"""simple docstring"""




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a   :	Tuple 					=     logging.get_logger(__name__)
class 		__A							(				__snake_case	):
     _UpperCamelCase							:    str	      =	["pixel_values"]






     def __init__(	self       ,  a__ = True       ,  a__ = None       ,  a__ = PILImageResampling.BICUBIC       ,  a__ = True       ,  a__ = None       ,  a__ = True       ,  a__ = 1 / 255       ,  a__ = True       ,  a__ = IMAGENET_DEFAULT_MEAN       ,  a__ = IMAGENET_DEFAULT_STD       ,  **a__       ,  ):
       super().__init__(**__UpperCamelCase						)
       _lowerCAmelCase		: Union[str, Any]						    =  size if size is not None else {"""shortest_edge""": 224}
       _lowerCAmelCase		: Optional[int]						    =  get_size_dict(__UpperCamelCase       ,  default_to_square=__UpperCamelCase						)
       _lowerCAmelCase		: Union[str, Any]						    =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
       _lowerCAmelCase		: int						    =  get_size_dict(__UpperCamelCase       ,  param_name="""crop_size"""						)

       _lowerCAmelCase		: str						    =  do_resize
       _lowerCAmelCase		: Dict						    =  size
       _lowerCAmelCase		: Optional[int]						    =  resample
       _lowerCAmelCase		: Any						    =  do_center_crop
       _lowerCAmelCase		: List[str]						    =  crop_size
       _lowerCAmelCase		: List[str]						    =  do_rescale
       _lowerCAmelCase		: Optional[Any]						    =  rescale_factor
       _lowerCAmelCase		: List[Any]						    =  do_normalize
       _lowerCAmelCase		: Optional[int]						    =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       _lowerCAmelCase		: int						    =  image_std if image_std is not None else IMAGENET_DEFAULT_STD






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = PILImageResampling.BICUBIC       ,  a__ = None       ,  **a__       ,  ):
       _lowerCAmelCase		: List[Any]						    =  get_size_dict(__UpperCamelCase       ,  default_to_square=__UpperCamelCase						)
       # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
       if "shortest_edge" in size:
         _lowerCAmelCase		: str						    =  int((256 / 224) * size["""shortest_edge"""]						)
         _lowerCAmelCase		: Tuple						    =  get_resize_output_image_size(__UpperCamelCase       ,  size=__UpperCamelCase       ,  default_to_square=__UpperCamelCase						)
         _lowerCAmelCase		: Dict						    =  {"""height""": output_size[0], """width""": output_size[1]}
       if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             F"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"						)
       return resize(
           __UpperCamelCase       ,  size=(size_dict["""height"""], size_dict["""width"""])       ,  resample=__UpperCamelCase       ,  data_format=__UpperCamelCase       ,  **__UpperCamelCase						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = None       ,  **a__       ,  ):
       _lowerCAmelCase		: Dict						    =  get_size_dict(__UpperCamelCase						)
       if "height" not in size or "width" not in size:
         raise ValueError(F"Size dict must have keys 'height' and 'width'. Got {size.keys()}"						)
       return center_crop(__UpperCamelCase       ,  size=(size["""height"""], size["""width"""])       ,  data_format=__UpperCamelCase       ,  **__UpperCamelCase						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__ = None       ,  **a__       ,  ):
       return rescale(__UpperCamelCase       ,  scale=__UpperCamelCase       ,  data_format=__UpperCamelCase       ,  **__UpperCamelCase						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__ = None       ,  **a__       ,  ):
       return normalize(__UpperCamelCase       ,  mean=__UpperCamelCase       ,  std=__UpperCamelCase       ,  data_format=__UpperCamelCase       ,  **__UpperCamelCase						)






     def 					__A						(	self       ,  a__       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = None       ,  a__ = ChannelDimension.FIRST       ,  **a__       ,  ):
       _lowerCAmelCase		: List[Any]						    =  do_resize if do_resize is not None else self.do_resize
       _lowerCAmelCase		: int						    =  resample if resample is not None else self.resample
       _lowerCAmelCase		: Optional[int]						    =  do_center_crop if do_center_crop is not None else self.do_center_crop
       _lowerCAmelCase		: Any						    =  do_rescale if do_rescale is not None else self.do_rescale
       _lowerCAmelCase		: Union[str, Any]						    =  rescale_factor if rescale_factor is not None else self.rescale_factor
       _lowerCAmelCase		: List[str]						    =  do_normalize if do_normalize is not None else self.do_normalize
       _lowerCAmelCase		: Dict						    =  image_mean if image_mean is not None else self.image_mean
       _lowerCAmelCase		: Dict						    =  image_std if image_std is not None else self.image_std

       _lowerCAmelCase		: Dict						    =  size if size is not None else self.size
       _lowerCAmelCase		: List[Any]						    =  get_size_dict(__UpperCamelCase       ,  default_to_square=__UpperCamelCase						)
       _lowerCAmelCase		: Optional[Any]						    =  crop_size if crop_size is not None else self.crop_size
       _lowerCAmelCase		: Dict						    =  get_size_dict(__UpperCamelCase       ,  param_name="""crop_size"""						)

