






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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_nllb import NllbTokenizer
else:
     lowerCamelCase__							=     None


lowerCamelCase__							=     logging.get_logger(__name__)


lowerCamelCase__							=     {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__							=     {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCamelCase__							=     {
    """facebook/nllb-large-en-ro""": 1024,
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
lowerCamelCase__							=     ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= VOCAB_FILES_NAMES
       lowerCamelCase							:				int 				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase							:				List[Any] 				= PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase							:				Any 				= ["input_ids", "attention_mask"]
       lowerCamelCase							:				Optional[Any] 				= NllbTokenizer

       lowerCamelCase							:				List[int] 				= []
       lowerCamelCase							:				List[int] 				= []



       def __init__(							self			,						a__=None			,						a__=None			,						a__="<s>"			,						a__="</s>"			,						a__="</s>"			,						a__="<s>"			,						a__="<unk>"			,						a__="<pad>"			,						a__="<mask>"			,						a__=None			,						a__=None			,						a__=None			,						a__=False			,						**a__			,						) ->      Dict:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						AddedToken(a__			,						lstrip=a__			,						rstrip=a__    ) if isinstance(a__			,						a__    ) else mask_token
            __snake_case			:List[Any]          =						legacy_behaviour
            super().__init__(
                vocab_file=a__			,						tokenizer_file=a__			,						bos_token=a__			,						eos_token=a__			,						sep_token=a__			,						cls_token=a__			,						unk_token=a__			,						pad_token=a__			,						mask_token=a__			,						src_lang=a__			,						tgt_lang=a__			,						additional_special_tokens=a__			,						legacy_behaviour=a__			,						**a__			,						)

            __snake_case			:Union[str, Any]          =						vocab_file
            __snake_case			:Dict          =						False if not self.vocab_file else True

            __snake_case			:Optional[Any]          =						FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 _additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in _additional_special_tokens]    )

            self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}    )
            __snake_case			:Dict          =						{
                lang_code: self.convert_tokens_to_ids(a__    ) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            __snake_case			:List[Any]          =						src_lang if src_lang is not None else """eng_Latn"""
            __snake_case			:str          =						self.convert_tokens_to_ids(self._src_lang    )
            __snake_case			:int          =						tgt_lang
            self.set_src_lang_special_tokens(self._src_lang    )



       @property
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            return self._src_lang



       @src_lang.setter
       def 					__lowercase						(							self			,						a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:List[str]          =						new_src_lang
            self.set_src_lang_special_tokens(self._src_lang    )



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






            '''simple docstring'''





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






            '''simple docstring'''





            __snake_case			:Tuple          =						[self.sep_token_id]
            __snake_case			:List[Any]          =						[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]



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						**a__    ) ->      Any:






            '''simple docstring'''





            if src_lang is None or tgt_lang is None:
                 raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""    )
            __snake_case			:Optional[int]          =						src_lang
            __snake_case			:Dict          =						self(a__			,						add_special_tokens=a__			,						return_tensors=a__			,						**a__    )
            __snake_case			:Tuple          =						self.convert_tokens_to_ids(a__    )
            __snake_case			:List[str]          =						tgt_lang_id
            return inputs



       def 					__lowercase						(							self			,						a__			,						a__ = "eng_Latn"			,						a__ = None			,						a__ = "fra_Latn"			,						**a__			,						) ->      BatchEncoding:






            '''simple docstring'''





            __snake_case			:List[str]          =						src_lang
            __snake_case			:Optional[int]          =						tgt_lang
            return super().prepare_seqaseq_batch(a__			,						a__			,						**a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            return self.set_src_lang_special_tokens(self.src_lang    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return self.set_tgt_lang_special_tokens(self.tgt_lang    )



       def 					__lowercase						(							self			,						a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Dict          =						self.convert_tokens_to_ids(a__    )

            if self.legacy_behaviour:
                 __snake_case			:List[Any]          =						[]
                 __snake_case			:str          =						[self.eos_token_id, self.cur_lang_code]
            else:
                 __snake_case			:Union[str, Any]          =						[self.cur_lang_code]
                 __snake_case			:Optional[Any]          =						[self.eos_token_id]

            __snake_case			:str          =						self.convert_ids_to_tokens(self.prefix_tokens    )
            __snake_case			:Dict          =						self.convert_ids_to_tokens(self.suffix_tokens    )

