


"""simple docstring"""






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

import sentencepiece as spm

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


_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  '''▁'''

_lowerCamelCase     =  {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase     =  {
    '''vocab_file''': {
        '''facebook/mbart-large-50-one-to-many-mmt''': (
            '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowerCamelCase     =  {
    '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
}

# fmt: off
_lowerCamelCase     =  ['''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''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']


class 			lowerCamelCase_		(			UpperCamelCase__   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Union[str, Any]      							=					VOCAB_FILES_NAMES
				_lowerCAmelCase      :      Any      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase      :      Optional[int]      							=					PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase      :      List[str]      							=					["""input_ids""", """attention_mask"""]

				_lowerCAmelCase      :      List[int]      							=					[]
				_lowerCAmelCase      :      List[int]      							=					[]



				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__="</s>"       ,		UpperCAmelCase__="</s>"       ,		UpperCAmelCase__="<s>"       ,		UpperCAmelCase__="<unk>"       ,		UpperCAmelCase__="<pad>"       ,		UpperCAmelCase__="<mask>"       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								# Mask token behave like a normal word, i.e. include the space before it
								SCREAMING_SNAKE_CASE__     							=				AddedToken(UpperCAmelCase__       ,		lstrip=UpperCAmelCase__       ,		rstrip=UpperCAmelCase__     ) if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ) else mask_token

								SCREAMING_SNAKE_CASE__     							=				{} if sp_model_kwargs is None else sp_model_kwargs

								SCREAMING_SNAKE_CASE__     							=				kwargs.get("additional_special_tokens"       ,		[]     )
								kwargs["additional_special_tokens"] += [
								    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    src_lang=UpperCAmelCase__       ,		tgt_lang=UpperCAmelCase__       ,		eos_token=UpperCAmelCase__       ,		unk_token=UpperCAmelCase__       ,		sep_token=UpperCAmelCase__       ,		cls_token=UpperCAmelCase__       ,		pad_token=UpperCAmelCase__       ,		mask_token=UpperCAmelCase__       ,		sp_model_kwargs=self.sp_model_kwargs       ,		**UpperCAmelCase__       ,		)

								SCREAMING_SNAKE_CASE__     							=				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(str(UpperCAmelCase__     )     )
								SCREAMING_SNAKE_CASE__     							=				vocab_file

								# Original fairseq vocab and spm vocab must be "aligned":
								# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
								# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
								# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
								# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

								# Mimic fairseq token-to-id alignment for the first 4 token
								SCREAMING_SNAKE_CASE__     							=				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

								# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
								SCREAMING_SNAKE_CASE__     							=				1

								SCREAMING_SNAKE_CASE__     							=				len(self.sp_model     )
								SCREAMING_SNAKE_CASE__     							=				{
								    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCAmelCase__     )
								}
								SCREAMING_SNAKE_CASE__     							=				{v: k for k, v in self.lang_code_to_id.items()}
								SCREAMING_SNAKE_CASE__     							=				len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

								self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
								SCREAMING_SNAKE_CASE__     							=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}

								SCREAMING_SNAKE_CASE__     							=				src_lang if src_lang is not None else "en_XX"
								SCREAMING_SNAKE_CASE__     							=				self.lang_code_to_id[self._src_lang]
								SCREAMING_SNAKE_CASE__     							=				tgt_lang
								self.set_src_lang_special_tokens(self._src_lang     )



				@property
				def   lowerCAmelCase__		(						self     ):
								return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token



				@property
				def   lowerCAmelCase__		(						self     ):
								return self._src_lang



				@src_lang.setter
				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				new_src_lang
								self.set_src_lang_special_tokens(self._src_lang     )



				def __getstate__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.__dict__.copy()
								SCREAMING_SNAKE_CASE__     							=				None
								return state



				def __setstate__(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				d

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

								SCREAMING_SNAKE_CASE__     							=				spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				{self.convert_ids_to_tokens(UpperCAmelCase__     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								return self.sp_model.encode(UpperCAmelCase__       ,		out_type=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
								SCREAMING_SNAKE_CASE__     							=				self.sp_model.PieceToId(UpperCAmelCase__     )

								# Need to return unknown token if the SP model returned 0
								return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(index - self.fairseq_offset     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				[]
								SCREAMING_SNAKE_CASE__     							=				""
								SCREAMING_SNAKE_CASE__     							=				False
								for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																if not prev_is_special:
																				out_string += " "
																out_string += self.sp_model.decode(UpperCAmelCase__     ) + token
																SCREAMING_SNAKE_CASE__     							=				True
																SCREAMING_SNAKE_CASE__     							=				[]
												else:
																current_sub_tokens.append(UpperCAmelCase__     )
																SCREAMING_SNAKE_CASE__     							=				False
								out_string += self.sp_model.decode(UpperCAmelCase__     )
								return out_string.strip()



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								if not os.path.isdir(UpperCAmelCase__     ):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
												return
								SCREAMING_SNAKE_CASE__     							=				os.path.join(
								    UpperCAmelCase__       ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

								return (out_vocab_file,)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = False     ):

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

								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(UpperCAmelCase__     )) + suffix_ones
								return prefix_ones + ([0] * len(UpperCAmelCase__     )) + ([0] * len(UpperCAmelCase__     )) + suffix_ones



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		**UpperCAmelCase__     ):
								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(UpperCAmelCase__       ,		add_special_tokens=UpperCAmelCase__       ,		return_tensors=UpperCAmelCase__       ,		**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.convert_tokens_to_ids(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tgt_lang_id
								return inputs



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = "en_XX"       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = "ro_RO"       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				src_lang
								SCREAMING_SNAKE_CASE__     							=				tgt_lang
								return super().prepare_seqaseq_batch(UpperCAmelCase__       ,		UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								return self.set_src_lang_special_tokens(self.src_lang     )



				def   lowerCAmelCase__		(						self     ):
								return self.set_tgt_lang_special_tokens(self.tgt_lang     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.lang_code_to_id[src_lang]
								SCREAMING_SNAKE_CASE__     							=				[self.cur_lang_code_id]
								SCREAMING_SNAKE_CASE__     							=				[self.eos_token_id]






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				self.lang_code_to_id[tgt_lang]
								SCREAMING_SNAKE_CASE__     							=				[self.cur_lang_code_id]
								SCREAMING_SNAKE_CASE__     							=				[self.eos_token_id]




"""simple docstring"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  500000

_lowerCamelCase     ,     _lowerCamelCase     =  os.path.split(__file__)
_lowerCamelCase     =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.map(**lowerCamelCase_  )


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.filter(**lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE__     							=				datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
								SCREAMING_SNAKE_CASE__     							=				generate_example_dataset(
								    os.path.join(lowerCamelCase_						,					"dataset.arrow"  )						,					lowerCamelCase_						,					num_examples=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"						,					use_fast=lowerCamelCase_  )

								def tokenize(lowerCamelCase_  :      Union[str, Any]  ):
												return tokenizer(examples["text"]  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="numpy"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="pandas"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="torch"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="tensorflow"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				filter(lowerCamelCase_  )

								# 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(lowerCamelCase_						,					"wb"  ) as f:
								f.write(json.dumps(lowerCamelCase_  ).encode("utf-8"  )  )


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