
import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertWithLMHeadModel,
		)
		from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class       __A(				UpperCAmelCase       ):

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]=1_3   ,					__UpperCamelCase					:     Optional[Any]=7   ,					__UpperCamelCase					:     Optional[int]=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     List[str]=True   ,					__UpperCamelCase					:     int=True   ,					__UpperCamelCase					:     Any=False   ,					__UpperCamelCase					:     List[str]=False   ,					__UpperCamelCase					:     List[Any]=False   ,					__UpperCamelCase					:     Optional[Any]=2   ,					__UpperCamelCase					:     Tuple=9_9   ,					__UpperCamelCase					:     Optional[int]=0   ,					__UpperCamelCase					:     str=3_2   ,					__UpperCamelCase					:     List[str]=5   ,					__UpperCamelCase					:     Union[str, Any]=4   ,					__UpperCamelCase					:     Optional[Any]=0.1   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     Any=5_1_2   ,					__UpperCamelCase					:     Any=1_2   ,					__UpperCamelCase					:     Tuple=2   ,					__UpperCamelCase					:     str=0.02   ,					__UpperCamelCase					:     Any=3   ,					__UpperCamelCase					:     Union[str, Any]=4   ,					__UpperCamelCase					:     Optional[Any]="last"   ,					__UpperCamelCase					:     int=None   ,					__UpperCamelCase					:     Optional[Any]=None   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					seq_length
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_input_lengths
												lowerCamelCase_							  =					use_token_type_ids
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					gelu_activation
												lowerCamelCase_							  =					sinusoidal_embeddings
												lowerCamelCase_							  =					causal
												lowerCamelCase_							  =					asm
												lowerCamelCase_							  =					n_langs
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					n_special
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					type_vocab_size
												lowerCamelCase_							  =					type_sequence_label_size
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					num_labels
												lowerCamelCase_							  =					num_choices
												lowerCamelCase_							  =					summary_type
												lowerCamelCase_							  =					use_proj
												lowerCamelCase_							  =					scope

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.vocab_size   )
												lowerCamelCase_							  =					random_attention_mask([self.batch_size, self.seq_length]   )

												lowerCamelCase_							  =					None
												if self.use_input_lengths:
																			lowerCamelCase_							  =					(
																			    ids_tensor([self.batch_size]   ,					vocab_size=2   ) + self.seq_length - 2
																			)  # small variation of seq_length

												lowerCamelCase_							  =					None
												if self.use_token_type_ids:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.n_langs   )

												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					None
												lowerCamelCase_							  =					None
												if self.use_labels:
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.type_sequence_label_size   )
																			lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length]   ,					self.num_labels   )
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					2   ).float()
																			lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.num_choices   )

												lowerCamelCase_							  =					self.get_config()

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    input_lengths,
												    sequence_labels,
												    token_labels,
												    is_impossible_labels,
												    choice_labels,
												    input_mask,
												)

					def    lowercase__      (	self					:     str   ):
												return FlaubertConfig(
												    vocab_size=self.vocab_size   ,					n_special=self.n_special   ,					emb_dim=self.hidden_size   ,					n_layers=self.num_hidden_layers   ,					n_heads=self.num_attention_heads   ,					dropout=self.hidden_dropout_prob   ,					attention_dropout=self.attention_probs_dropout_prob   ,					gelu_activation=self.gelu_activation   ,					sinusoidal_embeddings=self.sinusoidal_embeddings   ,					asm=self.asm   ,					causal=self.causal   ,					n_langs=self.n_langs   ,					max_position_embeddings=self.max_position_embeddings   ,					initializer_range=self.initializer_range   ,					summary_type=self.summary_type   ,					use_proj=self.use_proj   ,					)

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     int   ,					):
												lowerCamelCase_							  =					FlaubertModel(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					lengths=__UpperCamelCase   ,					langs=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					langs=__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   )
												self.parent.assertEqual(result.last_hidden_state.shape   ,					(self.batch_size, self.seq_length, self.hidden_size)   )