            __snake_case			:Optional[int]          =						processors.TemplateProcessing(
                single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str			,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str			,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,						self.prefix_tokens + self.suffix_tokens    )    )			,						)



       def 					__lowercase						(							self			,						a__    ) ->      None:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.convert_tokens_to_ids(a__    )
            if self.legacy_behaviour:
                 __snake_case			:str          =						[]
                 __snake_case			:List[Any]          =						[self.eos_token_id, self.cur_lang_code]
            else:
                 __snake_case			:List[Any]          =						[self.cur_lang_code]
                 __snake_case			:Dict          =						[self.eos_token_id]

            __snake_case			:Optional[int]          =						self.convert_ids_to_tokens(self.prefix_tokens    )
            __snake_case			:int          =						self.convert_ids_to_tokens(self.suffix_tokens    )

            __snake_case			:List[Any]          =						processors.TemplateProcessing(
                single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str			,						pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str			,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,						self.prefix_tokens + self.suffix_tokens    )    )			,						)



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






            '''simple docstring'''





            if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""    )

            if not os.path.isdir(a__    ):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''    )
                 return
            __snake_case			:Any          =						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__    ):
                 copyfile(self.vocab_file			,						a__    )

            return (out_vocab_file,)










import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowerCamelCase__							=     {"""LayoutLMv2Config""", """LayoutLMv3Config"""}




@is_pipeline_test
class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				List[Any] 				= MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
       lowerCamelCase							:				Tuple 				= TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

       if model_mapping is not None:
              lowerCamelCase							:				List[Any] 				= {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
       if tf_model_mapping is not None:
              lowerCamelCase							:				List[str] 				= {
                  config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
              }



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






            '''simple docstring'''





            __snake_case			:Union[str, Any]          =						ZeroShotClassificationPipeline(
                model=a__			,						tokenizer=a__			,						candidate_labels=["""polics""", """health"""]    )
            return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Tuple          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels="""politics"""    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            # No kwarg
            __snake_case			:Optional[int]          =						classifier("""Who are you voting for in 2020?"""			,						["""politics"""]    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            __snake_case			:Tuple          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels=["""politics"""]    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            __snake_case			:List[str]          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels="""politics, public health"""    )
            self.assertEqual(
                a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}    )
            self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]    )    )			,						1.0    )

            __snake_case			:Optional[Any]          =						classifier("""Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health"""]    )
            self.assertEqual(
                a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}    )
            self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]    )    )			,						1.0    )

            __snake_case			:List[str]          =						classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels="""politics"""			,						hypothesis_template="""This text is about {}"""    )
            self.assertEqual(a__			,						{"""sequence""": ANY(a__    ), """labels""": [ANY(a__    )], """scores""": [ANY(a__    )]}    )

            # https://github.com/huggingface/transformers/issues/13846
            __snake_case			:Union[str, Any]          =						classifier(["""I am happy"""]			,						["""positive""", """negative"""]    )
            self.assertEqual(
                a__			,						[
                    {"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}
                    for i in range(1    )
                ]			,						)
            __snake_case			:Tuple          =						classifier(["""I am happy""", """I am sad"""]			,						["""positive""", """negative"""]    )
            self.assertEqual(
                a__			,						[
                    {"""sequence""": ANY(a__    ), """labels""": [ANY(a__    ), ANY(a__    )], """scores""": [ANY(a__    ), ANY(a__    )]}
                    for i in range(2    )
                ]			,						)

            with self.assertRaises(a__    ):
                 classifier(""""""			,						candidate_labels="""politics"""    )

            with self.assertRaises(a__    ):
                 classifier(a__			,						candidate_labels="""politics"""    )

            with self.assertRaises(a__    ):
                 classifier("""Who are you voting for in 2020?"""			,						candidate_labels=""""""    )

            with self.assertRaises(a__    ):
                 classifier("""Who are you voting for in 2020?"""			,						candidate_labels=a__    )

            with self.assertRaises(a__    ):
                 classifier(
                     """Who are you voting for in 2020?"""			,						candidate_labels="""politics"""			,						hypothesis_template="""Not formatting template"""			,						)

            with self.assertRaises(a__    ):
                 classifier(
                     """Who are you voting for in 2020?"""			,						candidate_labels="""politics"""			,						hypothesis_template=a__			,						)

            self.run_entailment_id(a__    )



