"""simple docstring"""






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_mbartaa import MBartaaTokenizer
else:
				UpperCamelCase     					=     None


UpperCamelCase     					=     logging.get_logger(__name__)

UpperCamelCase     					=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase     					=     {
    """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"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-50-one-to-many-mmt""": (
            """https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCamelCase     					=     {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
UpperCamelCase     					=     ["""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   UpperCAmelCase__				(    __lowerCamelCase			):


	"""simple docstring"""
	lowerCAmelCase__    :					List[str]						      =							VOCAB_FILES_NAMES
	lowerCAmelCase__    :					Dict						      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__    :					Optional[int]						      =							PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__    :					Optional[int]						      =							["""input_ids""", """attention_mask"""]
	lowerCAmelCase__    :					Optional[int]						      =							MBartaaTokenizer

	lowerCAmelCase__    :					Union[str, Any]						      =							[]
	lowerCAmelCase__    :					List[str]						      =							[]







	def __init__(			self       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     _SCREAMING_SNAKE_CASE=None       ,     _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       ,     )    ->	int:
							# Mask token behave like a normal word, i.e. include the space before it
							a_							:			int     		=					AddedToken(_lowerCAmelCase       ,     lstrip=_lowerCAmelCase       ,     rstrip=_lowerCAmelCase ) if isinstance(_lowerCAmelCase       ,     _lowerCAmelCase ) else mask_token

							a_							:			Union[str, Any]     		=					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__(
							    _lowerCAmelCase       ,     src_lang=_lowerCAmelCase       ,     tgt_lang=_lowerCAmelCase       ,     tokenizer_file=_lowerCAmelCase       ,     eos_token=_lowerCAmelCase       ,     sep_token=_lowerCAmelCase       ,     cls_token=_lowerCAmelCase       ,     unk_token=_lowerCAmelCase       ,     pad_token=_lowerCAmelCase       ,     mask_token=_lowerCAmelCase       ,     **_lowerCAmelCase       ,     )

							a_							:			Union[str, Any]     		=					vocab_file
							a_							:			Optional[int]     		=					False if not self.vocab_file else True

							a_							:			Any     		=					{
							    lang_code: self.convert_tokens_to_ids(_lowerCAmelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							a_							:			List[str]     		=					src_lang if src_lang is not None else """en_XX"""
							a_							:			Optional[Any]     		=					tgt_lang
							a_							:			List[str]     		=					self.lang_code_to_id[self._src_lang]
							self.set_src_lang_special_tokens(self._src_lang )







	@property
	def       A    (			self )    ->	Optional[Any]:
							return self._src_lang







	@src_lang.setter
	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Dict:
							a_							:			Dict     		=					new_src_lang
							self.set_src_lang_special_tokens(self._src_lang )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None )    ->	Optional[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       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = "en_XX"       ,     _SCREAMING_SNAKE_CASE = None       ,     _SCREAMING_SNAKE_CASE = "ro_RO"       ,     **_SCREAMING_SNAKE_CASE       ,     )    ->	List[Any]:
							a_							:			int     		=					src_lang
							a_							:			Optional[int]     		=					tgt_lang
							return super().prepare_seqaseq_batch(_lowerCAmelCase       ,     _lowerCAmelCase       ,     **_lowerCAmelCase )







	def       A    (			self )    ->	Union[str, Any]:
							return self.set_src_lang_special_tokens(self.src_lang )







	def       A    (			self )    ->	int:
							return self.set_tgt_lang_special_tokens(self.tgt_lang )







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Any:
							a_							:			Any     		=					self.convert_tokens_to_ids(_lowerCAmelCase )
							a_							:			Optional[Any]     		=					[self.cur_lang_code_id]
							a_							:			Optional[Any]     		=					[self.eos_token_id]

