
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 					lowerCamelCase		(  A_							,			unittest.TestCase  ):
     UpperCAmelCase__    :							Optional[Any]									=		LongformerTokenizer
     UpperCAmelCase__    :							Dict									=		True
     UpperCAmelCase__    :							int									=		LongformerTokenizerFast
     UpperCAmelCase__    :							List[Any]									=		True





     def 		UpperCAmelCase(self				:   List[str]       )						->						Optional[Any]:
         super().setUp()

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

         snake_case								= os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES["vocab_file"]       )
         snake_case								= 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				:   str    ,   **_A				:   Dict       )						->						Tuple:
         kwargs.update(self.special_tokens_map       )
         return self.tokenizer_class.from_pretrained(self.tmpdirname    ,   **_A       )





     def 		UpperCAmelCase(self				:   Optional[int]    ,   **_A				:   Dict       )						->						Optional[Any]:
         kwargs.update(self.special_tokens_map       )
         return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,   **_A       )





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   Tuple       )						->						Any:
         snake_case								= "lower newer"
         snake_case								= "lower newer"
         return input_text, output_text





     def 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case								= self.tokenizer_class(self.vocab_file    ,   self.merges_file    ,   **self.special_tokens_map       )
         snake_case								= "lower newer"
         snake_case								= ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
         snake_case								= tokenizer.tokenize(_A       )  # , add_prefix_space=True)
         self.assertListEqual(_A    ,   _A       )

         snake_case								= tokens + [tokenizer.unk_token]
         snake_case								= [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A       )    ,   _A       )





     def 		UpperCAmelCase(self				:   Dict       )						->						str:
         snake_case								= self.get_tokenizer()

         self.assertListEqual(tokenizer.encode("Hello world!"    ,   add_special_tokens=_A       )    ,   [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]       )
         self.assertListEqual(
             tokenizer.encode("Hello world! cécé herlolip 418"    ,   add_special_tokens=_A       )    ,   [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]    ,   )





     @slow
     def 		UpperCAmelCase(self				:   List[Any]       )						->						str:
         snake_case								= self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"       )

         snake_case								= tokenizer.encode("sequence builders"    ,   add_special_tokens=_A       )
         snake_case								= tokenizer.encode("multi-sequence build"    ,   add_special_tokens=_A       )

         snake_case								= tokenizer.encode(
             "sequence builders"    ,   add_special_tokens=_A    ,   add_prefix_space=_A       )
         snake_case								= tokenizer.encode(
             "sequence builders"    ,   "multi-sequence build"    ,   add_special_tokens=_A    ,   add_prefix_space=_A       )

         snake_case								= tokenizer.build_inputs_with_special_tokens(_A       )
         snake_case								= 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[int]       )						->						Any:
         snake_case								= self.get_tokenizer()

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

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

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

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

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

         snake_case								= "Encode <mask> sequence"
         snake_case								= "Encode <mask>sequence"

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

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





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						Tuple:
         pass





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Tuple:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'       ):
                 snake_case								= self.rust_tokenizer_class.from_pretrained(_A    ,   **_A       )
                 snake_case								= self.tokenizer_class.from_pretrained(_A    ,   **_A       )
                 snake_case								= "A, <mask> AllenNLP sentence."
                 snake_case								= tokenizer_r.encode_plus(_A    ,   add_special_tokens=_A    ,   return_token_type_ids=_A       )
                 snake_case								= 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"]       )    ,   )

                 snake_case								= tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]       )
                 snake_case								= 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_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]       )
                 self.assertSequenceEqual(tokens_r["input_ids"]    ,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 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				:   List[str]       )						->						Any:
         for trim_offsets, add_prefix_space in itertools.product([True, False]    ,   repeat=2       ):
             snake_case								= self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )

             snake_case								= json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()       )
             snake_case								= 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				:   List[str]       )						->						Optional[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})'       ):
                 snake_case								= "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                 snake_case								= f'{text_of_1_token} {text_of_1_token}'

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )

                 snake_case								= 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)),
                 # )

