


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		__UpperCAmelCase : Optional[int] 			=				None


__UpperCAmelCase : int 			=				logging.get_logger(__name__)


__UpperCAmelCase : List[Any] 			=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase : str 			=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase : Union[str, Any] 			=				{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__UpperCAmelCase : Any 			=				["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]



class    UpperCAmelCase_	(							_a):
							'''simple docstring'''
							__UpperCamelCase      :     List[str]							=					VOCAB_FILES_NAMES
							__UpperCamelCase      :     str							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCamelCase      :     Tuple							=					PRETRAINED_VOCAB_FILES_MAP
							__UpperCamelCase      :     Union[str, Any]							=					["input_ids", "attention_mask"]
							__UpperCamelCase      :     Any							=					MBartTokenizer

							__UpperCamelCase      :     List[int]							=					[]
							__UpperCamelCase      :     List[int]							=					[]






							def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="<unk>" ,					__SCREAMING_SNAKE_CASE="<pad>" ,					__SCREAMING_SNAKE_CASE="<mask>" ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




														UpperCamelCase    :	Union[str, Any]   	=		AddedToken(__SCREAMING_SNAKE_CASE ,					lstrip=__SCREAMING_SNAKE_CASE ,					rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) else mask_token

														super().__init__(
														    vocab_file=__SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					bos_token=__SCREAMING_SNAKE_CASE ,					eos_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					src_lang=__SCREAMING_SNAKE_CASE ,					tgt_lang=__SCREAMING_SNAKE_CASE ,					additional_special_tokens=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

														UpperCamelCase    :	Dict   	=		vocab_file
														UpperCamelCase    :	List[str]   	=		False if not self.vocab_file else True

														UpperCamelCase    :	List[Any]   	=		FAIRSEQ_LANGUAGE_CODES.copy()

														if additional_special_tokens is not None:
																					# Only add those special tokens if they are not already there.
																					_additional_special_tokens.extend(
																					    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

														self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}						)
														UpperCamelCase    :	List[Any]   	=		{
														    lang_code: self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						) for lang_code in FAIRSEQ_LANGUAGE_CODES
														}

														UpperCamelCase    :	Dict   	=		src_lang if src_lang is not None else '''en_XX'''
														UpperCamelCase    :	List[Any]   	=		self.convert_tokens_to_ids(self._src_lang						)
														UpperCamelCase    :	str   	=		tgt_lang
														self.set_src_lang_special_tokens(self._src_lang						)






							@property
							def  _lowercase   (     self						):




														"""simple docstring"""




														return self._src_lang






							@src_lang.setter
							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														UpperCamelCase    :	Union[str, Any]   	=		new_src_lang
														self.set_src_lang_special_tokens(self._src_lang						)






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




														"""simple docstring"""




														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 ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




														"""simple docstring"""




														UpperCamelCase    :	str   	=		[self.sep_token_id]
														UpperCamelCase    :	Union[str, Any]   	=		[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 + sep + token_ids_a + sep						) * [0]






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														if src_lang is None or tgt_lang is None:
																					raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''						)
														UpperCamelCase    :	List[str]   	=		src_lang
														UpperCamelCase    :	Dict   	=		self(__SCREAMING_SNAKE_CASE ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					return_tensors=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	List[Any]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	Any   	=		tgt_lang_id
														return inputs






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = "en_XX" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "ro_RO" ,					**__SCREAMING_SNAKE_CASE ,					):




														"""simple docstring"""




														UpperCamelCase    :	Optional[Any]   	=		src_lang
														UpperCamelCase    :	Optional[int]   	=		tgt_lang
														return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														return self.set_src_lang_special_tokens(self.src_lang						)






							def  _lowercase   (     self						):




														"""simple docstring"""




														return self.set_tgt_lang_special_tokens(self.tgt_lang						)






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														UpperCamelCase    :	Optional[int]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	Any   	=		[]
														UpperCamelCase    :	Dict   	=		[self.eos_token_id, self.cur_lang_code]

														UpperCamelCase    :	Union[str, Any]   	=		self.convert_ids_to_tokens(self.prefix_tokens						)
														UpperCamelCase    :	int   	=		self.convert_ids_to_tokens(self.suffix_tokens						)

														UpperCamelCase    :	Tuple   	=		processors.TemplateProcessing(
														    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,					self.prefix_tokens + self.suffix_tokens						)						) ,					)






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




														"""simple docstring"""




														UpperCamelCase    :	Optional[int]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
														UpperCamelCase    :	int   	=		[]
														UpperCamelCase    :	Optional[Any]   	=		[self.eos_token_id, self.cur_lang_code]

														UpperCamelCase    :	Optional[Any]   	=		self.convert_ids_to_tokens(self.prefix_tokens						)
														UpperCamelCase    :	List[str]   	=		self.convert_ids_to_tokens(self.suffix_tokens						)

														UpperCamelCase    :	Optional[int]   	=		processors.TemplateProcessing(
														    single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,					self.prefix_tokens + self.suffix_tokens						)						) ,					)






							def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




														"""simple docstring"""




														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(__SCREAMING_SNAKE_CASE						):
																					logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
																					return
														UpperCamelCase    :	Optional[int]   	=		os.path.join(
														    __SCREAMING_SNAKE_CASE ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

														return (out_vocab_file,)





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCAmelCase : Optional[int] 			=				500000

__UpperCAmelCase			,    __UpperCAmelCase : Any 			=				os.path.split(__file__)
__UpperCAmelCase : int 			=				os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Union[str, Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Tuple   	=		dataset.map(**SCREAMING_SNAKE_CASE_       )


@get_duration
def      a	(							SCREAMING_SNAKE_CASE_      :		datasets.Dataset							,			**SCREAMING_SNAKE_CASE_      :		Any       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		dataset.filter(**SCREAMING_SNAKE_CASE_       )


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{'''num examples''': SPEED_TEST_N_EXAMPLES}
       with tempfile.TemporaryDirectory() as tmp_dir:
              UpperCamelCase    :	Dict   	=		datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
              UpperCamelCase    :	List[str]   	=		generate_example_dataset(
                  os.path.join(SCREAMING_SNAKE_CASE_							,			'''dataset.arrow'''       )							,			SCREAMING_SNAKE_CASE_							,			num_examples=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Union[str, Any]   	=		transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,			use_fast=SCREAMING_SNAKE_CASE_       )

              def tokenize(SCREAMING_SNAKE_CASE_      :		Dict       ):
                     return tokenizer(examples['''text''']       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[Any]   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''numpy'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''pandas'''       ):
                     UpperCamelCase    :	int   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''torch'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Dict   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              with dataset.formatted_as(type='''tensorflow'''							,			columns='''numbers'''       ):
                     UpperCamelCase    :	Tuple   	=		map(SCREAMING_SNAKE_CASE_							,			function=lambda SCREAMING_SNAKE_CASE_       : None							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		map(SCREAMING_SNAKE_CASE_							,			function=SCREAMING_SNAKE_CASE_							,			batched=SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	Dict   	=		filter(SCREAMING_SNAKE_CASE_       )

              # Activate later when tokenizer support batched inputs
              # with dataset.formatted_as(type='numpy'):
              #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

       with open(SCREAMING_SNAKE_CASE_							,			'''wb'''       ) as f:
              f.write(json.dumps(SCREAMING_SNAKE_CASE_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
  benchmark_map_filter()