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[int]   ,					):
												lowerCamelCase_							  =					FlaubertWithLMHeadModel(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()

												lowerCamelCase_							  =					model(__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertEqual(result.loss.shape   ,					()   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     int   ,					):
												lowerCamelCase_							  =					FlaubertForQuestionAnsweringSimple(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()

												lowerCamelCase_							  =					model(__UpperCamelCase   )

												lowerCamelCase_							  =					model(__UpperCamelCase   ,					start_positions=__UpperCamelCase   ,					end_positions=__UpperCamelCase   )
												self.parent.assertEqual(result.start_logits.shape   ,					(self.batch_size, self.seq_length)   )
												self.parent.assertEqual(result.end_logits.shape   ,					(self.batch_size, self.seq_length)   )

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					):
												lowerCamelCase_							  =					FlaubertForQuestionAnswering(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()

												lowerCamelCase_							  =					model(__UpperCamelCase   )

												lowerCamelCase_							  =					model(
												    __UpperCamelCase   ,					start_positions=__UpperCamelCase   ,					end_positions=__UpperCamelCase   ,					cls_index=__UpperCamelCase   ,					is_impossible=__UpperCamelCase   ,					p_mask=__UpperCamelCase   ,					)

												lowerCamelCase_							  =					model(
												    __UpperCamelCase   ,					start_positions=__UpperCamelCase   ,					end_positions=__UpperCamelCase   ,					cls_index=__UpperCamelCase   ,					is_impossible=__UpperCamelCase   ,					)

												((lowerCamelCase_)  ,   )							  =					result_with_labels.to_tuple()

												lowerCamelCase_							  =					model(__UpperCamelCase   ,					start_positions=__UpperCamelCase   ,					end_positions=__UpperCamelCase   )

												((lowerCamelCase_)  ,   )							  =					result_with_labels.to_tuple()

												self.parent.assertEqual(result_with_labels.loss.shape   ,					()   )
												self.parent.assertEqual(result.start_top_log_probs.shape   ,					(self.batch_size, model.config.start_n_top)   )
												self.parent.assertEqual(result.start_top_index.shape   ,					(self.batch_size, model.config.start_n_top)   )
												self.parent.assertEqual(
												    result.end_top_log_probs.shape   ,					(self.batch_size, model.config.start_n_top * model.config.end_n_top)   )
												self.parent.assertEqual(
												    result.end_top_index.shape   ,					(self.batch_size, model.config.start_n_top * model.config.end_n_top)   )
												self.parent.assertEqual(result.cls_logits.shape   ,					(self.batch_size,)   )

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     int   ,					):
												lowerCamelCase_							  =					FlaubertForSequenceClassification(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()

												lowerCamelCase_							  =					model(__UpperCamelCase   )
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					labels=__UpperCamelCase   )

												self.parent.assertEqual(result.loss.shape   ,					()   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.type_sequence_label_size)   )

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ,					__UpperCamelCase					:     Optional[Any]   ,					):
												lowerCamelCase_							  =					self.num_labels
												lowerCamelCase_							  =					FlaubertForTokenClassification(__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()

												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					labels=__UpperCamelCase   )
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.num_labels)   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]   ,					):
												lowerCamelCase_							  =					self.num_choices
												lowerCamelCase_							  =					FlaubertForMultipleChoice(config=__UpperCamelCase   )
												model.to(__UpperCamelCase   )
												model.eval()
												lowerCamelCase_							  =					input_ids.unsqueeze(1   ).expand(-1   ,					self.num_choices   ,					-1   ).contiguous()
												lowerCamelCase_							  =					token_type_ids.unsqueeze(1   ).expand(-1   ,					self.num_choices   ,					-1   ).contiguous()
												lowerCamelCase_							  =					input_mask.unsqueeze(1   ).expand(-1   ,					self.num_choices   ,					-1   ).contiguous()
												lowerCamelCase_							  =					model(
												    __UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					token_type_ids=__UpperCamelCase   ,					labels=__UpperCamelCase   ,					)
												self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_choices)   )



					def    lowercase__      (	self					:     str   ):
												lowerCamelCase_							  =					self.prepare_config_and_inputs()
												(
												    (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   (
												    lowerCamelCase_
												)  ,   
												)							  =					config_and_inputs
												lowerCamelCase_							  =					{
												    """input_ids""": input_ids,
												    """token_type_ids""": token_type_ids,
												    """lengths""": input_lengths,
												    """attention_mask""": input_mask,
												}
												return config, inputs_dict





