

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  :						Dict         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    '''▁'''

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

_SCREAMING_SNAKE_CASE  :						List[Any]         =    {
    '''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  :						Tuple         =    {
    '''facebook/m2m100_418M''': 1024,
}

# fmt: off
_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''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  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      VOCAB_FILES_NAMES
  a     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     =      PRETRAINED_VOCAB_FILES_MAP
  a     =      ["input_ids", "attention_mask"]

  a     =      []
  a     =      []






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

       SCREAMING_SNAKE_CASE__					=					language_codes
       SCREAMING_SNAKE_CASE__					=					FAIRSEQ_LANGUAGE_CODES[language_codes]
       SCREAMING_SNAKE_CASE__					=					{lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

       SCREAMING_SNAKE_CASE__					=					kwargs.get('''additional_special_tokens'''			,     []      )
       kwargs["additional_special_tokens"] += [
           self.get_lang_token(__lowerCamelCase      )
           for lang_code in fairseq_language_code
           if self.get_lang_token(__lowerCamelCase      ) not in kwargs["additional_special_tokens"]
       ]

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

       SCREAMING_SNAKE_CASE__					=					vocab_file
       SCREAMING_SNAKE_CASE__					=					load_json(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{v: k for k, v in self.encoder.items()}
       SCREAMING_SNAKE_CASE__					=					spm_file
       SCREAMING_SNAKE_CASE__					=					load_spm(__lowerCamelCase			,     self.sp_model_kwargs      )

       SCREAMING_SNAKE_CASE__					=					len(self.encoder      )

       SCREAMING_SNAKE_CASE__					=					{
           self.get_lang_token(__lowerCamelCase      ): self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase      )
       }
       SCREAMING_SNAKE_CASE__					=					{lang_code: self.encoder_size + i for i, lang_code in enumerate(__lowerCamelCase      )}
       SCREAMING_SNAKE_CASE__					=					{v: k for k, v in self.lang_token_to_id.items()}

       SCREAMING_SNAKE_CASE__					=					src_lang if src_lang is not None else '''en'''
       SCREAMING_SNAKE_CASE__					=					tgt_lang
       SCREAMING_SNAKE_CASE__					=					self.get_lang_id(self._src_lang      )
       self.set_src_lang_special_tokens(self._src_lang      )

       SCREAMING_SNAKE_CASE__					=					num_madeup_words






  @property
  def    lowercase_	(							self		:	Any      )	->							int:
       return len(self.encoder      ) + len(self.lang_token_to_id      )






  @property
  def    lowercase_	(							self		:	Tuple      )	->							str:
       return self._src_lang






  @src_lang.setter
  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	str      )	->							None:
       SCREAMING_SNAKE_CASE__					=					new_src_lang
       self.set_src_lang_special_tokens(self._src_lang      )






  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	str      )	->							List[str]:
       return self.sp_model.encode(__lowerCamelCase			,     out_type=__lowerCamelCase      )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	Dict      )	->							Union[str, Any]:
       if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
       return self.encoder.get(__lowerCamelCase			,     self.encoder[self.unk_token]      )






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	int      )	->							str:
       if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
       return self.decoder.get(__lowerCamelCase			,     self.unk_token      )






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	Dict      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					[]
       SCREAMING_SNAKE_CASE__					=					''''''
       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(__lowerCamelCase      ) + token
                 SCREAMING_SNAKE_CASE__					=					[]
            else:
                 current_sub_tokens.append(__lowerCamelCase      )
       out_string += self.sp_model.decode(__lowerCamelCase      )
       return out_string.strip()






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None			,     __lowerCamelCase		:	bool = False      )	->							List[int]:

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

       SCREAMING_SNAKE_CASE__					=					[1] * len(self.prefix_tokens      )
       SCREAMING_SNAKE_CASE__					=					[1] * len(self.suffix_tokens      )
       if token_ids_a is None:
            return prefix_ones + ([0] * len(__lowerCamelCase      )) + suffix_ones
       return prefix_ones + ([0] * len(__lowerCamelCase      )) + ([0] * len(__lowerCamelCase      )) + suffix_ones






