

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
							import torch

							from transformers import XLMRobertaModel







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


		"""simple docstring"""




		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=  XLMRobertaModel.from_pretrained('xlm-roberta-base')
			_UpperCamelCase   :		Optional[int]			=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]])
			# The dog is cute and lives in the garden house

			_UpperCamelCase   :		str			=  torch.Size((1, 12, 7_68))  # batch_size, sequence_length, embedding_vector_dim
			_UpperCamelCase   :		Optional[int]			=  torch.tensor(
			    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]])
			#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
			#  xlmr.eval()
			#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
			with torch.no_grad():
				_UpperCamelCase   :		List[Any]			=  model(__snake_case)['last_hidden_state'].detach()
			self.assertEqual(output.shape	,       __snake_case)
			# compare the actual values for a slice of last dim
			self.assertTrue(torch.allclose(output[:, :, -1]	,       __snake_case	,       atol=1e-3))






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=  XLMRobertaModel.from_pretrained('xlm-roberta-large')
			_UpperCamelCase   :		Tuple			=  torch.tensor([[0, 5_81, 1_02_69, 83, 9_99_42, 1_36, 6_07_42, 23, 70, 8_05_83, 1_82_76, 2]])
			# The dog is cute and lives in the garden house

			_UpperCamelCase   :		Dict			=  torch.Size((1, 12, 10_24))  # batch_size, sequence_length, embedding_vector_dim
			_UpperCamelCase   :		List[str]			=  torch.tensor(
			    [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]])
			#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
			#  xlmr.eval()
			#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
			with torch.no_grad():
				_UpperCamelCase   :		str			=  model(__snake_case)['last_hidden_state'].detach()
			self.assertEqual(output.shape	,       __snake_case)
			# compare the actual values for a slice of last dim
			self.assertTrue(torch.allclose(output[:, :, -1]	,       __snake_case	,       atol=1e-3))







# 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 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "facebook/bart-large-mnli"
		a__					       =  (
		    "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."
		)
		a__					       =  "text_classifier"
		a__					       =  AutoTokenizer
		a__					       =  AutoModelForSequenceClassification

		a__					       =  ["text", ["text"]]
		a__					       =  ["text"]


		def 		A__					(				self):
			super().setup()
			_UpperCamelCase   :		List[Any]			=						self.model.config
			_UpperCamelCase   :		Optional[int]			=						-1
			for idx, label in config.idalabel.items():
				if label.lower().startswith('entail'):
					_UpperCamelCase   :		Tuple			=						int(__snake_case)
			if self.entailment_id == -1:
				raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




