


"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
   from transformers import TvltImageProcessor

if is_speech_available():
   from transformers import TvltFeatureExtractor

from transformers import TvltProcessor


@require_torch
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				'''ZinengTang/tvlt-base'''
          _a    	=				tempfile.mkdtemp()


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							Tuple:
          return TvltImageProcessor.from_pretrained(self.checkpoint    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				**__UpperCAmelCase							)       ->							Optional[Any]:
          return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,				**__UpperCAmelCase							)


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


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_feature_extractor()

          _a    	=				TvltProcessor(image_processor=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase							)
          processor.save_pretrained(self.tmpdirname							)
          _a    	=				TvltProcessor.from_pretrained(self.tmpdirname							)

          self.assertIsInstance(processor.feature_extractor    ,				__UpperCAmelCase							)
          self.assertIsInstance(processor.image_processor    ,				__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Tuple:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_feature_extractor()

          _a    	=				TvltProcessor(image_processor=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase							)

          _a    	=				np.ones([12000]							)

          _a    	=				feature_extractor(__UpperCAmelCase    ,				return_tensors='''np'''							)
          _a    	=				processor(audio=__UpperCAmelCase    ,				return_tensors='''np'''							)

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


     def 							_UpperCAmelCase      (      self							)       ->							str:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_feature_extractor()

          _a    	=				TvltProcessor(image_processor=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase							)

          _a    	=				np.ones([3, 224, 224]							)

          _a    	=				image_processor(__UpperCAmelCase    ,				return_tensors='''np'''							)
          _a    	=				processor(images=__UpperCAmelCase    ,				return_tensors='''np'''							)

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


     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_feature_extractor()

          _a    	=				TvltProcessor(image_processor=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase							)

          _a    	=				np.ones([12000]							)
          _a    	=				np.ones([3, 224, 224]							)

          _a    	=				processor(audio=__UpperCAmelCase    ,				images=__UpperCAmelCase							)

          self.assertListEqual(list(inputs.keys()							)    ,				['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']							)

          # test if it raises when no input is passed
          with pytest.raises(__UpperCAmelCase							):
               processor()





     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				self.get_image_processor()
          _a    	=				self.get_feature_extractor()

          _a    	=				TvltProcessor(image_processor=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase							)

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



"""simple docstring"""






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)