import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
    from transformers.models.mobilevitva.modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        make_divisible,
    )


if is_vision_available():
    from PIL import Image

    from transformers import MobileViTImageProcessor



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.config_class(**self.inputs_dict							)
        self.parent.assertTrue(hasattr(lowerCAmelCase_		,				"""width_multiplier"""							)							)



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=64		,				lowerCAmelCase_=2		,				lowerCAmelCase_=3		,				lowerCAmelCase_="swish"		,				lowerCAmelCase_=3		,				lowerCAmelCase_=32		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=10		,				lowerCAmelCase_=None		,				lowerCAmelCase_=0.25		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				)  ->					List[str]:
        _A        =  parent
        _A        =  batch_size
        _A        =  image_size
        _A        =  patch_size
        _A        =  num_channels
        _A        =  make_divisible(5_12 * width_multiplier		,				divisor=8							)
        _A        =  hidden_act
        _A        =  conv_kernel_size
        _A        =  output_stride
        _A        =  classifier_dropout_prob
        _A        =  use_labels
        _A        =  is_training
        _A        =  num_labels
        _A        =  initializer_range
        _A        =  scope
        _A        =  width_multiplier
        _A        =  ffn_dropout
        _A        =  attn_dropout



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        _A        =  None
        _A        =  None
        if self.use_labels:
               _A        =  ids_tensor([self.batch_size]		,				self.num_labels							)
               _A        =  ids_tensor([self.batch_size, self.image_size, self.image_size]		,				self.num_labels							)

        _A        =  self.get_config()

        return config, pixel_values, labels, pixel_labels



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        return MobileViTVaConfig(
            image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				hidden_act=self.hidden_act		,				conv_kernel_size=self.conv_kernel_size		,				output_stride=self.output_stride		,				classifier_dropout_prob=self.classifier_dropout_prob		,				initializer_range=self.initializer_range		,				width_multiplier=self.width_multiplier		,				ffn_dropout=self.ffn_dropout_prob		,				attn_dropout=self.attn_dropout_prob		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Tuple:
        _A        =  MobileViTVaModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(
            result.last_hidden_state.shape		,				(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  self.num_labels
        _A        =  MobileViTVaForImageClassification(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_		,				labels=lowerCAmelCase_							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Optional[Any]:
        _A        =  self.num_labels
        _A        =  MobileViTVaForSemanticSegmentation(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(
            result.logits.shape		,				(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )		,				)
        _A        =  model(lowerCAmelCase_		,				labels=lowerCAmelCase_							)
        self.parent.assertEqual(
            result.logits.shape		,				(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )		,				)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:List[str]    			=		(
     (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
     if is_torch_available()
     else ()
 )

 lowerCamelCase	:List[str]    			=		(
     {
         '''feature-extraction''': MobileViTVaModel,
         '''image-classification''': MobileViTVaForImageClassification,
         '''image-segmentation''': MobileViTVaForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowerCamelCase	:Union[str, Any]    			=		False
 lowerCamelCase	:Dict    			=		False
 lowerCamelCase	:Tuple    			=		False
 lowerCamelCase	:Dict    			=		False



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  MobileViTVaModelTester(self							)
        _A        =  MobileViTVaConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					str:
        self.config_tester.run_common_tests()



 @unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        pass



 @unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""							)
 def      UpperCAmelCase							(					self							)  ->					int:
        pass



 @unittest.skip(reason="""MobileViTV2 does not output attentions"""							)
 def      UpperCAmelCase							(					self							)  ->					int:
        pass



 @require_torch_multi_gpu
 @unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""							)
 def      UpperCAmelCase							(					self							)  ->					int:
        pass



 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def      UpperCAmelCase							(					self							)  ->					str:
        pass



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               _A        =  inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _A        =  [*signature.parameters.keys()]

               _A        =  ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        def check_hidden_states_output(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							):
               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.eval()

               with torch.no_grad():
                      _A        =  model(**self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_							)							)

