





import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
		import optax
		from flax.training.common_utils import onehot

		from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
		from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 						lowerCamelCase       (unittest.TestCase      ):







							"""simple docstring"""




							@slow
							def  A_     ( self     :					Union[str, Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						AutoTokenizer.from_pretrained("google/mt5-small"						)

														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer("Hello there",			return_tensors="np"						).input_ids
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer("Hi I am",			return_tensors="np"						).input_ids

														SCREAMING_SNAKE_CASE__       :      Any     =						shift_tokens_right(_UpperCAmelCase,			model.config.pad_token_id,			model.config.decoder_start_token_id						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						model(_UpperCAmelCase,			decoder_input_ids=_UpperCAmelCase						).logits
														SCREAMING_SNAKE_CASE__       :      str     =						optax.softmax_cross_entropy(_UpperCAmelCase,			onehot(_UpperCAmelCase,			logits.shape[-1]						)						).mean()

														SCREAMING_SNAKE_CASE__       :      List[str]     =						-(labels.shape[-1] * loss.item())

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						-84.9127
														self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 1E-4						)













import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase :    int  							= logging.get_logger(__name__)

_lowerCamelCase :    Optional[Any]  							= '''▁'''

_lowerCamelCase :    Dict  							= {'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase :    int  							= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowerCamelCase :    Optional[Any]  							= {
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   VOCAB_FILES_NAMES
							UpperCAmelCase_      						=   PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_      						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_      						=   ["input_ids", "attention_mask"]






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





														"""simple docstring"""
														# Mask token behave like a normal word, i.e. include the space before it
														SCREAMING_SNAKE_CASE__       :      int     =						AddedToken(_UpperCAmelCase,			lstrip=_UpperCAmelCase,			rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) else mask_token

														SCREAMING_SNAKE_CASE__       :      List[str]     =						{} if sp_model_kwargs is None else sp_model_kwargs

														super().__init__(
														    bos_token=_UpperCAmelCase,			eos_token=_UpperCAmelCase,			unk_token=_UpperCAmelCase,			sep_token=_UpperCAmelCase,			cls_token=_UpperCAmelCase,			pad_token=_UpperCAmelCase,			mask_token=_UpperCAmelCase,			sp_model_kwargs=self.sp_model_kwargs,			**_UpperCAmelCase,			)

														SCREAMING_SNAKE_CASE__       :      str     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.Load(str(_UpperCAmelCase						)						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{"<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
														SCREAMING_SNAKE_CASE__       :      Dict     =						1

														SCREAMING_SNAKE_CASE__       :      int     =						len(self.sp_model						) + self.fairseq_offset
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}






							def __getstate__( self     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.__dict__.copy()
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						None
														SCREAMING_SNAKE_CASE__       :      Dict     =						self.sp_model.serialized_model_proto()
														return state






							def __setstate__( self     :					int,			_UpperCAmelCase     :					List[Any]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						d

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

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						spm.SentencePieceProcessor(**self.sp_model_kwargs						)
														self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""

														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[self.cls_token_id]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None,			_UpperCAmelCase     :					bool = False						)				->			List[int]:





														"""simple docstring"""

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

														if token_ids_a is None:
																					return [1] + ([0] * len(_UpperCAmelCase						)) + [1]
														return [1] + ([0] * len(_UpperCAmelCase						)) + [1, 1] + ([0] * len(_UpperCAmelCase						)) + [1]






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					List[int],			_UpperCAmelCase     :					Optional[List[int]] = None						)				->			List[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						[self.sep_token_id]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[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]






							@property
							def  A_     ( self     :					List[str]						)				->			List[str]:





														"""simple docstring"""
														return len(self.sp_model						) + self.fairseq_offset + 1  # Add the <mask> token






							def  A_     ( self     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						{self.convert_ids_to_tokens(_UpperCAmelCase						): i for i in range(self.vocab_size						)}
														vocab.update(self.added_tokens_encoder						)
														return vocab






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.sp_model.encode(_UpperCAmelCase,			out_type=_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.sp_model.PieceToId(_UpperCAmelCase						)

														# 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     :					Tuple,			_UpperCAmelCase     :					List[str]						)				->			List[str]:





														"""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  A_     ( self     :					Any,			_UpperCAmelCase     :					int						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						"".join(_UpperCAmelCase						).replace(_UpperCAmelCase,			" "						).strip()
														return out_string






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[str] = None						)				->			Tuple[str]:





														"""simple docstring"""
														if not os.path.isdir(_UpperCAmelCase						):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
																					return
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						os.path.join(
														    _UpperCAmelCase,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

														return (out_vocab_file,)






