






import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=99			,						a__=13			,						a__=7			,						a__=9			,						a__=True			,						a__=True			,						a__=False			,						a__=32			,						a__=5			,						a__=4			,						a__=37			,						a__=8			,						a__=0.1			,						a__=0.0_02			,						a__=1			,						a__=0			,						a__=0			,						a__=None			,						a__=None			,						) ->      Dict:






            '''simple docstring'''





            __snake_case			:Tuple          =						parent
            __snake_case			:Union[str, Any]          =						batch_size
            __snake_case			:List[str]          =						encoder_seq_length
            __snake_case			:Any          =						decoder_seq_length
            # For common tests
            __snake_case			:str          =						self.decoder_seq_length
            __snake_case			:Dict          =						is_training
            __snake_case			:Optional[int]          =						use_attention_mask
            __snake_case			:Dict          =						use_labels
            __snake_case			:Any          =						vocab_size
            __snake_case			:Tuple          =						hidden_size
            __snake_case			:int          =						num_hidden_layers
            __snake_case			:Optional[int]          =						num_attention_heads
            __snake_case			:Optional[int]          =						d_ff
            __snake_case			:int          =						relative_attention_num_buckets
            __snake_case			:Optional[Any]          =						dropout_rate
            __snake_case			:Optional[Any]          =						initializer_factor
            __snake_case			:Optional[Any]          =						eos_token_id
            __snake_case			:Any          =						pad_token_id
            __snake_case			:Tuple          =						decoder_start_token_id
            __snake_case			:str          =						None
            __snake_case			:List[str]          =						decoder_layers



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






            '''simple docstring'''





            return TaConfig.from_pretrained("""google/umt5-base"""    )



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






            '''simple docstring'''





            if attention_mask is None:
                 __snake_case			:Optional[Any]          =						input_ids.ne(config.pad_token_id    )
            if decoder_attention_mask is None:
                 __snake_case			:Optional[int]          =						decoder_input_ids.ne(config.pad_token_id    )
            if head_mask is None:
                 __snake_case			:Any          =						torch.ones(config.num_hidden_layers			,						config.num_attention_heads			,						device=a__    )
            if decoder_head_mask is None:
                 __snake_case			:List[str]          =						torch.ones(config.num_decoder_layers			,						config.num_attention_heads			,						device=a__    )
            if cross_attn_head_mask is None:
                 __snake_case			:Tuple          =						torch.ones(
                     config.num_decoder_layers			,						config.num_attention_heads			,						device=a__    )
            return {
                "input_ids": input_ids,
                "decoder_input_ids": decoder_input_ids,
                "attention_mask": attention_mask,
                "decoder_attention_mask": decoder_attention_mask,
                "head_mask": head_mask,
                "decoder_head_mask": decoder_head_mask,
                "cross_attn_head_mask": cross_attn_head_mask,
            }



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






            '''simple docstring'''





            __snake_case			:List[str]          =						ids_tensor([self.batch_size, self.encoder_seq_length]			,						self.vocab_size    )
            __snake_case			:int          =						ids_tensor([self.batch_size, self.decoder_seq_length]			,						self.vocab_size    )

            # we need to clamp the input ids here to avoid having pad token in between
            # this is because for NllbMoe the position_ids are prepared such that
            # all pad tokens have pos id = 2 and rest are between 2..seq_length
            # and the seq_length here is seq_length - num_pad_tokens
            # but when using past, there is no way of knowing if the past input ids had
            # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
            # position_ids being off by num_pad_tokens in past input
            __snake_case			:Union[str, Any]          =						input_ids.clamp(self.pad_token_id + 1    )
            __snake_case			:int          =						decoder_input_ids.clamp(self.pad_token_id + 1    )

            __snake_case			:List[str]          =						self.get_config()
            __snake_case			:List[str]          =						config.num_attention_heads
            __snake_case			:int          =						self.prepare_inputs_dict(a__			,						a__			,						a__    )
            return config, input_dict



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:int          =						self.prepare_config_and_inputs()
            return config, inputs_dict