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )

                 snake_case								= self.rust_tokenizer_class.from_pretrained(
                     _A    ,   use_fast=_A    ,   add_prefix_space=_A    ,   trim_offsets=_A       )
                 snake_case								= 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       ))    ,   )



from . import (
    albert,
    align,
    altclip,
    audio_spectrogram_transformer,
    auto,
    autoformer,
    bark,
    bart,
    barthez,
    bartpho,
    beit,
    bert,
    bert_generation,
    bert_japanese,
    bertweet,
    big_bird,
    bigbird_pegasus,
    biogpt,
    bit,
    blenderbot,
    blenderbot_small,
    blip,
    blip_a,
    bloom,
    bridgetower,
    byta,
    camembert,
    canine,
    chinese_clip,
    clap,
    clip,
    clipseg,
    codegen,
    conditional_detr,
    convbert,
    convnext,
    convnextva,
    cpm,
    cpmant,
    ctrl,
    cvt,
    dataavec,
    deberta,
    deberta_va,
    decision_transformer,
    deformable_detr,
    deit,
    deprecated,
    deta,
    detr,
    dialogpt,
    dinat,
    distilbert,
    dit,
    donut,
    dpr,
    dpt,
    efficientformer,
    efficientnet,
    electra,
    encodec,
    encoder_decoder,
    ernie,
    ernie_m,
    esm,
    falcon,
    flaubert,
    flava,
    fnet,
    focalnet,
    fsmt,
    funnel,
    git,
    glpn,
    gpta,
    gpt_bigcode,
    gpt_neo,
    gpt_neox,
    gpt_neox_japanese,
    gpt_swa,
    gptj,
    gptsan_japanese,
    graphormer,
    groupvit,
    herbert,
    hubert,
    ibert,
    imagegpt,
    informer,
    instructblip,
    jukebox,
    layoutlm,
    layoutlmva,
    layoutlmva,
    layoutxlm,
    led,
    levit,
    lilt,
    llama,
    longformer,
    longta,
    luke,
    lxmert,
    mam_aaa,
    marian,
    markuplm,
    maskaformer,
    maskformer,
    mbart,
    mbartaa,
    mega,
    megatron_bert,
    megatron_gpta,
    mgp_str,
    mluke,
    mobilebert,
    mobilenet_va,
    mobilenet_va,
    mobilevit,
    mobilevitva,
    mpnet,
    mra,
    mta,
    musicgen,
    mvp,
    nat,
    nezha,
    nllb,
    nllb_moe,
    nystromformer,
    oneformer,
    open_llama,
    openai,
    opt,
    owlvit,
    pegasus,
    pegasus_x,
    perceiver,
    phobert,
    pixastruct,
    plbart,
    poolformer,
    prophetnet,
    qdqbert,
    rag,
    realm,
    reformer,
    regnet,
    rembert,
    resnet,
    roberta,
    roberta_prelayernorm,
    roc_bert,
    roformer,
    rwkv,
    sam,
    segformer,
    sew,
    sew_d,
    speech_encoder_decoder,
    speech_to_text,
    speech_to_text_a,
    speechta,
    splinter,
    squeezebert,
    swiftformer,
    swin,
    swinasr,
    swinva,
    switch_transformers,
    ta,
    table_transformer,
    tapas,
    time_series_transformer,
    timesformer,
    timm_backbone,
    transfo_xl,
    trocr,
    tvlt,
    umta,
    unispeech,
    unispeech_sat,
    upernet,
    videomae,
    vilt,
    vision_encoder_decoder,
    vision_text_dual_encoder,
    visual_bert,
    vit,
    vit_hybrid,
    vit_mae,
    vit_msn,
    vivit,
    wavaveca,
    wavaveca_conformer,
    wavaveca_phoneme,
    wavaveca_with_lm,
    wavlm,
    whisper,
    x_clip,
    xglm,
    xlm,
    xlm_prophetnet,
    xlm_roberta,
    xlm_roberta_xl,
    xlnet,
    xmod,
    yolos,
    yoso,
)

