"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
   from huggingface_hub import snapshot_download
   from pyctcdecode import BeamSearchDecoderCTC

   from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
   from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
   from transformers import WavaVecaForCTC






@require_pyctcdecode
class 				snake_case__     (   unittest.TestCase   ):


 def 		a__ (				self		):
        __a     				=  "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
        __a     				=  dict(zip(lowerCamelCase  ,						range(len(lowerCamelCase		)		)		)		)

        __a     				=  {
            "unk_token": "<unk>",
            "bos_token": "<s>",
            "eos_token": "</s>",
        }
        __a     				=  {
            "feature_size": 1,
            "padding_value": 0.0,
            "sampling_rate": 16000,
            "return_attention_mask": False,
            "do_normalize": True,
        }

        __a     				=  tempfile.mkdtemp()
        __a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["vocab_file"]		)
        __a     				=  os.path.join(self.tmpdirname  ,						lowerCamelCase		)
        with open(self.vocab_file  ,						"w"  ,						encoding="utf-8"		) as fp:
               fp.write(json.dumps(lowerCamelCase		) + "\n"		)

        with open(self.feature_extraction_file  ,						"w"  ,						encoding="utf-8"		) as fp:
               fp.write(json.dumps(lowerCamelCase		) + "\n"		)

        # load decoder from hub
        __a     				=  "hf-internal-testing/ngram-beam-search-decoder"


 def 		a__ (				self  ,						**lowerCamelCase		):
        __a     				=  self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCamelCase		)
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						**lowerCamelCase		):
        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name  ,						**lowerCamelCase		)


 def 		a__ (				self		):
        shutil.rmtree(self.tmpdirname		)


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        processor.save_pretrained(self.tmpdirname		)
        __a     				=  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname		)

        # tokenizer
        self.assertEqual(processor.tokenizer.get_vocab()  ,						tokenizer.get_vocab()		)
        self.assertIsInstance(processor.tokenizer  ,						lowerCamelCase		)

        # feature extractor
        self.assertEqual(processor.feature_extractor.to_json_string()  ,						feature_extractor.to_json_string()		)
        self.assertIsInstance(processor.feature_extractor  ,						lowerCamelCase		)

        # decoder
        self.assertEqual(processor.decoder._alphabet.labels  ,						decoder._alphabet.labels		)
        self.assertEqual(
            processor.decoder.model_container[decoder._model_key]._unigram_set  ,						decoder.model_container[decoder._model_key]._unigram_set  ,						)
        self.assertIsInstance(processor.decoder  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM(
            tokenizer=self.get_tokenizer()  ,						feature_extractor=self.get_feature_extractor()  ,						decoder=self.get_decoder()		)
        processor.save_pretrained(self.tmpdirname		)

        # make sure that error is thrown when decoder alphabet doesn't match
        __a     				=  WavaVecaProcessorWithLM.from_pretrained(
            self.tmpdirname  ,						alpha=5.0  ,						beta=3.0  ,						score_boundary=-7.0  ,						unk_score_offset=3		)

        # decoder
        self.assertEqual(processor.language_model.alpha  ,						5.0		)
        self.assertEqual(processor.language_model.beta  ,						3.0		)
        self.assertEqual(processor.language_model.score_boundary  ,						-7.0		)
        self.assertEqual(processor.language_model.unk_score_offset  ,						3		)


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()
        # add token to trigger raise
        tokenizer.add_tokens(["xx"]		)
        with self.assertRaisesRegex(lowerCamelCase  ,						"include"		):
               WavaVecaProcessorWithLM(
                   tokenizer=lowerCamelCase  ,						feature_extractor=self.get_feature_extractor()  ,						decoder=self.get_decoder()		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  floats_list((3, 1000)		)

        __a     				=  feature_extractor(lowerCamelCase  ,						return_tensors="np"		)
        __a     				=  processor(lowerCamelCase  ,						return_tensors="np"		)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  "This is a test string"

        __a     				=  processor(text=lowerCamelCase		)

        __a     				=  tokenizer(lowerCamelCase		)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]  ,						encoded_processor[key]		)


 def 		a__ (				self  ,						lowerCamelCase=(2, 10, 16)  ,						lowerCamelCase=77		):
        np.random.seed(lowerCamelCase		)
        return np.random.rand(*lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits(shape=(10, 16)  ,						seed=13		)

        __a     				=  processor.decode(lowerCamelCase		)

        __a     				=  decoder.decode_beams(lowerCamelCase		)[0]

        self.assertEqual(decoded_decoder[0]  ,						decoded_processor.text		)
        self.assertEqual("</s> <s> </s>"  ,						decoded_processor.text		)
        self.assertEqual(decoded_decoder[-2]  ,						decoded_processor.logit_score		)
        self.assertEqual(decoded_decoder[-1]  ,						decoded_processor.lm_score		)


 @parameterized.expand([[None], ["fork"], ["spawn"]]		)
 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits()

        # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
        #       otherwise, the LM won't be available to the pool's sub-processes.
        # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
        if pool_context is None:
               __a     				=  processor.batch_decode(lowerCamelCase		)
        else:
               with get_context(lowerCamelCase		).Pool() as pool:
                      __a     				=  processor.batch_decode(lowerCamelCase  ,						lowerCamelCase		)

        __a     				=  list(lowerCamelCase		)

        with get_context("fork"		).Pool() as p:
               __a     				=  decoder.decode_beams_batch(lowerCamelCase  ,						lowerCamelCase		)

        __a				, __a				, __a     				=  [], [], []
        for beams in decoded_beams:
               texts_decoder.append(beams[0][0]		)
               logit_scores_decoder.append(beams[0][-2]		)
               lm_scores_decoder.append(beams[0][-1]		)

        self.assertListEqual(lowerCamelCase  ,						decoded_processor.text		)
        self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]  ,						decoded_processor.text		)
        self.assertListEqual(lowerCamelCase  ,						decoded_processor.logit_score		)
        self.assertListEqual(lowerCamelCase  ,						decoded_processor.lm_score		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits()

        __a     				=  15
        __a     				=  -20.0
        __a     				=  -4.0

        __a     				=  processor.batch_decode(
            lowerCamelCase  ,						beam_width=lowerCamelCase  ,						beam_prune_logp=lowerCamelCase  ,						token_min_logp=lowerCamelCase  ,						)
        __a     				=  decoded_processor_out.text

        __a     				=  list(lowerCamelCase		)

        with get_context("fork"		).Pool() as pool:
               __a     				=  decoder.decode_beams_batch(
                   lowerCamelCase  ,						lowerCamelCase  ,						beam_width=lowerCamelCase  ,						beam_prune_logp=lowerCamelCase  ,						token_min_logp=lowerCamelCase  ,						)

        __a     				=  [d[0][0] for d in decoded_decoder_out]
        __a     				=  [d[0][2] for d in decoded_decoder_out]
        __a     				=  [d[0][3] for d in decoded_decoder_out]

        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)
        self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]  ,						lowerCamelCase		)

        self.assertTrue(np.array_equal(lowerCamelCase  ,						decoded_processor_out.logit_score		)		)
        self.assertTrue(np.allclose([-20.054, -18.447]  ,						lowerCamelCase  ,						atol=1E-3		)		)

        self.assertTrue(np.array_equal(lowerCamelCase  ,						decoded_processor_out.lm_score		)		)
        self.assertTrue(np.allclose([-15.554, -13.9474]  ,						lowerCamelCase  ,						atol=1E-3		)		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        __a     				=  self._get_dummy_logits()

        __a     				=  2.0
        __a     				=  5.0
        __a     				=  -20.0
        __a     				=  True

        __a     				=  processor.batch_decode(
            lowerCamelCase  ,						alpha=lowerCamelCase  ,						beta=lowerCamelCase  ,						unk_score_offset=lowerCamelCase  ,						lm_score_boundary=lowerCamelCase  ,						)
        __a     				=  decoded_processor_out.text

        __a     				=  list(lowerCamelCase		)
        decoder.reset_params(
            alpha=lowerCamelCase  ,						beta=lowerCamelCase  ,						unk_score_offset=lowerCamelCase  ,						lm_score_boundary=lowerCamelCase  ,						)

        with get_context("fork"		).Pool() as pool:
               __a     				=  decoder.decode_beams_batch(
                   lowerCamelCase  ,						lowerCamelCase  ,						)

        __a     				=  [d[0][0] for d in decoded_decoder_out]

        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)
        self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]  ,						lowerCamelCase		)
        __a     				=  processor.decoder.model_container[processor.decoder._model_key]
        self.assertEqual(lm_model.alpha  ,						2.0		)
        self.assertEqual(lm_model.beta  ,						5.0		)
        self.assertEqual(lm_model.unk_score_offset  ,						-20.0		)
        self.assertEqual(lm_model.score_boundary  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)

        __a     				=  processor.decoder.model_container[processor.decoder._model_key]
        __a     				=  Path(language_model._kenlm_model.path.decode("utf-8"		)		).parent.parent.absolute()

        __a     				=  os.listdir(lowerCamelCase		)
        __a     				=  ["alphabet.json", "language_model"]

        downloaded_decoder_files.sort()
        expected_decoder_files.sort()

        # test that only decoder relevant files from
        # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
        # are downloaded and none of the rest (e.g. README.md, ...)
        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  snapshot_download("hf-internal-testing/processor_with_lm"		)

        __a     				=  WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase		)

        __a     				=  processor.decoder.model_container[processor.decoder._model_key]
        __a     				=  Path(language_model._kenlm_model.path.decode("utf-8"		)		).parent.parent.absolute()

        __a     				=  os.listdir(lowerCamelCase		)
        __a     				=  os.listdir(lowerCamelCase		)

        local_decoder_files.sort()
        expected_decoder_files.sort()

        # test that both decoder form hub and local files in cache are the same
        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)
        __a     				=  AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"		)