               _A        =  outputs.hidden_states

               _A        =  5
               self.assertEqual(len(lowerCAmelCase_							)		,				lowerCAmelCase_							)

               # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
               # with the width and height being successively divided by 2.
               _A        =  2
               for i in range(len(lowerCAmelCase_							)							):
                      self.assertListEqual(
                          list(hidden_states[i].shape[-2:]							)		,				[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]		,				)
                      divisor *= 2

               self.assertEqual(self.model_tester.output_stride		,				divisor // 2							)

        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  True
               check_hidden_states_output(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               _A        =  True

               check_hidden_states_output(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  MobileViTVaModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)






def  snake_case  (       )						->   Any:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







@require_torch
@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 @cached_property
 def      UpperCAmelCase							(					self							)  ->					int:
        return (
            MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""							)
            if is_vision_available()
            else None
        )



 @slow
 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""							).to(
            lowerCAmelCase_							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # forward pass
        with torch.no_grad():
               _A        =  model(**lowerCAmelCase_							)

        # verify the logits
        _A        =  torch.Size((1, 10_00)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase_							)

        _A        =  torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]							).to(lowerCAmelCase_							)

        self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				lowerCAmelCase_		,				atol=1E-4							)							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""							)
        _A        =  model.to(lowerCAmelCase_							)

        _A        =  MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""							)

        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # forward pass
        with torch.no_grad():
               _A        =  model(**lowerCAmelCase_							)
        _A        =  outputs.logits

        # verify the logits
        _A        =  torch.Size((1, 21, 32, 32)							)
        self.assertEqual(logits.shape		,				lowerCAmelCase_							)

        _A        =  torch.tensor(
            [
                [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
                [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
                [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
            ]		,				device=lowerCAmelCase_		,				)

        self.assertTrue(torch.allclose(logits[0, :3, :3, :3]		,				lowerCAmelCase_		,				atol=1E-4							)							)



 @slow
 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""							)
        _A        =  model.to(lowerCAmelCase_							)

        _A        =  MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""							)

        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # forward pass
        with torch.no_grad():
               _A        =  model(**lowerCAmelCase_							)

        _A        =  outputs.logits.detach().cpu()

        _A        =  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_		,				target_sizes=[(50, 60)]							)
        _A        =  torch.Size((50, 60)							)
        self.assertEqual(segmentation[0].shape		,				lowerCAmelCase_							)

        _A        =  image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_							)
        _A        =  torch.Size((32, 32)							)
        self.assertEqual(segmentation[0].shape		,				lowerCAmelCase_							)





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 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _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""": 1_60_00,
            """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      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Tuple:
        _A        =  self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase_							)
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _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      UpperCAmelCase							(					self							)  ->					Tuple:
        _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      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _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      UpperCAmelCase							(					self							)  ->					Any:
        _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, 10_00)							)

        _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      UpperCAmelCase							(					self							)  ->					Any:
        _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      UpperCAmelCase							(					self		,				lowerCAmelCase_=(2, 10, 16)		,				lowerCAmelCase_=77							)  ->					Tuple:
        np.random.seed(lowerCAmelCase_							)
        return np.random.rand(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _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      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Optional[int]:
        _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      UpperCAmelCase							(					self							)  ->					List[Any]:
        _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      UpperCAmelCase							(					self							)  ->					List[Any]:
        _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      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _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      UpperCAmelCase							(					self							)  ->					List[str]:
        _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      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

        _A        =  floats_list((3, 10_00)							)

        _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      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _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      UpperCAmelCase							(					lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  [d[key] for d in offsets]
        return retrieved_list



 def      UpperCAmelCase							(					self							)  ->					Any:
        _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      UpperCAmelCase							(					self							)  ->					Any:
        _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      UpperCAmelCase							(					self							)  ->					Any:
        import torch

        _A        =  load_dataset("""common_voice"""		,				"""en"""		,				split="""train"""		,				streaming=lowerCAmelCase_							)
        _A        =  ds.cast_column("""audio"""		,				datasets.Audio(sampling_rate=1_60_00							)							)
        _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							)							)