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            return TaConfig(
                vocab_size=1_66			,						d_model=self.hidden_size			,						d_ff=self.d_ff			,						d_kv=self.hidden_size // self.num_attention_heads			,						num_layers=self.num_hidden_layers			,						num_decoder_layers=self.decoder_layers			,						num_heads=self.num_attention_heads			,						relative_attention_num_buckets=self.relative_attention_num_buckets			,						dropout_rate=self.dropout_rate			,						initializer_factor=self.initializer_factor			,						eos_token_id=self.eos_token_id			,						bos_token_id=self.pad_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.decoder_start_token_id			,						)



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






            '''simple docstring'''





            return TaConfig(
                vocab_size=self.vocab_size			,						d_model=self.hidden_size			,						d_ff=self.d_ff			,						d_kv=self.hidden_size // self.num_attention_heads			,						num_layers=self.num_hidden_layers			,						num_decoder_layers=self.decoder_layers			,						num_heads=self.num_attention_heads			,						relative_attention_num_buckets=self.relative_attention_num_buckets			,						dropout_rate=self.dropout_rate			,						initializer_factor=self.initializer_factor			,						eos_token_id=self.eos_token_id			,						bos_token_id=self.pad_token_id			,						pad_token_id=self.pad_token_id			,						decoder_start_token_id=self.decoder_start_token_id			,						)



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






            '''simple docstring'''





            __snake_case			:Optional[int]          =						UMTaModel(config=a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:int          =						model(
                input_ids=a__			,						decoder_input_ids=a__			,						attention_mask=a__			,						decoder_attention_mask=a__			,						)
            __snake_case			:str          =						model(input_ids=a__			,						decoder_input_ids=a__    )
            __snake_case			:Tuple          =						result.last_hidden_state
            __snake_case			:str          =						result.past_key_values
            __snake_case			:Dict          =						result.encoder_last_hidden_state

            self.parent.assertEqual(encoder_output.size()			,						(self.batch_size, self.encoder_seq_length, self.hidden_size)    )
            self.parent.assertEqual(decoder_output.size()			,						(self.batch_size, self.decoder_seq_length, self.hidden_size)    )
            # There should be `num_layers` key value embeddings stored in decoder_past
            self.parent.assertEqual(len(a__    )			,						config.num_layers    )
            # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
            self.parent.assertEqual(len(decoder_past[0]    )			,						4    )



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






            '''simple docstring'''





            __snake_case			:Any          =						UMTaModel(config=a__    ).get_decoder().to(a__    ).eval()
            # first forward pass
            __snake_case			:Optional[Any]          =						model(a__			,						use_cache=a__    )
            __snake_case			:Any          =						model(a__    )
            __snake_case			:Optional[Any]          =						model(a__			,						use_cache=a__    )

            self.parent.assertTrue(len(a__    ) == len(a__    )    )
            self.parent.assertTrue(len(a__    ) == len(a__    ) + 1    )

            __snake_case			:Optional[Any]          =						outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __snake_case			:Optional[Any]          =						ids_tensor((self.batch_size, 1)			,						config.vocab_size    )

            # append to next input_ids and
            __snake_case			:List[Any]          =						torch.cat([input_ids, next_tokens]			,						dim=-1    )

            __snake_case			:Dict          =						model(a__    )["""last_hidden_state"""]
            __snake_case			:Any          =						model(a__			,						past_key_values=a__    )["""last_hidden_state"""]

            # select random slice
            __snake_case			:Optional[int]          =						ids_tensor((1,)			,						output_from_past.shape[-1]    ).item()
            __snake_case			:List[str]          =						output_from_no_past[:, -1, random_slice_idx].detach()
            __snake_case			:Tuple          =						output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(a__			,						a__			,						atol=1e-3    )    )



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






            '''simple docstring'''





            __snake_case			:Optional[int]          =						UMTaModel(config=a__    ).to(a__    ).half().eval()
            __snake_case			:Optional[int]          =						model(**a__    )["""last_hidden_state"""]
            self.parent.assertFalse(torch.isnan(a__    ).any().item()    )