        __a     				=  floats_list((3, 1000)		)

        __a     				=  processor_wavaveca(lowerCamelCase  ,						return_tensors="np"		)
        __a     				=  processor_auto(lowerCamelCase  ,						return_tensors="np"		)

        for key in input_wavaveca.keys():
               self.assertAlmostEqual(input_wavaveca[key].sum()  ,						input_auto[key].sum()  ,						delta=1E-2		)

        __a     				=  self._get_dummy_logits()

        __a     				=  processor_wavaveca.batch_decode(lowerCamelCase		)
        __a     				=  processor_auto.batch_decode(lowerCamelCase		)

        self.assertListEqual(decoded_wavaveca.text  ,						decoded_auto.text		)


 def 		a__ (				self		):
        __a     				=  self.get_feature_extractor()
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_decoder()

        __a     				=  WavaVecaProcessorWithLM(tokenizer=lowerCamelCase  ,						feature_extractor=lowerCamelCase  ,						decoder=lowerCamelCase		)

        self.assertListEqual(
            processor.model_input_names  ,						feature_extractor.model_input_names  ,						msg="`processor` and `feature_extractor` model input names do not match"  ,						)


 @staticmethod
 def 		a__ (				lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  [d[key] for d in offsets]
        return retrieved_list


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)
        __a     				=  self._get_dummy_logits()[0]

        __a     				=  processor.decode(lowerCamelCase  ,						output_word_offsets=lowerCamelCase		)
        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()		)  ,						4		)
        self.assertTrue("text" in outputs		)
        self.assertTrue("word_offsets" in outputs		)
        self.assertTrue(isinstance(lowerCamelCase  ,						lowerCamelCase		)		)

        self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]  ,						"word"		)		)  ,						outputs.text		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]  ,						"word"		)  ,						["<s>", "<s>", "</s>"]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]  ,						"start_offset"		)  ,						[0, 2, 4]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]  ,						"end_offset"		)  ,						[1, 3, 5]		)


 def 		a__ (				self		):
        __a     				=  WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"		)
        __a     				=  self._get_dummy_logits()

        __a     				=  processor.batch_decode(lowerCamelCase  ,						output_word_offsets=lowerCamelCase		)

        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()		)  ,						4		)
        self.assertTrue("text" in outputs		)
        self.assertTrue("word_offsets" in outputs		)
        self.assertTrue(isinstance(lowerCamelCase  ,						lowerCamelCase		)		)

        self.assertListEqual(
            [" ".join(self.get_from_offsets(lowerCamelCase  ,						"word"		)		) for o in outputs["word_offsets"]]  ,						outputs.text		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]  ,						"word"		)  ,						["<s>", "<s>", "</s>"]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]  ,						"start_offset"		)  ,						[0, 2, 4]		)
        self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]  ,						"end_offset"		)  ,						[1, 3, 5]		)






 @slow
 @require_torch
 @require_torchaudio
 def 		a__ (				self		):
        import torch

        __a     				=  load_dataset("common_voice"  ,						"en"  ,						split="train"  ,						streaming=lowerCamelCase		)
        __a     				=  ds.cast_column("audio"  ,						datasets.Audio(sampling_rate=16000		)		)
        __a     				=  iter(lowerCamelCase		)
        __a     				=  next(lowerCamelCase		)

        __a     				=  AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"		)
        __a     				=  WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"		)

        # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
        __a     				=  processor(sample["audio"]["array"]  ,						return_tensors="pt"		).input_values

        with torch.no_grad():
               __a     				=  model(lowerCamelCase		).logits.cpu().numpy()

        __a     				=  processor.decode(logits[0]  ,						output_word_offsets=lowerCamelCase		)

        __a     				=  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
        __a     				=  [
            {
                "start_time": d["start_offset"] * time_offset,
                "end_time": d["end_offset"] * time_offset,
                "word": d["word"],
            }
            for d in output["word_offsets"]
        ]

        __a     				=  "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

        # output words
        self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase  ,						"word"		)		)  ,						lowerCamelCase		)
        self.assertEqual(" ".join(self.get_from_offsets(lowerCamelCase  ,						"word"		)		)  ,						output.text		)

        # output times
        __a     				=  torch.tensor(self.get_from_offsets(lowerCamelCase  ,						"start_time"		)		)
        __a     				=  torch.tensor(self.get_from_offsets(lowerCamelCase  ,						"end_time"		)		)

        # fmt: off
        __a     				=  torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]		)
        __a     				=  torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]		)
        # fmt: on

        self.assertTrue(torch.allclose(lowerCamelCase  ,						lowerCamelCase  ,						atol=0.01		)		)
        self.assertTrue(torch.allclose(lowerCamelCase  ,						lowerCamelCase  ,						atol=0.01		)		)

"""simple docstring"""



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   import tensorflow as tf

   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   from ..tf_utils import stable_softmax

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        requires_backends(self  ,						"vision"		)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)


 def 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

        __a     				=  scores.tolist()
        __a     				=  ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase  ,						lowerCamelCase		)]
