





'''simple docstring'''







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 					__a      (unittest.TestCase     ):

							@slow
							def     UpperCAmelCase__	(							self							:     Any   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  XLMRobertaModel.from_pretrained('''xlm-roberta-base'''   )
													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_			:      Tuple     						=  torch.Size((1, 12, 7_68)   )  # batch_size, sequence_length, embedding_vector_dim
													UpperCAmelCase_			:      List[str]     						=  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_			:      Tuple     						=  model(__lowerCamelCase   )["last_hidden_state"].detach()
													self.assertEqual(output.shape       ,     __lowerCamelCase   )
													# compare the actual values for a slice of last dim
													self.assertTrue(torch.allclose(output[:, :, -1]       ,     __lowerCamelCase       ,     atol=1E-3   )   )



							@slow
							def     UpperCAmelCase__	(							self							:     Optional[int]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  XLMRobertaModel.from_pretrained('''xlm-roberta-large'''   )
													UpperCAmelCase_			:      List[Any]     						=  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_			:      Optional[Any]     						=  torch.Size((1, 12, 10_24)   )  # batch_size, sequence_length, embedding_vector_dim
													UpperCAmelCase_			:      List[Any]     						=  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_			:      Optional[int]     						=  model(__lowerCamelCase   )["last_hidden_state"].detach()
													self.assertEqual(output.shape       ,     __lowerCamelCase   )
													# compare the actual values for a slice of last dim
													self.assertTrue(torch.allclose(output[:, :, -1]       ,     __lowerCamelCase       ,     atol=1E-3   )   )












'''simple docstring'''







# 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool


class 					__a      (lowerCamelCase     ):
							__a					:   List[Any]									=       "openai/whisper-base"
							__a					:   Optional[Any]									=       (
							    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
							    "transcribed text."
							)
							__a					:   Any									=       "transcriber"
							__a					:   str									=       WhisperProcessor
							__a					:   List[Any]									=       WhisperForConditionalGeneration

							__a					:   int									=       ["audio"]
							__a					:   Optional[Any]									=       ["text"]

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     List[str]   )					->       Optional[int]:







													"""simple docstring"""
													return self.pre_processor(__magic_name__       ,     return_tensors='''pt'''   ).input_features

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Dict   )					->       Tuple:







													"""simple docstring"""
													return self.model.generate(inputs=__magic_name__   )



							def     UpperCAmelCase__	(							self							:     List[str]       ,     __magic_name__							:     Dict   )					->       str:







													"""simple docstring"""
													return self.pre_processor.batch_decode(__magic_name__       ,     skip_special_tokens=__magic_name__   )[0]