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






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						zero_shot_classifier.model.config
            __snake_case			:Optional[Any]          =						config.labelaid
            __snake_case			:Dict          =						zero_shot_classifier.entailment_id

            __snake_case			:List[Any]          =						{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
            self.assertEqual(zero_shot_classifier.entailment_id			,						-1    )

            __snake_case			:Optional[int]          =						{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
            self.assertEqual(zero_shot_classifier.entailment_id			,						0    )

            __snake_case			:List[Any]          =						{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
            self.assertEqual(zero_shot_classifier.entailment_id			,						0    )

            __snake_case			:str          =						{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
            self.assertEqual(zero_shot_classifier.entailment_id			,						2    )

            __snake_case			:Union[str, Any]          =						original_labelaid
            self.assertEqual(a__			,						zero_shot_classifier.entailment_id    )



       @require_torch
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:str          =						pipeline(
                """zero-shot-classification"""			,						model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""			,						framework="""pt"""			,						)
            # There was a regression in 4.10 for this
            # Adding a test so we don't make the mistake again.
            # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
            zero_shot_classifier(
                """Who are you voting for in 2020?""" * 1_00			,						candidate_labels=["""politics""", """public health""", """science"""]    )



       @require_torch
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Dict          =						pipeline(
                """zero-shot-classification"""			,						model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""			,						framework="""pt"""			,						)
            __snake_case			:Tuple          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""science""", """public health""", """politics"""],
                    """scores""": [0.3_33, 0.3_33, 0.3_33],
                }			,						)



       @require_tf
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]          =						pipeline(
                """zero-shot-classification"""			,						model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""			,						framework="""tf"""			,						)
            __snake_case			:List[Any]          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""science""", """public health""", """politics"""],
                    """scores""": [0.3_33, 0.3_33, 0.3_33],
                }			,						)



       @slow
       @require_torch
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						pipeline("""zero-shot-classification"""			,						model="""roberta-large-mnli"""			,						framework="""pt"""    )
            __snake_case			:Optional[int]          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""politics""", """public health""", """science"""],
                    """scores""": [0.9_76, 0.0_15, 0.0_09],
                }			,						)
            __snake_case			:Union[str, Any]          =						zero_shot_classifier(
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
                """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
                """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
                """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
                """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
                """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
                """ English-to-German translation task, improving over the existing best results, including ensembles by"""
                """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
                """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
                """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
                """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
                """ large and limited training data."""			,						candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]			,						multi_label=a__			,						)
            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": (
                        """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                        """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                        """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                        """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                        """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                        """ superior in quality while being more parallelizable and requiring significantly less time to"""
                        """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                        """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                        """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                        """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                        """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                        """ other tasks by applying it successfully to English constituency parsing both with large and"""
                        """ limited training data."""
                    ),
                    """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                    """scores""": [0.8_17, 0.7_13, 0.0_18, 0.0_18],
                }			,						)



       @slow
       @require_tf
       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Tuple          =						pipeline("""zero-shot-classification"""			,						model="""roberta-large-mnli"""			,						framework="""tf"""    )
            __snake_case			:Optional[int]          =						zero_shot_classifier(
                """Who are you voting for in 2020?"""			,						candidate_labels=["""politics""", """public health""", """science"""]    )

            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": """Who are you voting for in 2020?""",
                    """labels""": ["""politics""", """public health""", """science"""],
                    """scores""": [0.9_76, 0.0_15, 0.0_09],
                }			,						)
            __snake_case			:Union[str, Any]          =						zero_shot_classifier(
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
                """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
                """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
                """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
                """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
                """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
                """ English-to-German translation task, improving over the existing best results, including ensembles by"""
                """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
                """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
                """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
                """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
                """ large and limited training data."""			,						candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]			,						multi_label=a__			,						)
            self.assertEqual(
                nested_simplify(a__    )			,						{
                    """sequence""": (
                        """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                        """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                        """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                        """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                        """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                        """ superior in quality while being more parallelizable and requiring significantly less time to"""
                        """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                        """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                        """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                        """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                        """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                        """ other tasks by applying it successfully to English constituency parsing both with large and"""
                        """ limited training data."""
                    ),
                    """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
                    """scores""": [0.8_17, 0.7_13, 0.0_18, 0.0_18],
                }			,						)