       _lowerCAmelCase		: Union[str, Any]						    =  make_list_of_images(__UpperCamelCase						)

       if not valid_images(__UpperCamelCase						):
         raise ValueError(
             """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
             """torch.Tensor, tf.Tensor or jax.ndarray."""						)

       if do_resize and size is None:
         raise ValueError("""Size must be specified if do_resize is True."""						)

       if do_center_crop and crop_size is None:
         raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

       if do_rescale and rescale_factor is None:
         raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

       # All transformations expect numpy arrays.
       _lowerCAmelCase		: List[Any]						    =  [to_numpy_array(__UpperCamelCase						) for image in images]

       if do_resize:
         _lowerCAmelCase		: Optional[int]						    =  [self.resize(__UpperCamelCase       ,  __UpperCamelCase       ,  __UpperCamelCase						) for image in images]

       if do_center_crop:
         _lowerCAmelCase		: str						    =  [self.center_crop(__UpperCamelCase       ,  __UpperCamelCase						) for image in images]

       if do_rescale:
         _lowerCAmelCase		: List[str]						    =  [self.rescale(__UpperCamelCase       ,  __UpperCamelCase						) for image in images]

       if do_normalize:
         _lowerCAmelCase		: Any						    =  [self.normalize(__UpperCamelCase       ,  __UpperCamelCase       ,  __UpperCamelCase						) for image in images]

       _lowerCAmelCase		: List[Any]						    =  [to_channel_dimension_format(__UpperCamelCase       ,  __UpperCamelCase						) for image in images]

       _lowerCAmelCase		: Tuple						    =  {"""pixel_values""": images}
       return BatchFeature(data=__UpperCamelCase       ,  tensor_type=__UpperCamelCase						)





"""simple docstring"""




import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY
@is_pipeline_test
class 		__A							(				unittest.TestCase	):
     _UpperCamelCase							:    str	      =	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     _UpperCamelCase							:    Any	      =	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  AudioClassificationPipeline(model=a__       ,  feature_extractor=a__						)

       # test with a raw waveform
       _lowerCAmelCase		: Optional[int]						    =  np.zeros((34000,)						)
       _lowerCAmelCase		: Optional[Any]						    =  np.zeros((14000,)						)
       return audio_classifier, [audioa, audio]






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  examples
       _lowerCAmelCase		: List[Any]						    =  audio_classifier(a__						)
       # by default a model is initialized with num_labels=2
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )
       _lowerCAmelCase		: Tuple						    =  audio_classifier(a__       ,  top_k=1						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )

       self.run_torchaudio(a__						)






     @require_torchaudio
     def 					__A						(	self       ,  a__						):
       import datasets

       # test with a local file
       _lowerCAmelCase		: int						    =  datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""       ,  """clean"""       ,  split="""validation"""						)
       _lowerCAmelCase		: List[Any]						    =  dataset[0]["""audio"""]["""array"""]
       _lowerCAmelCase		: str						    =  audio_classifier(a__						)
       self.assertEqual(
           a__       ,  [
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
               {"""score""": ANY(a__						), """label""": ANY(a__						)},
           ]       ,  )






     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  """anton-l/wav2vec2-random-tiny-classifier"""

       _lowerCAmelCase		: Optional[Any]						    =  pipeline("""audio-classification"""       ,  model=a__						)

       _lowerCAmelCase		: Any						    =  np.ones((8000,)						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)

       _lowerCAmelCase		: List[str]						    =  [
           {"""score""": 0.0_8_4_2, """label""": """no"""},
           {"""score""": 0.0_8_3_8, """label""": """up"""},
           {"""score""": 0.0_8_3_7, """label""": """go"""},
           {"""score""": 0.0_8_3_4, """label""": """right"""},
       ]
       _lowerCAmelCase		: str						    =  [
           {"""score""": 0.0_8_4_5, """label""": """stop"""},
           {"""score""": 0.0_8_4_4, """label""": """on"""},
           {"""score""": 0.0_8_4_1, """label""": """right"""},
           {"""score""": 0.0_8_3_4, """label""": """left"""},
       ]
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

       _lowerCAmelCase		: int						    =  {"""array""": np.ones((8000,)						), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
       _lowerCAmelCase		: int						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertIn(nested_simplify(a__       ,  decimals=4						)       ,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)






     @require_torch
     @slow
     def 					__A						(	self						):
       import datasets

       _lowerCAmelCase		: Optional[Any]						    =  """superb/wav2vec2-base-superb-ks"""

       _lowerCAmelCase		: List[str]						    =  pipeline("""audio-classification"""       ,  model=a__						)
       _lowerCAmelCase		: str						    =  datasets.load_dataset("""anton-l/superb_dummy"""       ,  """ks"""       ,  split="""test"""						)

       _lowerCAmelCase		: Optional[Any]						    =  np.array(dataset[3]["""speech"""]       ,  dtype=np.floataa						)
       _lowerCAmelCase		: List[str]						    =  audio_classifier(a__       ,  top_k=4						)
       self.assertEqual(
           nested_simplify(a__       ,  decimals=3						)       ,  [
               {"""score""": 0.9_8_1, """label""": """go"""},
               {"""score""": 0.0_0_7, """label""": """up"""},
               {"""score""": 0.0_0_6, """label""": """_unknown_"""},
               {"""score""": 0.0_0_1, """label""": """down"""},
           ]       ,  )






     @require_tf
     @unittest.skip("""Audio classification is not implemented for TF"""						)
     def 					__A						(	self						):
       pass




