
import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     LongformerTokenizer
	snake_case_					    =     True
	snake_case_					    =     LongformerTokenizerFast
	snake_case_					    =     True






	def 				UpperCamelCase_  (       self					: Any ):
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__A   			= [
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					__A   			= dict(zip(A							,range(len(A ) ) ) )
					__A   			= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
					__A   			= {"unk_token": "<unk>"}

					__A   			= os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["vocab_file"] )
					__A   			= os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES["merges_file"] )
					with open(self.vocab_file							,"w"							,encoding="utf-8" ) as fp:
									fp.write(json.dumps(A ) + "\n" )
					with open(self.merges_file							,"w"							,encoding="utf-8" ) as fp:
									fp.write("\n".join(A ) )






	def 				UpperCamelCase_  (       self					: Tuple							,**A					: Dict ):
					kwargs.update(self.special_tokens_map )
					return self.tokenizer_class.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: int							,**A					: Optional[Any] ):
					kwargs.update(self.special_tokens_map )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,**A )






	def 				UpperCamelCase_  (       self					: Any							,A					: Tuple ):
					__A   			= "lower newer"
					__A   			= "lower newer"
					return input_text, output_text






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.tokenizer_class(self.vocab_file							,self.merges_file							,**self.special_tokens_map )
					__A   			= "lower newer"
					__A   			= ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
					__A   			= tokenizer.tokenize(A )  # , add_prefix_space=True)
					self.assertListEqual(A							,A )

					__A   			= tokens + [tokenizer.unk_token]
					__A   			= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(A )							,A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.get_tokenizer()

					self.assertListEqual(tokenizer.encode("Hello world!"							,add_special_tokens=A )							,[0, 3_14_14, 2_32, 3_28, 2] )
					self.assertListEqual(
					    tokenizer.encode("Hello world! cécé herlolip 418"							,add_special_tokens=A )							,[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]							,)






	@slow
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )

					__A   			= tokenizer.encode("sequence builders"							,add_special_tokens=A )
					__A   			= tokenizer.encode("multi-sequence build"							,add_special_tokens=A )

					__A   			= tokenizer.encode(
					    "sequence builders"							,add_special_tokens=A							,add_prefix_space=A )
					__A   			= tokenizer.encode(
					    "sequence builders"							,"multi-sequence build"							,add_special_tokens=A							,add_prefix_space=A )

					__A   			= tokenizer.build_inputs_with_special_tokens(A )
					__A   			= tokenizer.build_inputs_with_special_tokens(A							,A )

					assert encoded_sentence == encoded_text_from_decode
					assert encoded_pair == encoded_pair_from_decode






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.get_tokenizer()

					__A   			= "Encode this sequence."
					__A   			= tokenizer.byte_encoder[" ".encode("utf-8" )[0]]

					# Testing encoder arguments
					__A   			= tokenizer.encode(A							,add_special_tokens=A							,add_prefix_space=A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[0] )[0]
					self.assertNotEqual(A							,A )

					__A   			= tokenizer.encode(A							,add_special_tokens=A							,add_prefix_space=A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[0] )[0]
					self.assertEqual(A							,A )

					tokenizer.add_special_tokens({"bos_token": "<s>"} )
					__A   			= tokenizer.encode(A							,add_special_tokens=A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[1] )[0]
					self.assertNotEqual(A							,A )

					# Testing spaces after special tokens
					__A   			= "<mask>"
					tokenizer.add_special_tokens(
					    {"mask_token": AddedToken(A							,lstrip=A							,rstrip=A )} )  # mask token has a left space
					__A   			= tokenizer.convert_tokens_to_ids(A )

					__A   			= "Encode <mask> sequence"
					__A   			= "Encode <mask>sequence"

					__A   			= tokenizer.encode(A )
					__A   			= encoded.index(A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
					self.assertEqual(A							,A )

					__A   			= tokenizer.encode(A )
					__A   			= encoded.index(A )
					__A   			= tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
					self.assertNotEqual(A							,A )






	def 				UpperCamelCase_  (       self					: int ):
					pass






	def 				UpperCamelCase_  (       self					: str ):
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
													__A   			= self.rust_tokenizer_class.from_pretrained(A							,**A )
													__A   			= self.tokenizer_class.from_pretrained(A							,**A )
													__A   			= "A, <mask> AllenNLP sentence."
													__A   			= tokenizer_r.encode_plus(A							,add_special_tokens=A							,return_token_type_ids=A )
													__A   			= tokenizer_p.encode_plus(A							,add_special_tokens=A							,return_token_type_ids=A )

													# token_type_ids should put 0 everywhere
													self.assertEqual(sum(tokens_r["token_type_ids"] )							,sum(tokens_p["token_type_ids"] ) )

													# attention_mask should put 1 everywhere, so sum over length should be 1
													self.assertEqual(
													    sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] )							,sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] )							,)

