

'''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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= '▁'

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

lowercase_     		= {
    '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'
        ),
    }
}

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

# fmt: off
lowercase_     		= ['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   SCREAMING_SNAKE_CASE			(						UpperCamelCase_):







		"""simple docstring"""
		lowercase   :      Union[str, Any]       =							VOCAB_FILES_NAMES
		lowercase   :      str       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase   :      Dict       =							PRETRAINED_VOCAB_FILES_MAP
		lowercase   :      Optional[int]       =							['input_ids', 'attention_mask']

		lowercase   :      Optional[Any]       =							[]
		lowercase   :      Dict       =							[]






		def __init__(   self		,						__A		,						__A=None		,						__A=None		,						__A="</s>"		,						__A="</s>"		,						__A="<s>"		,						__A="<unk>"		,						__A="<pad>"		,						__A="<mask>"		,						__A = None		,						**__A		,						) ->     None:
								# Mask token behave like a normal word, i.e. include the space before it
								_lowerCAmelCase										=AddedToken(__A		,						lstrip=__A		,						rstrip=__A				) if isinstance(__A		,						__A				) 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=__A		,						tgt_lang=__A		,						eos_token=__A		,						unk_token=__A		,						sep_token=__A		,						cls_token=__A		,						pad_token=__A		,						mask_token=__A		,						sp_model_kwargs=self.sp_model_kwargs		,						**__A		,						)

								_lowerCAmelCase										=spm.SentencePieceProcessor(**self.sp_model_kwargs				)
								self.sp_model.Load(str(__A				)				)
								_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(__A				)
								}
								_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 		UpperCamelCase__       (   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 		UpperCamelCase__       (   self				) ->     str:
								return self._src_lang






		@src_lang.setter
		def 		UpperCamelCase__       (   self		,						__A				) ->     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		,						__A				) ->     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 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										={self.convert_ids_to_tokens(__A				): i for i in range(self.vocab_size				)}
								vocab.update(self.added_tokens_encoder				)
								return vocab






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






		def 		UpperCamelCase__       (   self		,						__A				) ->     int:
								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								_lowerCAmelCase										=self.sp_model.PieceToId(__A				)

								# 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 		UpperCamelCase__       (   self		,						__A				) ->     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 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_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(__A				) + token
																				_lowerCAmelCase										=True
																				_lowerCAmelCase										=[]
														else:
																				current_sub_tokens.append(__A				)
																				_lowerCAmelCase										=False
								out_string += self.sp_model.decode(__A				)
								return out_string.strip()






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None				) ->     Tuple[str]:
								if not os.path.isdir(__A				):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
														return
								_lowerCAmelCase										=os.path.join(
								    __A		,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

								return (out_vocab_file,)






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = False				) ->     List[int]:

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

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A = 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 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A		,						__A		,						**__A				) ->     Dict:
								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(__A		,						add_special_tokens=__A		,						return_tensors=__A		,						**__A				)
								_lowerCAmelCase										=self.convert_tokens_to_ids(__A				)
								_lowerCAmelCase										=tgt_lang_id
								return inputs






		def 		UpperCamelCase__       (   self		,						__A		,						__A = "en_XX"		,						__A = None		,						__A = "ro_RO"		,						**__A		,						) ->     BatchEncoding:
								_lowerCAmelCase										=src_lang
								_lowerCAmelCase										=tgt_lang
								return super().prepare_seqaseq_batch(__A		,						__A		,						**__A				)






		def 		UpperCamelCase__       (   self				) ->     Dict:
								return self.set_src_lang_special_tokens(self.src_lang				)






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								return self.set_tgt_lang_special_tokens(self.tgt_lang				)






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:
								_lowerCAmelCase										=self.lang_code_to_id[src_lang]
								_lowerCAmelCase										=[self.cur_lang_code_id]
								_lowerCAmelCase										=[self.eos_token_id]






		def 		UpperCamelCase__       (   self		,						__A				) ->     None:
								_lowerCAmelCase										=self.lang_code_to_id[tgt_lang]
								_lowerCAmelCase										=[self.cur_lang_code_id]
								_lowerCAmelCase										=[self.eos_token_id]






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





