





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


UpperCamelCase__   : Union[str, Any]					  = logging.get_logger(__name__)

UpperCamelCase__   : Union[str, Any]					  = """▁"""

UpperCamelCase__   : List[Any]					  = {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase__   : List[Any]					  = {
    """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"""
        ),
    }
}

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

# fmt: off
UpperCamelCase__   : Optional[int]					  = ["""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  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       VOCAB_FILES_NAMES
					lowerCamelCase     :     Any			    =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCamelCase     :     List[str]			    =       PRETRAINED_VOCAB_FILES_MAP
					lowerCamelCase     :     Any			    =       ['input_ids', 'attention_mask']

					lowerCamelCase     :     List[int]			    =       []
					lowerCamelCase     :     List[int]			    =       []





					def __init__(						self    :  Optional[int] ,					__lowercase    :  Tuple ,					__lowercase    :  int=None ,					__lowercase    :  List[str]=None ,					__lowercase    :  Union[str, Any]="</s>" ,					__lowercase    :  Union[str, Any]="</s>" ,					__lowercase    :  List[Any]="<s>" ,					__lowercase    :  List[Any]="<unk>" ,					__lowercase    :  int="<pad>" ,					__lowercase    :  Tuple="<mask>" ,					__lowercase    :  Optional[Dict[str, Any]] = None ,					**__lowercase    :  Tuple ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     AddedToken(__lowercase ,					lstrip=__lowercase ,					rstrip=__lowercase		) if isinstance(__lowercase ,					__lowercase		) else mask_token

						UpperCAmelCase_								=     {} if sp_model_kwargs is None else sp_model_kwargs

						UpperCAmelCase_								=     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=__lowercase ,					tgt_lang=__lowercase ,					eos_token=__lowercase ,					unk_token=__lowercase ,					sep_token=__lowercase ,					cls_token=__lowercase ,					pad_token=__lowercase ,					mask_token=__lowercase ,					sp_model_kwargs=self.sp_model_kwargs ,					**__lowercase ,					)

						UpperCAmelCase_								=     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
						self.sp_model.Load(str(__lowercase		)		)
						UpperCAmelCase_								=     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
						UpperCAmelCase_								=     {"""<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
						UpperCAmelCase_								=     1

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

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

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





					@property
					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any]		):






						'''simple docstring'''







						return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token





					@property
					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						return self._src_lang





					@src_lang.setter
					def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     new_src_lang
						self.set_src_lang_special_tokens(self._src_lang		)





					def __getstate__(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.__dict__.copy()
						UpperCAmelCase_								=     None
						return state





					def __setstate__(						self    :  Tuple ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     d

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

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





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     {self.convert_ids_to_tokens(__lowercase		): i for i in range(self.vocab_size		)}
						vocab.update(self.added_tokens_encoder		)
						return vocab





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  str		):






						'''simple docstring'''







						return self.sp_model.encode(__lowercase ,					out_type=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str		):






						'''simple docstring'''







						if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
						UpperCAmelCase_								=     self.sp_model.PieceToId(__lowercase		)

						# 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        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  int		):






						'''simple docstring'''







						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        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     []
						UpperCAmelCase_								=     """"""
						UpperCAmelCase_								=     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(__lowercase		) + token
								UpperCAmelCase_								=     True
								UpperCAmelCase_								=     []
							else:
								current_sub_tokens.append(__lowercase		)
								UpperCAmelCase_								=     False
						out_string += self.sp_model.decode(__lowercase		)
						return out_string.strip()





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str ,					__lowercase    :  Optional[str] = None		):






						'''simple docstring'''







						if not os.path.isdir(__lowercase		):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
						UpperCAmelCase_								=     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		) and os.path.isfile(self.vocab_file		):
							copyfile(self.vocab_file ,					__lowercase		)
						elif not os.path.isfile(self.vocab_file		):
							with open(__lowercase ,					"""wb"""		) as fi:
								UpperCAmelCase_								=     self.sp_model.serialized_model_proto()
								fi.write(__lowercase		)

						return (out_vocab_file,)





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = None ,					__lowercase    :  bool = False		):






						'''simple docstring'''







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

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





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  List[int] ,					__lowercase    :  Optional[List[int]] = 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        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  Dict ,					__lowercase    :  str ,					__lowercase    :  Optional[str] ,					__lowercase    :  Optional[str] ,					**__lowercase    :  Optional[Any]		):






						'''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_								=     src_lang
						UpperCAmelCase_								=     self(__lowercase ,					add_special_tokens=__lowercase ,					return_tensors=__lowercase ,					**__lowercase		)
						UpperCAmelCase_								=     self.convert_tokens_to_ids(__lowercase		)
						UpperCAmelCase_								=     tgt_lang_id
						return inputs





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  List[str] ,					__lowercase    :  str = "en_XX" ,					__lowercase    :  Optional[List[str]] = None ,					__lowercase    :  str = "ro_RO" ,					**__lowercase    :  str ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     src_lang
						UpperCAmelCase_								=     tgt_lang
						return super().prepare_seqaseq_batch(__lowercase ,					__lowercase ,					**__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any		):






						'''simple docstring'''







						return self.set_src_lang_special_tokens(self.src_lang		)





					def        SCREAMING_SNAKE_CASE							(						self    :  int		):






						'''simple docstring'''







						return self.set_tgt_lang_special_tokens(self.tgt_lang		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int] ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.lang_code_to_id[src_lang]
						UpperCAmelCase_								=     [self.cur_lang_code_id]
						UpperCAmelCase_								=     [self.eos_token_id]





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.lang_code_to_id[tgt_lang]
						UpperCAmelCase_								=     [self.cur_lang_code_id]
						UpperCAmelCase_								=     [self.eos_token_id]









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	UpperCAmelCase_								=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]

	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]
	with tempfile.TemporaryDirectory() as tmp_dir:
		print("""generating dataset"""   )
		UpperCAmelCase_								=     datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
		UpperCAmelCase_								=     generate_example_dataset(
		    os.path.join(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

		print("""shuffling dataset"""   )
		UpperCAmelCase_								=     dataset.shuffle()
		print("""Second set of iterations (after shuffling"""   )
		for func, kwargs in functions_shuffled:
			print("""shuffled """				,      func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(
			    A				,      **A   )

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


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


