import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import YolosImageProcessor



class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_=7				,				A_=3				,				A_=30				,				A_=4_00				,				A_=True				,				A_=None				,				A_=True				,				A_=[0.5, 0.5, 0.5]				,				A_=[0.5, 0.5, 0.5]				,				A_=True				,				A_=1 / 2_55				,				A_=True				,				) ->				List[Any]:






           """simple docstring"""
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           _lowerCamelCase			    =				size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				min_resolution
           _lowerCamelCase			    =				max_resolution
           _lowerCamelCase			    =				do_resize
           _lowerCamelCase			    =				size
           _lowerCamelCase			    =				do_normalize
           _lowerCamelCase			    =				image_mean
           _lowerCamelCase			    =				image_std
           _lowerCamelCase			    =				do_rescale
           _lowerCamelCase			    =				rescale_factor
           _lowerCamelCase			    =				do_pad



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }





     def 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				List[str]:






           """simple docstring"""
           if not batched:
                 _lowerCamelCase			    =				image_inputs[0]
                 if isinstance(A_				,				Image.Image						):
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.size
                 else:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.shape[1], image.shape[2]
                 if w < h:
                       _lowerCamelCase			    =				int(self.size['''shortest_edge'''] * h / w						)
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                 elif w > h:
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                       _lowerCamelCase			    =				int(self.size['''shortest_edge'''] * w / h						)
                 else:
                       _lowerCamelCase			    =				self.size['''shortest_edge''']
                       _lowerCamelCase			    =				self.size['''shortest_edge''']

           else:
                 _lowerCamelCase			    =				[]
                 for image in image_inputs:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				self.get_expected_values([image]						)
                       expected_values.append((expected_height, expected_width)						)
                 _lowerCamelCase			    =				max(A_				,				key=lambda A_						: item[0]						)[0]
                 _lowerCamelCase			    =				max(A_				,				key=lambda A_						: item[1]						)[1]

           return expected_height, expected_width





@require_torch
@require_vision
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  YolosImageProcessor if is_vision_available() else None



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				YolosImageProcessingTester(self						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           self.assertTrue(hasattr(A_				,				'''image_mean'''						)						)
           self.assertTrue(hasattr(A_				,				'''image_std'''						)						)
           self.assertTrue(hasattr(A_				,				'''do_normalize'''						)						)
           self.assertTrue(hasattr(A_				,				'''do_resize'''						)						)
           self.assertTrue(hasattr(A_				,				'''size'''						)						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.image_processing_class.from_dict(self.image_processor_dict						)
           self.assertEqual(image_processor.size				,				{'''shortest_edge''': 18, '''longest_edge''': 13_33}						)
           self.assertEqual(image_processor.do_pad				,				A_						)

           _lowerCamelCase			    =				self.image_processing_class.from_dict(
               self.image_processor_dict				,				size=42				,				max_size=84				,				pad_and_return_pixel_mask=A_						)
           self.assertEqual(image_processor.size				,				{'''shortest_edge''': 42, '''longest_edge''': 84}						)
           self.assertEqual(image_processor.do_pad				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           pass



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






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				Image.Image						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)

           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values
           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random numpy tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				numpify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				np.ndarray						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)

           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



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






           """simple docstring"""
           # Initialize image_processing
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				torchify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				torch.Tensor						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)

           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)

           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           # Initialize image_processings
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           _lowerCamelCase			    =				self.image_processing_class(do_resize=A_				,				do_normalize=A_				,				do_rescale=A_						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_				,				torchify=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				torch.Tensor						)

           # Test whether the method "pad" and calling the image processor return the same tensors
           _lowerCamelCase			    =				image_processing_a.pad(A_				,				return_tensors='''pt'''						)
           _lowerCamelCase			    =				image_processing_a(A_				,				return_tensors='''pt'''						)

           self.assertTrue(
               torch.allclose(encoded_images_with_method['''pixel_values''']				,				encoded_images['''pixel_values''']				,				atol=1E-4						)						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           # prepare image and target
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

           _lowerCamelCase			    =				{'''image_id''': 3_97_69, '''annotations''': target}

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				return_tensors='''pt'''						)

           # verify pixel values
           _lowerCamelCase			    =				torch.Size([1, 3, 8_00, 10_66]						)
           self.assertEqual(encoding['''pixel_values'''].shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor([0.2796, 0.3138, 0.3481]						)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,				A_				,				atol=1E-4						)						)

           # verify area
           _lowerCamelCase			    =				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([75, 75, 63, 65, 17, 17]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           # prepare image, target and masks_path
           _lowerCamelCase			    =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''				,				'''r'''						) as f:
                 _lowerCamelCase			    =				json.loads(f.read()						)

           _lowerCamelCase			    =				{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

           _lowerCamelCase			    =				pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

           # encode them
           _lowerCamelCase			    =				YolosImageProcessor(format='''coco_panoptic'''						)
           _lowerCamelCase			    =				image_processing(images=A_				,				annotations=A_				,				masks_path=A_				,				return_tensors='''pt'''						)

           # verify pixel values
           _lowerCamelCase			    =				torch.Size([1, 3, 8_00, 10_66]						)
           self.assertEqual(encoding['''pixel_values'''].shape				,				A_						)

           _lowerCamelCase			    =				torch.tensor([0.2796, 0.3138, 0.3481]						)
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]				,				A_				,				atol=1E-4						)						)

