



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 			__lowerCAmelCase    (			unittest.TestCase						):



			def     _lowerCamelCase       (						self   :  List[str])   ->    int:



						"""simple docstring"""






						_UpperCAmelCase		       =   """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
						_UpperCAmelCase		       =   dict(zip(A ,		range(len(A))))

						_UpperCAmelCase		       =   {
						    """unk_token""": """<unk>""",
						    """bos_token""": """<s>""",
						    """eos_token""": """</s>""",
						}
						_UpperCAmelCase		       =   {
						    """feature_size""": 1,
						    """padding_value""": 0.0,
						    """sampling_rate""": 1_60_00,
						    """return_attention_mask""": False,
						    """do_normalize""": True,
						}

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

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

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



			def     _lowerCamelCase       (						self   :  Tuple ,		**A   :  List[Any])   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.add_kwargs_tokens_map.copy()
						kwargs.update(A)
						return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,		**A)



			def     _lowerCamelCase       (						self   :  int ,		**A   :  str)   ->    Optional[Any]:



						"""simple docstring"""






						return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,		**A)



			def     _lowerCamelCase       (						self   :  Optional[int] ,		**A   :  int)   ->    Union[str, Any]:



						"""simple docstring"""






						return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,		**A)



			def     _lowerCamelCase       (						self   :  int)   ->    str:



						"""simple docstring"""






						shutil.rmtree(self.tmpdirname)



			def     _lowerCamelCase       (						self   :  List[str])   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

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

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

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

						# 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 ,		A)



			def     _lowerCamelCase       (						self   :  str)   ->    Tuple:



						"""simple docstring"""






						_UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   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     _lowerCamelCase       (						self   :  str)   ->    List[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_tokenizer()
						# add token to trigger raise
						tokenizer.add_tokens(['xx'])
						with self.assertRaisesRegex(A ,		'include'):
									WavaVecaProcessorWithLM(
									    tokenizer=A ,		feature_extractor=self.get_feature_extractor() ,		decoder=self.get_decoder())



			def     _lowerCamelCase       (						self   :  Tuple)   ->    List[str]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

						_UpperCAmelCase		       =   floats_list((3, 10_00))

						_UpperCAmelCase		       =   feature_extractor(A ,		return_tensors='np')
						_UpperCAmelCase		       =   processor(A ,		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     _lowerCamelCase       (						self   :  Dict)   ->    Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

						_UpperCAmelCase		       =   """This is a test string"""

						_UpperCAmelCase		       =   processor(text=A)

						_UpperCAmelCase		       =   tokenizer(A)

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



			def     _lowerCamelCase       (						self   :  List[str] ,		A   :  str=(2, 10, 16) ,		A   :  Optional[Any]=77)   ->    Optional[int]:



						"""simple docstring"""






						np.random.seed(A)
						return np.random.rand(*A)



			def     _lowerCamelCase       (						self   :  Optional[int])   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

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

						_UpperCAmelCase		       =   processor.decode(A)

						_UpperCAmelCase		       =   decoder.decode_beams(A)[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     _lowerCamelCase       (						self   :  Optional[int] ,		A   :  str)   ->    Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

						_UpperCAmelCase		       =   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:
									_UpperCAmelCase		       =   processor.batch_decode(A)
						else:
									with get_context(A).Pool() as pool:
												_UpperCAmelCase		       =   processor.batch_decode(A ,		A)

						_UpperCAmelCase		       =   list(A)

						with get_context('fork').Pool() as p:
									_UpperCAmelCase		       =   decoder.decode_beams_batch(A ,		A)

						_UpperCAmelCase		       =   [], [], []
						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(A ,		decoded_processor.text)
						self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>'] ,		decoded_processor.text)
						self.assertListEqual(A ,		decoded_processor.logit_score)
						self.assertListEqual(A ,		decoded_processor.lm_score)



			def     _lowerCamelCase       (						self   :  List[str])   ->    Tuple:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

						_UpperCAmelCase		       =   self._get_dummy_logits()

						_UpperCAmelCase		       =   15
						_UpperCAmelCase		       =   -2_0.0
						_UpperCAmelCase		       =   -4.0

						_UpperCAmelCase		       =   processor.batch_decode(
						    A ,		beam_width=A ,		beam_prune_logp=A ,		token_min_logp=A ,		)
						_UpperCAmelCase		       =   decoded_processor_out.text

						_UpperCAmelCase		       =   list(A)

						with get_context('fork').Pool() as pool:
									_UpperCAmelCase		       =   decoder.decode_beams_batch(
									    A ,		A ,		beam_width=A ,		beam_prune_logp=A ,		token_min_logp=A ,		)

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

						self.assertListEqual(A ,		A)
						self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>'] ,		A)

						self.assertTrue(np.array_equal(A ,		decoded_processor_out.logit_score))
						self.assertTrue(np.allclose([-2_0.0_5_4, -1_8.4_4_7] ,		A ,		atol=1E-3))

						self.assertTrue(np.array_equal(A ,		decoded_processor_out.lm_score))
						self.assertTrue(np.allclose([-1_5.5_5_4, -1_3.9_4_7_4] ,		A ,		atol=1E-3))



			def     _lowerCamelCase       (						self   :  int)   ->    Dict:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

