




'''simple docstring'''





import os
import re
import unicodedata
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


_SCREAMING_SNAKE_CASE							:     List[str]   	=				logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							:     List[str]   	=				{"vocab_file": "spiece.model"}

_SCREAMING_SNAKE_CASE							:     List[str]   	=				{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

_SCREAMING_SNAKE_CASE							:     List[Any]   	=				{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

_SCREAMING_SNAKE_CASE							:     str   	=				"▁"




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Dict		 =					VOCAB_FILES_NAMES
			lowerCAmelCase_  :   Union[str, Any]		 =					PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_  :   Tuple		 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_  :   str		 =					["input_ids", "token_type_ids"]




			def __init__(      self       ,							a__       ,							a__=False       ,							a__=True       ,							a__=True       ,							a__="<unk>"       ,							a__="[SEP]"       ,							a__="<pad>"       ,							a__="[CLS]"       ,							a__="[MASK]"       ,							a__ = None       ,							**a__       ,							)       ->       None:

										'''simple docstring'''




										snake_case_			      =   (
										    AddedToken(a__       ,							lstrip=a__       ,							rstrip=a__       ,							normalized=a__							)
										    if isinstance(a__       ,							a__							)
										    else mask_token
										)

										snake_case_			      =   {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    do_lower_case=a__       ,							remove_space=a__       ,							keep_accents=a__       ,							unk_token=a__       ,							sep_token=a__       ,							pad_token=a__       ,							cls_token=a__       ,							mask_token=a__       ,							sp_model_kwargs=self.sp_model_kwargs       ,							**a__       ,							)

										snake_case_			      =   do_lower_case
										snake_case_			      =   remove_space
										snake_case_			      =   keep_accents
										snake_case_			      =   vocab_file

										snake_case_			      =   spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(a__							)




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

										'''simple docstring'''




										return len(self.sp_model							)




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




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




			def __getstate__(      self							)       ->       List[str]:

										'''simple docstring'''




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




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

										'''simple docstring'''




										snake_case_			      =   d

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

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




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[Any]:

										'''simple docstring'''




										if self.remove_space:
																	snake_case_			      =   " ".join(inputs.strip().split()							)
										else:
																	snake_case_			      =   inputs
										snake_case_			      =   outputs.replace("``"       ,							"\""							).replace("''"       ,							"\""							)

										if not self.keep_accents:
																	snake_case_			      =   unicodedata.normalize("NFKD"       ,							a__							)
																	snake_case_			      =   "".join([c for c in outputs if not unicodedata.combining(a__							)]							)
										if self.do_lower_case:
																	snake_case_			      =   outputs.lower()

										return outputs




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   self.preprocess_text(a__							)
										snake_case_			      =   self.sp_model.encode(a__       ,							out_type=a__							)
										snake_case_			      =   []
										for piece in pieces:
																	if len(a__							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
																								snake_case_			      =   self.sp_model.EncodeAsPieces(piece[:-1].replace(a__       ,							""							)							)
																								if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																															if len(cur_pieces[0]							) == 1:
																																						snake_case_			      =   cur_pieces[1:]
																															else:
																																						snake_case_			      =   cur_pieces[0][1:]
																								cur_pieces.append(piece[-1]							)
																								new_pieces.extend(a__							)
																	else:
																								new_pieces.append(a__							)

										return new_pieces




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       int:

										'''simple docstring'''




										return self.sp_model.PieceToId(a__							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Union[str, Any]:

										'''simple docstring'''




										return self.sp_model.IdToPiece(a__							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       List[str]:

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = False       ,							a__ = None       ,							a__ = True       ,							**a__       ,							)       ->       str:

										'''simple docstring'''




										snake_case_			      =   kwargs.pop("use_source_tokenizer"       ,							a__							)

										snake_case_			      =   self.convert_ids_to_tokens(a__       ,							skip_special_tokens=a__							)

										# To avoid mixing byte-level and unicode for byte-level BPT
										# we need to build string separately for added tokens and byte-level tokens
										# cf. https://github.com/huggingface/transformers/issues/1133
										snake_case_			      =   []
										snake_case_			      =   []
										for token in filtered_tokens:
																	if skip_special_tokens and token in self.all_special_ids:
																								continue
																	if token in self.added_tokens_encoder:
																								if current_sub_text:
																															sub_texts.append(self.convert_tokens_to_string(a__							)							)
																															snake_case_			      =   []
																								sub_texts.append(a__							)
																	else:
																								current_sub_text.append(a__							)
										if current_sub_text:
																	sub_texts.append(self.convert_tokens_to_string(a__							)							)

										# Mimic the behavior of the Rust tokenizer:
										# No space after <unk>
										if spaces_between_special_tokens:
																	snake_case_			      =   re.sub(r"(<unk>) "       ,							r"\1"       ,							" ".join(a__							)							)
										else:
																	snake_case_			      =   "".join(a__							)

										snake_case_			      =   (
										    clean_up_tokenization_spaces
										    if clean_up_tokenization_spaces is not None
										    else self.clean_up_tokenization_spaces
										)
										if clean_up_tokenization_spaces:
																	snake_case_			      =   self.clean_up_tokenization(a__							)
																	return clean_text
										else:
																	return text




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None							)       ->       List[int]:

										'''simple docstring'''




										snake_case_			      =   [self.sep_token_id]
										snake_case_			      =   [self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None       ,							a__ = False							)       ->       List[int]:

										'''simple docstring'''




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

										if token_ids_a is not None:
																	return [1] + ([0] * len(a__							)) + [1] + ([0] * len(a__							)) + [1]
										return [1] + ([0] * len(a__							)) + [1]




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None							)       ->       List[int]:

										'''simple docstring'''




										snake_case_			      =   [self.sep_token_id]
										snake_case_			      =   [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							) * [0] + len(token_ids_a + sep							) * [1]




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None							)       ->       Tuple[str]:

										'''simple docstring'''




										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_			      =   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:
																								snake_case_			      =   self.sp_model.serialized_model_proto()
																								fi.write(a__							)

										return (out_vocab_file,)










'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 				_snake_case	(			unittest.TestCase							):




			@slow
			def      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   CamembertModel.from_pretrained("camembert-base"							)
										model.to(a__							)

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   torch.tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]       ,							device=a__       ,							dtype=torch.float       ,							)
										# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
										# camembert.eval()
										# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

										self.assertTrue(torch.allclose(output[:, :3, :3]       ,							a__       ,							atol=1e-4							)							)