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	List[int]			,     __lowerCamelCase		:	Optional[List[int]] = None      )	->							List[int]:
       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    lowercase_	(							self		:	List[Any]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					{self.convert_ids_to_tokens(__lowerCamelCase      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab






  def __getstate__(							self		:	Optional[int]      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					self.__dict__.copy()
       SCREAMING_SNAKE_CASE__					=					None
       return state






  def __setstate__(							self		:	Any			,     __lowerCamelCase		:	Dict      )	->							None:
       SCREAMING_SNAKE_CASE__					=					d

       # for backward compatibility
       if not hasattr(self			,     '''sp_model_kwargs'''      ):
            SCREAMING_SNAKE_CASE__					=					{}

       SCREAMING_SNAKE_CASE__					=					load_spm(self.spm_file			,     self.sp_model_kwargs      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Optional[str] = None      )	->							Tuple[str]:
       SCREAMING_SNAKE_CASE__					=					Path(__lowerCamelCase      )
       if not save_dir.is_dir():
            raise OSError(f'''{save_directory} should be a directory'''      )
       SCREAMING_SNAKE_CASE__					=					save_dir / (
           (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
       )
       SCREAMING_SNAKE_CASE__					=					save_dir / (
           (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
       )

       save_json(self.encoder			,     __lowerCamelCase      )

       if os.path.abspath(self.spm_file      ) != os.path.abspath(__lowerCamelCase      ) and os.path.isfile(self.spm_file      ):
            copyfile(self.spm_file			,     __lowerCamelCase      )
       elif not os.path.isfile(self.spm_file      ):
            with open(__lowerCamelCase			,     '''wb'''      ) as fi:
                 SCREAMING_SNAKE_CASE__					=					self.sp_model.serialized_model_proto()
                 fi.write(__lowerCamelCase      )

       return (str(__lowerCamelCase      ), str(__lowerCamelCase      ))






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	str = "en"			,     __lowerCamelCase		:	Optional[List[str]] = None			,     __lowerCamelCase		:	str = "ro"			,     **__lowerCamelCase		:	int			,     )	->							BatchEncoding:
       SCREAMING_SNAKE_CASE__					=					src_lang
       SCREAMING_SNAKE_CASE__					=					tgt_lang
       self.set_src_lang_special_tokens(self.src_lang      )
       return super().prepare_seqaseq_batch(__lowerCamelCase			,     __lowerCamelCase			,     **__lowerCamelCase      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Optional[str]			,     __lowerCamelCase		:	Optional[str]			,     **__lowerCamelCase		:	Dict      )	->							Dict:
       if src_lang is None or tgt_lang is None:
            raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''      )
       SCREAMING_SNAKE_CASE__					=					src_lang
       SCREAMING_SNAKE_CASE__					=					self(__lowerCamelCase			,     add_special_tokens=__lowerCamelCase			,     **__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.get_lang_id(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					tgt_lang_id
       return inputs






  def    lowercase_	(							self		:	Dict      )	->							str:
       self.set_src_lang_special_tokens(self.src_lang      )






  def    lowercase_	(							self		:	List[Any]      )	->							Optional[int]:
       self.set_tgt_lang_special_tokens(self.tgt_lang      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	str      )	->							None:
       SCREAMING_SNAKE_CASE__					=					self.get_lang_token(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.lang_token_to_id[lang_token]
       SCREAMING_SNAKE_CASE__					=					[self.cur_lang_id]
       SCREAMING_SNAKE_CASE__					=					[self.eos_token_id]






  def    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	str      )	->							None:
       SCREAMING_SNAKE_CASE__					=					self.get_lang_token(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					self.lang_token_to_id[lang_token]
       SCREAMING_SNAKE_CASE__					=					[self.cur_lang_id]
       SCREAMING_SNAKE_CASE__					=					[self.eos_token_id]






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	str      )	->							str:
       return self.lang_code_to_token[lang]







  def    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	str      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.get_lang_token(__lowerCamelCase      )
       return self.lang_token_to_id[lang_token]



def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					sentencepiece.SentencePieceProcessor(**_A       )
     spm.Load(str(_A       )       )
     return spm
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     with open(_A   ,	'''r'''       ) as f:
          return json.load(_A       )







def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     with open(_A   ,	'''w'''       ) as f:
          json.dump(_A   ,	_A   ,	indent=2       )