           # verify area
           _lowerCamelCase			    =				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']				,				A_						)						)
           # verify boxes
           _lowerCamelCase			    =				torch.Size([6, 4]						)
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape				,				A_						)
           _lowerCamelCase			    =				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]				,				A_				,				atol=1E-3						)						)
           # verify image_id
           _lowerCamelCase			    =				torch.tensor([3_97_69]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']				,				A_						)						)
           # verify is_crowd
           _lowerCamelCase			    =				torch.tensor([0, 0, 0, 0, 0, 0]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']				,				A_						)						)
           # verify class_labels
           _lowerCamelCase			    =				torch.tensor([17, 17, 63, 75, 75, 93]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']				,				A_						)						)
           # verify masks
           _lowerCamelCase			    =				82_28_73
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()				,				A_						)
           # verify orig_size
           _lowerCamelCase			    =				torch.tensor([4_80, 6_40]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']				,				A_						)						)
           # verify size
           _lowerCamelCase			    =				torch.tensor([8_00, 10_66]						)
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']				,				A_						)						)
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  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






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

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

           _lowerCamelCase			    =				tempfile.mkdtemp()
           _lowerCamelCase			    =				os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES['''vocab_file''']						)
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				'''hf-internal-testing/ngram-beam-search-decoder'''



     def 		UpperCamelCase_						(					self				,				**A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.add_kwargs_tokens_map.copy()
           kwargs.update(A_						)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				int:






           """simple docstring"""
           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,				**A_						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           shutil.rmtree(self.tmpdirname						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           processor.save_pretrained(self.tmpdirname						)
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				floats_list((3, 10_00)						)

           _lowerCamelCase			    =				feature_extractor(A_				,				return_tensors='''np'''						)
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				'''This is a test string'''

           _lowerCamelCase			    =				processor(text=A_						)

           _lowerCamelCase			    =				tokenizer(A_						)

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



     def 		UpperCamelCase_						(					self				,				A_=(2, 10, 16)				,				A_=77						) ->				Optional[Any]:






           """simple docstring"""
           np.random.seed(A_						)
           return np.random.rand(*A_						)



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






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

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

           _lowerCamelCase			    =				processor.decode(A_						)

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				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:
                 _lowerCamelCase			    =				processor.batch_decode(A_						)
           else:
                 with get_context(A_						).Pool() as pool:
                       _lowerCamelCase			    =				processor.batch_decode(A_				,				A_						)

           _lowerCamelCase			    =				list(A_						)

           with get_context('''fork'''						).Pool() as p:
                 _lowerCamelCase			    =				decoder.decode_beams_batch(A_				,				A_						)

           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				[], [], []
           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 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				15
           _lowerCamelCase			    =				-20.0
           _lowerCamelCase			    =				-4.0

           _lowerCamelCase			    =				processor.batch_decode(
               A_				,				beam_width=A_				,				beam_prune_logp=A_				,				token_min_logp=A_				,				)
           _lowerCamelCase			    =				decoded_processor_out.text

           _lowerCamelCase			    =				list(A_						)

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

           _lowerCamelCase			    =				[d[0][0] for d in decoded_decoder_out]
           _lowerCamelCase			    =				[d[0][2] for d in decoded_decoder_out]
           _lowerCamelCase			    =				[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([-20.054, -18.447]				,				A_				,				atol=1E-3						)						)

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



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






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				2.0
           _lowerCamelCase			    =				5.0
           _lowerCamelCase			    =				-20.0
           _lowerCamelCase			    =				True

           _lowerCamelCase			    =				processor.batch_decode(
               A_				,				alpha=A_				,				beta=A_				,				unk_score_offset=A_				,				lm_score_boundary=A_				,				)
           _lowerCamelCase			    =				decoded_processor_out.text

           _lowerCamelCase			    =				list(A_						)
           decoder.reset_params(
               alpha=A_				,				beta=A_				,				unk_score_offset=A_				,				lm_score_boundary=A_				,				)

           with get_context('''fork'''						).Pool() as pool:
                 _lowerCamelCase			    =				decoder.decode_beams_batch(
                     A_				,				A_				,				)

           _lowerCamelCase			    =				[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_						)
           _lowerCamelCase			    =				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				,				A_						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

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

           _lowerCamelCase			    =				os.listdir(A_						)
           _lowerCamelCase			    =				['''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 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				snapshot_download('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained(A_						)

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

           _lowerCamelCase			    =				os.listdir(A_						)
           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				floats_list((3, 10_00)						)

           _lowerCamelCase			    =				processor_wavaveca(A_				,				return_tensors='''np'''						)
           _lowerCamelCase			    =				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						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				processor_wavaveca.batch_decode(A_						)
           _lowerCamelCase			    =				processor_auto.batch_decode(A_						)

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



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					A_				,				A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				[d[key] for d in offsets]
           return retrieved_list



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				self._get_dummy_logits()[0]

           _lowerCamelCase			    =				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 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				self._get_dummy_logits()

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






           """simple docstring"""
           import torch

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

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

           with torch.no_grad():
                 _lowerCamelCase			    =				model(A_						).logits.cpu().numpy()

           _lowerCamelCase			    =				processor.decode(logits[0]				,				output_word_offsets=A_						)

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

           _lowerCamelCase			    =				'''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
           _lowerCamelCase			    =				torch.tensor(self.get_from_offsets(A_				,				'''start_time'''						)						)
           _lowerCamelCase			    =				torch.tensor(self.get_from_offsets(A_				,				'''end_time'''						)						)

           # fmt: off
           _lowerCamelCase			    =				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]						)
           _lowerCamelCase			    =				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(A_				,				A_				,				atol=0.01						)						)
           self.assertTrue(torch.allclose(A_				,				A_				,				atol=0.01						)						)