						_UpperCAmelCase		       =   self._get_dummy_logits()

						_UpperCAmelCase		       =   2.0
						_UpperCAmelCase		       =   5.0
						_UpperCAmelCase		       =   -2_0.0
						_UpperCAmelCase		       =   True

						_UpperCAmelCase		       =   processor.batch_decode(
						    A ,		alpha=A ,		beta=A ,		unk_score_offset=A ,		lm_score_boundary=A ,		)
						_UpperCAmelCase		       =   decoded_processor_out.text

						_UpperCAmelCase		       =   list(A)
						decoder.reset_params(
						    alpha=A ,		beta=A ,		unk_score_offset=A ,		lm_score_boundary=A ,		)

						with get_context('fork').Pool() as pool:
									_UpperCAmelCase		       =   decoder.decode_beams_batch(
									    A ,		A ,		)

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

						self.assertListEqual(A ,		A)
						self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>'] ,		A)
						_UpperCAmelCase		       =   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 ,		-2_0.0)
						self.assertEqual(lm_model.score_boundary ,		A)



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



						"""simple docstring"""






						_UpperCAmelCase		       =   WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')

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

						_UpperCAmelCase		       =   os.listdir(A)
						_UpperCAmelCase		       =   ["""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(A ,		A)



			def     _lowerCamelCase       (						self   :  Tuple)   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   snapshot_download('hf-internal-testing/processor_with_lm')

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM.from_pretrained(A)

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

						_UpperCAmelCase		       =   os.listdir(A)
						_UpperCAmelCase		       =   os.listdir(A)

						local_decoder_files.sort()
						expected_decoder_files.sort()

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



			def     _lowerCamelCase       (						self   :  Dict)   ->    Dict:



						"""simple docstring"""






						_UpperCAmelCase		       =   WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
						_UpperCAmelCase		       =   AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm')

						_UpperCAmelCase		       =   floats_list((3, 10_00))

						_UpperCAmelCase		       =   processor_wavaveca(A ,		return_tensors='np')
						_UpperCAmelCase		       =   processor_auto(A ,		return_tensors='np')

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

						_UpperCAmelCase		       =   self._get_dummy_logits()

						_UpperCAmelCase		       =   processor_wavaveca.batch_decode(A)
						_UpperCAmelCase		       =   processor_auto.batch_decode(A)

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



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



						"""simple docstring"""






						_UpperCAmelCase		       =   self.get_feature_extractor()
						_UpperCAmelCase		       =   self.get_tokenizer()
						_UpperCAmelCase		       =   self.get_decoder()

						_UpperCAmelCase		       =   WavaVecaProcessorWithLM(tokenizer=A ,		feature_extractor=A ,		decoder=A)

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



			@staticmethod
			def     _lowerCamelCase       (						A   :  Dict ,		A   :  List[Any])   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   [d[key] for d in offsets]
						return retrieved_list



			def     _lowerCamelCase       (						self   :  Optional[int])   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
						_UpperCAmelCase		       =   self._get_dummy_logits()[0]

						_UpperCAmelCase		       =   processor.decode(A ,		output_word_offsets=A)
						# 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(A ,		A))

						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     _lowerCamelCase       (						self   :  int)   ->    Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase		       =   WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm')
						_UpperCAmelCase		       =   self._get_dummy_logits()

						_UpperCAmelCase		       =   processor.batch_decode(A ,		output_word_offsets=A)

						# 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(A ,		A))

						self.assertListEqual(
						    [' '.join(self.get_from_offsets(A ,		'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     _lowerCamelCase       (						self   :  Optional[Any])   ->    str:



						"""simple docstring"""






						import torch

						_UpperCAmelCase		       =   load_dataset('common_voice' ,		'en' ,		split='train' ,		streaming=A)
						_UpperCAmelCase		       =   ds.cast_column('audio' ,		datasets.Audio(sampling_rate=1_60_00))
						_UpperCAmelCase		       =   iter(A)
						_UpperCAmelCase		       =   next(A)

						_UpperCAmelCase		       =   AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm')
						_UpperCAmelCase		       =   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
						_UpperCAmelCase		       =   processor(sample['audio']['array'] ,		return_tensors='pt').input_values

						with torch.no_grad():
									_UpperCAmelCase		       =   model(A).logits.cpu().numpy()

						_UpperCAmelCase		       =   processor.decode(logits[0] ,		output_word_offsets=A)

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

						_UpperCAmelCase		       =   """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(A ,		'word')) ,		A)
						self.assertEqual(' '.join(self.get_from_offsets(A ,		'word')) ,		output.text)

						# output times
						_UpperCAmelCase		       =   torch.tensor(self.get_from_offsets(A ,		'start_time'))
						_UpperCAmelCase		       =   torch.tensor(self.get_from_offsets(A ,		'end_time'))

						# fmt: off
						_UpperCAmelCase		       =   torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9])
						_UpperCAmelCase		       =   torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4])
						# fmt: on

						self.assertTrue(torch.allclose(A ,		A ,		atol=0.0_1))
						self.assertTrue(torch.allclose(A ,		A ,		atol=0.0_1))


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

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           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       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

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