													__A   			= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
													__A   			= tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )

													# Rust correctly handles the space before the mask while python doesnt
													self.assertSequenceEqual(tokens_p["input_ids"]							,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
													self.assertSequenceEqual(tokens_r["input_ids"]							,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

													self.assertSequenceEqual(
													    A							,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
													self.assertSequenceEqual(
													    A							,["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )






	def 				UpperCamelCase_  (       self					: int ):
					for trim_offsets, add_prefix_space in itertools.product([True, False]							,repeat=2 ):
									__A   			= self.rust_tokenizer_class.from_pretrained(
									    self.tmpdirname							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )

									__A   			= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
									__A   			= json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

									self.assertEqual(pre_tokenizer_state["add_prefix_space"]							,A )

									self.assertEqual(post_processor_state["add_prefix_space"]							,A )
									self.assertEqual(post_processor_state["trim_offsets"]							,A )







	def 				UpperCamelCase_  (       self					: Any ):
					# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
					# `trim_offsets`
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
													__A   			= "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
													__A   			= f'''{text_of_1_token} {text_of_1_token}'''

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ) + 1, len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ) + 1, len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ), len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(len(A ), len(A ) + 1 + len(A ))							,)

													__A   			= f''' {text}'''

													# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
													#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
													# )
													# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
													# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
													# self.assertEqual(
													#     encoding.offset_mapping[1],
													#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
													# )

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(1, 1 + len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(1 + len(A ) + 1, 1 + len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, 1 + len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(1 + len(A ), 1 + len(A ) + 1 + len(A ))							,)

													__A   			= self.rust_tokenizer_class.from_pretrained(
													    A							,use_fast=A							,add_prefix_space=A							,trim_offsets=A )
													__A   			= tokenizer_r(A							,return_offsets_mapping=A							,add_special_tokens=A )
													self.assertEqual(encoding.offset_mapping[0]							,(0, 1 + len(A )) )
													self.assertEqual(
													    encoding.offset_mapping[1]							,(1 + len(A ), 1 + len(A ) + 1 + len(A ))							,)


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


SCREAMING_SNAKE_CASE							:Optional[int]							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Optional[int]							 =				{'vocab_file': 'sentencepiece.bpe.model'}

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    }
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{
    'camembert-base': 512,
}

SCREAMING_SNAKE_CASE							:List[str]							 =				'▁'





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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






	def __init__(       self					: Optional[Any]							,A					: List[str]							,A					: List[Any]="<s>"							,A					: Tuple="</s>"							,A					: Any="</s>"							,A					: Optional[Any]="<s>"							,A					: Tuple="<unk>"							,A					: str="<pad>"							,A					: int="<mask>"							,A					: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]							,A					: Optional[Dict[str, Any]] = None							,**A					: Optional[Any]							,):
					# Mask token behave like a normal word, i.e. include the space before it
					__A   			= AddedToken(A							,lstrip=A							,rstrip=A ) if isinstance(A							,A ) else mask_token

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A							,eos_token=A							,unk_token=A							,sep_token=A							,cls_token=A							,pad_token=A							,mask_token=A							,additional_special_tokens=A							,sp_model_kwargs=self.sp_model_kwargs							,**A							,)
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )
					__A   			= vocab_file
					# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
					# sentencepiece vocabulary (this is the case for <s> and </s>
					__A   			= {"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
					__A   			= len(self.fairseq_tokens_to_ids )
					__A   			= len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
					__A   			= {v: k for k, v in self.fairseq_tokens_to_ids.items()}






	def 				UpperCamelCase_  (       self					: int							,A					: List[int]							,A					: Optional[List[int]] = None ):

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






	def 				UpperCamelCase_  (       self					: Dict							,A					: List[int]							,A					: Optional[List[int]] = None							,A					: bool = False ):
					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 None:
									return [1] + ([0] * len(A )) + [1]
					return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: List[int]							,A					: Optional[List[int]] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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 				UpperCamelCase_  (       self					: Dict ):
					return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {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					: Any							,A					: str ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Dict ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(A ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(A )






	def 				UpperCamelCase_  (       self					: Dict							,A					: Tuple ):
					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					: Optional[Any]							,A					: Dict ):
					__A   			= []
					__A   			= ""
					__A   			= 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
													__A   			= True
													__A   			= []
									else:
													current_sub_tokens.append(A )
													__A   			= False
					out_string += self.sp_model.decode(A )
					return out_string.strip()






	def __getstate__(       self					: Dict ):
					__A   			= self.__dict__.copy()
					__A   			= None
					return state






	def __setstate__(       self					: Union[str, Any]							,A					: Any ):
					__A   			= d

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

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







	def 				UpperCamelCase_  (       self					: Any							,A					: str							,A					: Optional[str] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= 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:
													__A   			= self.sp_model.serialized_model_proto()
													fi.write(A )

					return (out_vocab_file,)