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be trained."} )
  a     =      field(
      default="./"		,  metadata={"help": "Save dir where model repo is cloned and models updates are saved to."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path of training dataset."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for training."} )
  a     =      field(default=2		,  metadata={"help": "Batch size for evaluation."} )
  a     =      field(default=0.1		,  metadata={"help": "Value of weight decay."} )
  a     =      field(
      default=1_00_00		,  metadata={"help": "Size of buffer used to shuffle streaming dataset."} )
  a     =      field(default=2e-4		,  metadata={"help": "Learning rate fo training."} )
  a     =      field(default="cosine"		,  metadata={"help": "Learning rate."} )
  a     =      field(
      default=7_50		,  metadata={"help": "Number of warmup steps in the learning rate schedule."} )
  a     =      field(
      default=16		,  metadata={"help": "Number of gradient accumulation steps."} )
  a     =      field(
      default=A__		,  metadata={"help": "Use gradient checkpointing to reduce memory footprint."} )
  a     =      field(default=5_00_00		,  metadata={"help": "Maximum number of training steps."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Sequence lengths used for training."} )
  a     =      field(default=1		,  metadata={"help": "Training seed."} )
  a     =      field(
      default=10_24		,  metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "States path if the training should continue from a checkpoint folder."} )
  a     =      field(default=A__		,  metadata={"help": "If True the data is pretokenized."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-valid"		,  metadata={"help": "Name or path of validation dataset."} )
  a     =      field(default=2		,  metadata={"help": "Batch size used for evaluation."} )
  a     =      field(
      default=-1		,  metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
  a     =      field(default=10_24		,  metadata={"help": "Length of sequences to be evaluated."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )
  a     =      field(
      default=A__		,  metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Sample from the language model's output distribution."} )
  a     =      field(default=0.2		,  metadata={"help": "Sampling temperature used for generation."} )
  a     =      field(default=2_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  metadata={"help": "Top-p parameter used for nucleus sampling."} )
  a     =      field(default=10		,  metadata={"help": "Number of generations to run in parallel."} )
  a     =      field(
      default=2_00		,  metadata={"help": "Number of completions to generate for each sample."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="eval_results.json"		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="0"		,  metadata={"help": "Allow `code_eval` to execute Python code on machine"} )
  a     =      field(
      default=-1		,  metadata={
          "help": (
              "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
              " number corresponds to which GPU device id to run on."
          )
      }		,  )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
      }		,  )
  a     =      field(
      default="transformersbook/codeparrot"		,  metadata={"help": "Folder or name of dataset to process."} )
  a     =      field(
      default="codeparrot-clean"		,  metadata={"help": "Folder to save processed processed dataset."} )
  a     =      field(
      default=10_00_00		,  metadata={"help": "Number of files to save per JSON output file."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(
      default=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} )
  a     =      field(
      default=1.5		,  metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} )
  a     =      field(
      default=0.7		,  metadata={"help": "Probability for filtering config, test and uncommon files."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "If True, near-duplicate samples are removed."} )
  a     =      field(
      default=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2"		,  metadata={"help": "Base tokenizer to build new tokenizer from."} )
  a     =      field(
      default="transformersbook/codeparrot-train"		,  metadata={"help": "Dataset to train tokenizer on."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(default=20_00_00		,  metadata={"help": "Number of examples to train tokenizer on."} )
  a     =      field(
      default=3_27_68		,  metadata={"help": "Number of examples to train the tokenizer on."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of new tokenizer."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."} )
  a     =      field(
      default="codeparrot/codeparrot-clean-train"		,  metadata={"help": "Name or path to the dataset to pretokenize."} )
  a     =      field(
      default="tokenized-codeparrot-train"		,  metadata={"help": "Repo name of the pretokenized data."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="gpt2-large"		,  metadata={"help": "Configuration to use for model initialization."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Tokenizer attached to model."} )
  a     =      field(default="codeparrot"		,  metadata={"help": "Name of the created model."} )
  a     =      field(default=A__		,  metadata={"help": "Push saved tokenizer to the hub."} )