@require_torch
class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= (
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       lowerCamelCase							:				Optional[Any] 				= (UMTaForConditionalGeneration,) if is_torch_available() else ()
       lowerCamelCase							:				Tuple 				= (
           {
               "conversational": UMTaForConditionalGeneration,
               "feature-extraction": UMTaModel,
               "summarization": UMTaForConditionalGeneration,
               "text2text-generation": UMTaForConditionalGeneration,
               "translation": UMTaForConditionalGeneration,
               "question-answering": UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       lowerCamelCase							:				int 				= True
       lowerCamelCase							:				Optional[int] 				= False
       lowerCamelCase							:				str 				= False
       lowerCamelCase							:				Optional[Any] 				= True
       lowerCamelCase							:				Optional[Any] 				= True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       lowerCamelCase							:				List[Any] 				= [0.8, 0.9]



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:List[str]          =						UMTaModelTester(self    )



       @unittest.skip("""Test has a segmentation fault on torch 1.8.0"""    )
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.model_tester.prepare_config_and_inputs()
            __snake_case			:Dict          =						UMTaModel(config_and_inputs[0]    ).to(a__    )
            with tempfile.TemporaryDirectory() as tmpdirname:
                 torch.onnx.export(
                     a__			,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])			,						F'''{tmpdirname}/t5_test.onnx'''			,						export_params=a__			,						opset_version=9			,						input_names=["""input_ids""", """decoder_input_ids"""]			,						)



       @unittest.skipIf(torch_device == """cpu"""			,						"""Cant do half precision"""    )
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model_fpaa_forward(*a__    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:List[Any]          =						["""encoder_attentions""", """decoder_attentions""", """cross_attentions"""]
            __snake_case			:str          =						self.model_tester.prepare_config_and_inputs()
            __snake_case			:Dict          =						config_and_inputs[0]
            __snake_case			:Optional[Any]          =						UMTaForConditionalGeneration(a__    ).eval()
            model.to(a__    )

            __snake_case			:str          =						{
                """head_mask""": torch.zeros(config.num_layers			,						config.num_heads			,						device=a__    ),
                """decoder_head_mask""": torch.zeros(config.num_decoder_layers			,						config.num_heads			,						device=a__    ),
                """cross_attn_head_mask""": torch.zeros(config.num_decoder_layers			,						config.num_heads			,						device=a__    ),
            }

            for attn_name, (name, mask) in zip(a__			,						head_masking.items()    ):
                 __snake_case			:Union[str, Any]          =						{name: mask}
                 # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                 if name == "head_mask":
                      __snake_case			:Tuple          =						torch.ones(
                          config.num_decoder_layers			,						config.num_heads			,						device=a__    )

                 __snake_case			:List[str]          =						model.generate(
                     config_and_inputs[1]["""input_ids"""]			,						num_beams=1			,						max_length=3			,						output_attentions=a__			,						return_dict_in_generate=a__			,						**a__			,						)
                 # We check the state of decoder_attentions and cross_attentions just from the last step
                 __snake_case			:List[str]          =						out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                 self.assertEqual(sum([w.sum().item() for w in attn_weights]    )			,						0.0    )



       @unittest.skip("""Does not work on the tiny model as we keep hitting edge cases."""    )
       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass




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

       '''simple docstring'''



       @slow
       @unittest.skip(
           """Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"""    )
       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:str          =						UMTaForConditionalGeneration.from_pretrained("""google/umt5-small"""			,						return_dict=a__    ).to(a__    )
            __snake_case			:str          =						AutoTokenizer.from_pretrained("""google/umt5-small"""			,						use_fast=a__			,						legacy=a__    )
            __snake_case			:List[Any]          =						[
                """Bonjour monsieur <extra_id_0> bien <extra_id_1>.""",
                """No se como puedo <extra_id_0>.""",
                """This is the reason why we <extra_id_0> them.""",
                """The <extra_id_0> walks in <extra_id_1>, seats""",
                """A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.""",
            ]
            __snake_case			:Any          =						tokenizer(a__			,						return_tensors="""pt"""			,						padding=a__    ).input_ids
            # fmt: off
            __snake_case			:str          =						torch.tensor(
                [
                    [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                    [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                    [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
                ]    )
            # fmt: on
            torch.testing.assert_allclose(a__			,						a__    )

            __snake_case			:int          =						model.generate(input_ids.to(a__    )    )
            __snake_case			:str          =						[
                """<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>""",
                """<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
                """<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>""",
            ]
            __snake_case			:str          =						tokenizer.batch_decode(a__    )
            self.assertEqual(a__			,						a__    )










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],
                }			,						)