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

							a_							:			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       A    (			self       ,     _SCREAMING_SNAKE_CASE )    ->	Any:
							a_							:			Tuple     		=					self.convert_tokens_to_ids(_lowerCAmelCase )
							a_							:			List[str]     		=					[self.cur_lang_code_id]
							a_							:			Optional[int]     		=					[self.eos_token_id]

							a_							:			str     		=					self.convert_ids_to_tokens(self.prefix_tokens )
							a_							:			Any     		=					self.convert_ids_to_tokens(self.suffix_tokens )

							a_							:			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       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE       ,     **_SCREAMING_SNAKE_CASE )    ->	List[str]:
							if src_lang is None or tgt_lang is None:
													raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
							a_							:			str     		=					src_lang
							a_							:			Tuple     		=					self(_lowerCAmelCase       ,     add_special_tokens=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase )
							a_							:			str     		=					self.convert_tokens_to_ids(_lowerCAmelCase )
							a_							:			int     		=					tgt_lang_id
							return inputs







	def       A    (			self       ,     _SCREAMING_SNAKE_CASE       ,     _SCREAMING_SNAKE_CASE = None )    ->	int:
							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(_lowerCAmelCase ):
													logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
													return
							a_							:			int     		=					os.path.join(
							    _lowerCAmelCase       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

							return (out_vocab_file,)





"""simple docstring"""






import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


UpperCamelCase     					=     (30, 4_87)
UpperCamelCase     					=     (36, 10_24)
UpperCamelCase     					=     (1_00, 1_00)
UpperCamelCase     					=     1_00

UpperCamelCase     					=     datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

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

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Tuple		,			_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Tuple  )				-> Any:
						with ArrowWriter(features=_SCREAMING_SNAKE_CASE		,			path=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )  ) as writer:
												for key, record in dummy_data:
																		a_							:			int     		=					my_features.encode_example(_SCREAMING_SNAKE_CASE  )
																		writer.write(_SCREAMING_SNAKE_CASE  )
												a_		,					a_							:			str     		=					writer.finalize()

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Any  )				-> Tuple:
						a_							:			Dict     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Dict  )				-> Optional[Any]:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for _ in dataset:
												pass

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[Any]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> List[str]:
						a_							:			Optional[Any]     		=					10
						a_							:			Optional[int]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			List[str]     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:List[str]  )				-> Optional[int]:
						a_							:			List[str]     		=					10
						a_							:			int     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for i in range(0		,			len(_SCREAMING_SNAKE_CASE  )		,			_SCREAMING_SNAKE_CASE  ):
												a_							:			str     		=					dataset[i : i + batch_size]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:List[str]		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			List[str]     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						for col in feats:
												a_							:			List[Any]     		=					dataset[col]

@get_duration
def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:str		,			_SCREAMING_SNAKE_CASE	:Optional[Any]  )				-> Dict:
						a_							:			Any     		=					datasets.Dataset.from_file(
						    filename=os.path.join(_SCREAMING_SNAKE_CASE		,			"beta.arrow"  )		,			info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE  )  )
						dataset.set_format("numpy"  )
						for col in feats:
												a_							:			Any     		=					dataset[col]

def  lowerCAmelCase_      ()				-> Optional[Any]:
						a_							:			List[Any]     		=					{}
						a_							:			str     		=					(
						    read_unformated,
						    read_formatted_as_numpy,
						    read_batch_unformated,
						    read_batch_formatted_as_numpy,
						    read_col_unformated,
						    read_col_formatted_as_numpy,
						)
						with tempfile.TemporaryDirectory() as tmp_dir:
												a_							:			Optional[int]     		=					datasets.Features({"image": ArrayaD(_SCREAMING_SNAKE_CASE		,			dtype="float32"  )}  )
												a_							:			Optional[Any]     		=					generate_examples(features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE  )
												a_							:			List[str]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			Optional[int]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
												# )
												a_							:			Union[str, Any]     		=					datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"  )  )  )}  )
												a_							:			List[str]     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": SPEED_TEST_SHAPE}  )
												a_							:			str     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			List[str]     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						with tempfile.TemporaryDirectory() as tmp_dir:
												# don't use fixed length for fair comparison
												# feats = datasets.Features(
												#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
												# )
												a_							:			int     		=					datasets.Features({"image": datasets.Sequence(datasets.Value("float32"  )  )}  )
												a_							:			str     		=					generate_examples(
												    features=_SCREAMING_SNAKE_CASE		,			num_examples=_SCREAMING_SNAKE_CASE		,			seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,			)
												a_							:			List[Any]     		=					write(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )
												for read_func in read_functions:
																		a_							:			str     		=					read_func(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						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_array_xd()