@require_torch
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE			    =    (
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[Any]   ):
												if (
												    pipeline_test_casse_name == "QAPipelineTests"
												    and tokenizer_name is not None
												    and not tokenizer_name.endswith("""Fast"""   )
												):
																			# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																			# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																			# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																			return True

												return False

					def    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     Optional[Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Any=False   ):
												lowerCamelCase_							  =					super()._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   ,					return_labels=__UpperCamelCase   )

												if return_labels:
																			if model_class.__name__ == "FlaubertForQuestionAnswering":
																										lowerCamelCase_							  =					torch.zeros(
																										    self.model_tester.batch_size   ,					dtype=torch.long   ,					device=__UpperCamelCase   )
																										lowerCamelCase_							  =					torch.zeros(
																										    self.model_tester.batch_size   ,					dtype=torch.long   ,					device=__UpperCamelCase   )

												return inputs_dict

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					FlaubertModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   ,					emb_dim=3_7   )

					def    lowercase__      (	self					:     int   ):
												self.config_tester.run_common_tests()

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_model(*__UpperCamelCase   )

					def    lowercase__      (	self					:     List[Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_lm_head(*__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_simple_qa(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_qa(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_sequence_classif(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_token_classif(*__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_flaubert_multiple_choice(*__UpperCamelCase   )

					@slow
					def    lowercase__      (	self					:     List[str]   ):
												for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_							  =					FlaubertModel.from_pretrained(__UpperCamelCase   )
																			self.assertIsNotNone(__UpperCamelCase   )



					@slow
					@require_torch_gpu
					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes:
																			# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
																			if model_class == FlaubertForMultipleChoice:
																										return

																			lowerCamelCase_							  =					True
																			lowerCamelCase_							  =					model_class(config=__UpperCamelCase   )

																			lowerCamelCase_							  =					self._prepare_for_class(__UpperCamelCase   ,					__UpperCamelCase   )
																			lowerCamelCase_							  =					torch.jit.trace(
																			    __UpperCamelCase   ,					(inputs_dict["""input_ids"""].to("""cpu"""   ), inputs_dict["""attention_mask"""].to("""cpu"""   ))   )

																			with tempfile.TemporaryDirectory() as tmp:
																										torch.jit.save(__UpperCamelCase   ,					os.path.join(__UpperCamelCase   ,					"""traced_model.pt"""   )   )
																										lowerCamelCase_							  =					torch.jit.load(os.path.join(__UpperCamelCase   ,					"""traced_model.pt"""   )   ,					map_location=__UpperCamelCase   )
																										loaded(inputs_dict["""input_ids"""].to(__UpperCamelCase   )   ,					inputs_dict["""attention_mask"""].to(__UpperCamelCase   )   )





@require_torch
class       __A(				unittest.TestCase       ):

					@slow
					def    lowercase__      (	self					:     List[str]   ):
												lowerCamelCase_							  =					FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""   )
												lowerCamelCase_							  =					torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]   )
												with torch.no_grad():
																			lowerCamelCase_							  =					model(__UpperCamelCase   )[0]
												lowerCamelCase_							  =					torch.Size((1, 1_1, 7_6_8)   )
												self.assertEqual(output.shape   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					torch.tensor(
												    [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]   )

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







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''facebook/bart-large-mnli'''
					SCREAMING_SNAKE_CASE			    =    (
					    '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
					    '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
					    '''It returns the most likely label in the list of provided `labels` for the input text.'''
					)
					SCREAMING_SNAKE_CASE			    =    '''text_classifier'''
					SCREAMING_SNAKE_CASE			    =    AutoTokenizer
					SCREAMING_SNAKE_CASE			    =    AutoModelForSequenceClassification

					SCREAMING_SNAKE_CASE			    =    ['''text''', ['''text''']]
					SCREAMING_SNAKE_CASE			    =    ['''text''']

					def    lowercase__      (	self					:     Union[str, Any]   ):
												super().setup()
												lowerCamelCase_							  =					self.model.config
												lowerCamelCase_							  =					-1
												for idx, label in config.idalabel.items():
																			if label.lower().startswith("""entail"""   ):
																										lowerCamelCase_							  =					int(__UpperCamelCase   )
												if self.entailment_id == -1:
																			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					labels
												return self.pre_processor(
												    [text] * len(__UpperCamelCase   )   ,					[F'''This example is {label}''' for label in labels]   ,					return_tensors="""pt"""   ,					padding="""max_length"""   ,					)



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					outputs.logits
												lowerCamelCase_							  =					torch.argmax(logits[:, 2]   ).item()
												return self._labels[label_id]





