
"""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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	'''▁'''

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

SCREAMING_SNAKE_CASE_					     =	{
    '''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'''
        ),
    }
}

SCREAMING_SNAKE_CASE_					     =	{
    '''facebook/mbart-large-50-one-to-many-mmt''': 1_024,
}

# fmt: off
SCREAMING_SNAKE_CASE_					     =	['''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_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		VOCAB_FILES_NAMES
							_snake_case						      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case						      =		PRETRAINED_VOCAB_FILES_MAP
							_snake_case						      =		['''input_ids''', '''attention_mask''']

							_snake_case						      =		[]
							_snake_case						      =		[]




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

													__lowerCAmelCase     =  {} if sp_model_kwargs is None else sp_model_kwargs

													__lowerCAmelCase     =  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=snake_case_    ,				tgt_lang=snake_case_    ,				eos_token=snake_case_    ,				unk_token=snake_case_    ,				sep_token=snake_case_    ,				cls_token=snake_case_    ,				pad_token=snake_case_    ,				mask_token=snake_case_    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**snake_case_    ,				)

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

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

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

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




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




							@property
							def    A__						( self				)       ->  str:
													return self._src_lang




							@src_lang.setter
							def    A__						( self    ,				snake_case_				)       ->  None:
													__lowerCAmelCase     =  new_src_lang
													self.set_src_lang_special_tokens(self._src_lang				)




							def __getstate__( self				)       ->  Dict:
													__lowerCAmelCase     =  self.__dict__.copy()
													__lowerCAmelCase     =  None
													return state




							def __setstate__( self    ,				snake_case_				)       ->  None:
													__lowerCAmelCase     =  d

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

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




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  {self.convert_ids_to_tokens(snake_case_				): i for i in range(self.vocab_size				)}
													vocab.update(self.added_tokens_encoder				)
													return vocab




							def    A__						( self    ,				snake_case_				)       ->  List[str]:
													return self.sp_model.encode(snake_case_    ,				out_type=snake_case_				)




							def    A__						( self    ,				snake_case_				)       ->  int:
													if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
													__lowerCAmelCase     =  self.sp_model.PieceToId(snake_case_				)

													# 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    A__						( self    ,				snake_case_				)       ->  str:
													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    A__						( self    ,				snake_case_				)       ->  Dict:
													__lowerCAmelCase     =  []
													__lowerCAmelCase     =  """"""
													__lowerCAmelCase     =  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(snake_case_				) + token
																									__lowerCAmelCase     =  True
																									__lowerCAmelCase     =  []
																			else:
																									current_sub_tokens.append(snake_case_				)
																									__lowerCAmelCase     =  False
													out_string += self.sp_model.decode(snake_case_				)
													return out_string.strip()




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  Tuple[str]:
													if not os.path.isdir(snake_case_				):
																			logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
																			return
													__lowerCAmelCase     =  os.path.join(
													    snake_case_    ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

													return (out_vocab_file,)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = False				)       ->  List[int]:

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

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




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  List[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    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				**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"""				)
													__lowerCAmelCase     =  src_lang
													__lowerCAmelCase     =  self(snake_case_    ,				add_special_tokens=snake_case_    ,				return_tensors=snake_case_    ,				**snake_case_				)
													__lowerCAmelCase     =  self.convert_tokens_to_ids(snake_case_				)
													__lowerCAmelCase     =  tgt_lang_id
													return inputs




							def    A__						( self    ,				snake_case_    ,				snake_case_ = "en_XX"    ,				snake_case_ = None    ,				snake_case_ = "ro_RO"    ,				**snake_case_    ,				)       ->  BatchEncoding:
													__lowerCAmelCase     =  src_lang
													__lowerCAmelCase     =  tgt_lang
													return super().prepare_seqaseq_batch(snake_case_    ,				snake_case_    ,				**snake_case_				)




							def    A__						( self				)       ->  int:
													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    ,				snake_case_				)       ->  None:
													__lowerCAmelCase     =  self.lang_code_to_id[src_lang]
													__lowerCAmelCase     =  [self.cur_lang_code_id]
													__lowerCAmelCase     =  [self.eos_token_id]




							def    A__						( self    ,				snake_case_				)       ->  None:
													__lowerCAmelCase     =  self.lang_code_to_id[tgt_lang]
													__lowerCAmelCase     =  [self.cur_lang_code_id]
													__lowerCAmelCase     =  [self.eos_token_id]


"""simple docstring"""
import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
SCREAMING_SNAKE_CASE_					     =	[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]

def  lowercase     (_lowerCAmelCase						):
						# Test all the extensions added in the setup
						for file in FILES_TO_FIND:
												if not (transformers_path / file).exists():
																		return False
						return True


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			if args.check_lib:
						SCREAMING_SNAKE_CASE_					     =	importlib.import_module('''transformers''')
						SCREAMING_SNAKE_CASE_					     =	Path(transformers_module.__file__).parent
			else:
						SCREAMING_SNAKE_CASE_					     =	Path.cwd() / '''build/lib/transformers'''
			if not test_custom_files_are_present(transformers_path):
						raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')
