



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_nllb import NllbTokenizer
else:
				A__								=						None


A__								=						logging.get_logger(__name__)


A__								=						{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__								=						{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

A__								=						{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
A__								=						['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    Any											=					VOCAB_FILES_NAMES
		__lowerCAmelCase	:    Any											=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowerCAmelCase	:    str											=					PRETRAINED_VOCAB_FILES_MAP
		__lowerCAmelCase	:    List[Any]											=					["""input_ids""", """attention_mask"""]
		__lowerCAmelCase	:    Dict											=					NllbTokenizer

		__lowerCAmelCase	:    List[int]											=					[]
		__lowerCAmelCase	:    List[int]											=					[]



		def __init__(     self     :Dict   ,__lowercase     :List[Any]=None   ,__lowercase     :List[str]=None   ,__lowercase     :List[Any]="<s>"   ,__lowercase     :Optional[Any]="</s>"   ,__lowercase     :Dict="</s>"   ,__lowercase     :Dict="<s>"   ,__lowercase     :List[Any]="<unk>"   ,__lowercase     :Optional[Any]="<pad>"   ,__lowercase     :Union[str, Any]="<mask>"   ,__lowercase     :Any=None   ,__lowercase     :Optional[int]=None   ,__lowercase     :Optional[int]=None   ,__lowercase     :List[str]=False   ,**__lowercase     :List[str]   ,):
						# Mask token behave like a normal word, i.e. include the space before it
						snake_case__	:      str     		=	AddedToken(__lowercase   ,lstrip=__lowercase   ,rstrip=__lowercase  ) if isinstance(__lowercase   ,__lowercase  ) else mask_token
						snake_case__	:      Optional[int]     		=	legacy_behaviour
						super().__init__(
						    vocab_file=__lowercase   ,tokenizer_file=__lowercase   ,bos_token=__lowercase   ,eos_token=__lowercase   ,sep_token=__lowercase   ,cls_token=__lowercase   ,unk_token=__lowercase   ,pad_token=__lowercase   ,mask_token=__lowercase   ,src_lang=__lowercase   ,tgt_lang=__lowercase   ,additional_special_tokens=__lowercase   ,legacy_behaviour=__lowercase   ,**__lowercase   ,)

						snake_case__	:      List[Any]     		=	vocab_file
						snake_case__	:      Dict     		=	False if not self.vocab_file else True

						snake_case__	:      Tuple     		=	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}  )
						snake_case__	:      int     		=	{
						    lang_code: self.convert_tokens_to_ids(__lowercase  ) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						snake_case__	:      Optional[int]     		=	src_lang if src_lang is not None else '''eng_Latn'''
						snake_case__	:      Tuple     		=	self.convert_tokens_to_ids(self._src_lang  )
						snake_case__	:      str     		=	tgt_lang
						self.set_src_lang_special_tokens(self._src_lang  )



		@property
		def 	__lowerCamelCase			(     self     :Any  ):
						return self._src_lang



		@src_lang.setter
		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :str  ):
						snake_case__	:      Any     		=	new_src_lang
						self.set_src_lang_special_tokens(self._src_lang  )



		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :List[int]   ,__lowercase     :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 	__lowerCamelCase			(     self     :List[Any]   ,__lowercase     :List[int]   ,__lowercase     :Optional[List[int]] = None  ):
						snake_case__	:      Dict     		=	[self.sep_token_id]
						snake_case__	:      Dict     		=	[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 	__lowerCamelCase			(     self     :str   ,__lowercase     :Any   ,__lowercase     :str   ,__lowercase     :Optional[str]   ,__lowercase     :Optional[str]   ,**__lowercase     :int  ):
						if src_lang is None or tgt_lang is None:
										raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''  )
						snake_case__	:      Union[str, Any]     		=	src_lang
						snake_case__	:      Tuple     		=	self(__lowercase   ,add_special_tokens=__lowercase   ,return_tensors=__lowercase   ,**__lowercase  )
						snake_case__	:      Tuple     		=	self.convert_tokens_to_ids(__lowercase  )
						snake_case__	:      int     		=	tgt_lang_id
						return inputs



		def 	__lowerCamelCase			(     self     :int   ,__lowercase     :List[str]   ,__lowercase     :str = "eng_Latn"   ,__lowercase     :Optional[List[str]] = None   ,__lowercase     :str = "fra_Latn"   ,**__lowercase     :Tuple   ,):
						snake_case__	:      List[str]     		=	src_lang
						snake_case__	:      Optional[int]     		=	tgt_lang
						return super().prepare_seqaseq_batch(__lowercase   ,__lowercase   ,**__lowercase  )



		def 	__lowerCamelCase			(     self     :Any  ):
						return self.set_src_lang_special_tokens(self.src_lang  )



