""attention_mask"""].dtype
        __lowercase						=						torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(A_   ,							A_   ,							dtype=A_				)]   ,							dim=1				)

       return ordered_inputs






      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       return 1_3