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



		def 	__lowerCamelCase			(     self     :Tuple   ,__lowercase     :str  ):
						snake_case__	:      Optional[Any]     		=	self.convert_tokens_to_ids(__lowercase  )

						if self.legacy_behaviour:
										snake_case__	:      Any     		=	[]
										snake_case__	:      Optional[Any]     		=	[self.eos_token_id, self.cur_lang_code]
						else:
										snake_case__	:      Optional[int]     		=	[self.cur_lang_code]
										snake_case__	:      Tuple     		=	[self.eos_token_id]

						snake_case__	:      Union[str, Any]     		=	self.convert_ids_to_tokens(self.prefix_tokens  )
						snake_case__	:      Dict     		=	self.convert_ids_to_tokens(self.suffix_tokens  )

						snake_case__	:      Any     		=	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 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :str  ):
						snake_case__	:      Any     		=	self.convert_tokens_to_ids(__lowercase  )
						if self.legacy_behaviour:
										snake_case__	:      Dict     		=	[]
										snake_case__	:      Any     		=	[self.eos_token_id, self.cur_lang_code]
						else:
										snake_case__	:      Optional[Any]     		=	[self.cur_lang_code]
										snake_case__	:      List[str]     		=	[self.eos_token_id]

						snake_case__	:      int     		=	self.convert_ids_to_tokens(self.prefix_tokens  )
						snake_case__	:      Optional[int]     		=	self.convert_ids_to_tokens(self.suffix_tokens  )

						snake_case__	:      List[str]     		=	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 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[str] = None  ):
						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(__lowercase  ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""  )
										return
						snake_case__	:      str     		=	os.path.join(
						    __lowercase   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )

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

						return (out_vocab_file,)






from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	[]
				for part_id in partition_order:
								snake_case__	:      Any     		=	df.where(f"""SPARK_PARTITION_ID() = {part_id}"""      ).collect()
								for row_idx, row in enumerate(__lowerCAmelCase      ):
												expected_row_ids_and_row_dicts.append((f"""{part_id}_{row_idx}""", row.asDict())      )
				return expected_row_ids_and_row_dicts



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Tuple:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Optional[Any]     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Optional[int]     		=	Spark(__lowerCAmelCase      )
				# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
				# that each partition can hold 2 rows.
				spark_builder._repartition_df_if_needed(max_shard_size=16      )
				# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
				assert spark_builder.df.rdd.getNumPartitions() == 50



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(10      ).repartition(2      )
				snake_case__	:      Any     		=	[1, 0]
				snake_case__	:      Tuple     		=	_generate_iterable_examples(__lowerCAmelCase  ,  __lowerCAmelCase      )  # Reverse the partitions.
				snake_case__	:      Any     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  __lowerCAmelCase      )

				for i, (row_id, row_dict) in enumerate(generate_fn()      ):
								snake_case__		, snake_case__	:      Union[str, Any]     		=	expected_row_ids_and_row_dicts[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Any:


				"""simple docstring"""

				snake_case__	:      Any     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      List[Any]     		=	spark.range(10      ).repartition(1      )
				snake_case__	:      int     		=	SparkExamplesIterable(__lowerCAmelCase      )
				assert it.n_shards == 1
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								assert row_id == f"""0_{i}"""
								assert row_dict == {"id": i}



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Tuple     		=	spark.range(30      ).repartition(3      )
				# Mock the generator so that shuffle reverses the partition indices.
				with patch('''numpy.random.Generator'''      ) as generator_mock:
								snake_case__	:      Union[str, Any]     		=	lambda __lowerCAmelCase      : x.reverse()
								snake_case__	:      List[str]     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [2, 1, 0]      )

								snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shuffle_data_sources(__lowerCAmelCase      )
								assert shuffled_it.n_shards == 3
								for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
												snake_case__		, snake_case__	:      Optional[Any]     		=	expected_row_ids_and_row_dicts[i]
												assert row_id == expected_row_id
												assert row_dict == expected_row_dict



@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Union[str, Any]     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(20      ).repartition(4      )

				# Partitions 0 and 2
				snake_case__	:      List[Any]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=0  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      int     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [0, 2]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, snake_case__	:      Dict     		=	expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict

				# Partitions 1 and 3
				snake_case__	:      List[str]     		=	SparkExamplesIterable(__lowerCAmelCase      ).shard_data_sources(worker_id=1  ,  num_workers=2      )
				assert shard_it_a.n_shards == 2
				snake_case__	:      List[Any]     		=	_get_expected_row_ids_and_row_dicts_for_partition_order(__lowerCAmelCase  ,  [1, 3]      )
				for i, (row_id, row_dict) in enumerate(__lowerCAmelCase      ):
								snake_case__		, snake_case__	:      Optional[int]     		=	expected_row_ids_and_row_dicts_a[i]
								assert row_id == expected_row_id
								assert row_dict == expected_row_dict





@require_not_windows
@require_dill_gt_0_3_2
def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      int     		=	pyspark.sql.SparkSession.builder.master('''local[*]'''      ).appName('''pyspark'''      ).getOrCreate()
				snake_case__	:      Dict     		=	spark.range(100      ).repartition(1      )
				snake_case__	:      Tuple     		=	Spark(__lowerCAmelCase      )
				# Choose a small max_shard_size for maximum partitioning.
				spark_builder._repartition_df_if_needed(max_shard_size=1      )
				# The new number of partitions should not be greater than the number of rows.
				assert spark_builder.df.rdd.getNumPartitions() == 100

