from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

__lowerCamelCase      :			Dict							       =		logging.get_logger(__name__)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[List[ImageInput]]:







  """simple docstring"""





  if isinstance(__UpperCamelCase			,					(list, tuple)     ) and isinstance(videos[0]			,					(list, tuple)     ) and is_valid_image(videos[0][0]     ):
    return videos

  elif isinstance(__UpperCamelCase			,					(list, tuple)     ) and is_valid_image(videos[0]     ):
    return [videos]

  elif is_valid_image(__UpperCamelCase     ):
    return [[videos]]

  raise ValueError(f"""Could not make batched video from {videos}"""     )







class __snake_case    (						__a		):
   lowerCAmelCase_														=							["""pixel_values"""]




   def __init__(   self      :    Any	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    bool = True	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = True	,     _lowercase      :    Union[int, float] = 1 / 2_55	,     _lowercase      :    bool = True	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     **_lowercase      :    Any	,     ):



     """simple docstring"""






     super().__init__(**a__				)
     SCREAMING_SNAKE_CASE__      							=				size if size is not None else {"""shortest_edge""": 2_56}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(a__	,     default_to_square=a__				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(a__	,     param_name="""crop_size"""				)

     SCREAMING_SNAKE_CASE__      							=				do_resize
     SCREAMING_SNAKE_CASE__      							=				size
     SCREAMING_SNAKE_CASE__      							=				do_center_crop
     SCREAMING_SNAKE_CASE__      							=				crop_size
     SCREAMING_SNAKE_CASE__      							=				resample
     SCREAMING_SNAKE_CASE__      							=				do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor
     SCREAMING_SNAKE_CASE__      							=				offset
     SCREAMING_SNAKE_CASE__      							=				do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE__      							=				image_std if image_std is not None else IMAGENET_STANDARD_STD




   def      __a						(   self      :    Any	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    PILImageResampling = PILImageResampling.BILINEAR	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    int	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(a__	,     default_to_square=a__				)
     if "shortest_edge" in size:
       SCREAMING_SNAKE_CASE__      							=				get_resize_output_image_size(a__	,     size["""shortest_edge"""]	,     default_to_square=a__				)
     elif "height" in size and "width" in size:
       SCREAMING_SNAKE_CASE__      							=				(size["""height"""], size["""width"""])
     else:
       raise ValueError(f"""Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}"""				)
     return resize(a__	,     size=a__	,     resample=a__	,     data_format=a__	,     **a__				)




   def      __a						(   self      :    Tuple	,     _lowercase      :    np.ndarray	,     _lowercase      :    Dict[str, int]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Dict	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				get_size_dict(a__				)
     if "height" not in size or "width" not in size:
       raise ValueError(f"""Size must have \'height\' and \'width\' as keys. Got {size.keys()}"""				)
     return center_crop(a__	,     size=(size["""height"""], size["""width"""])	,     data_format=a__	,     **a__				)




   def      __a						(   self      :    str	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[int, float]	,     _lowercase      :    bool = True	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Optional[Any]	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				image.astype(np.floataa				)
     if offset:
       SCREAMING_SNAKE_CASE__      							=				image - (scale / 2)
     return rescale(a__	,     scale=a__	,     data_format=a__	,     **a__				)




   def      __a						(   self      :    int	,     _lowercase      :    np.ndarray	,     _lowercase      :    Union[float, List[float]]	,     _lowercase      :    Union[float, List[float]]	,     _lowercase      :    Optional[Union[str, ChannelDimension]] = None	,     **_lowercase      :    Optional[Any]	,     ):



     """simple docstring"""






     return normalize(a__	,     mean=a__	,     std=a__	,     data_format=a__	,     **a__				)




   def      __a						(   self      :    List[Any]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[ChannelDimension] = ChannelDimension.FIRST	,     ):



     """simple docstring"""






     if do_resize and size is None or resample is None:
       raise ValueError("""Size and resample 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."""				)

     if offset and not do_rescale:
       raise ValueError("""For offset, do_rescale must also be set to True."""				)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__      							=				to_numpy_array(a__				)

     if do_resize:
       SCREAMING_SNAKE_CASE__      							=				self.resize(image=a__	,     size=a__	,     resample=a__				)

     if do_center_crop:
       SCREAMING_SNAKE_CASE__      							=				self.center_crop(a__	,     size=a__				)

     if do_rescale:
       SCREAMING_SNAKE_CASE__      							=				self.rescale(image=a__	,     scale=a__	,     offset=a__				)

     if do_normalize:
       SCREAMING_SNAKE_CASE__      							=				self.normalize(image=a__	,     mean=a__	,     std=a__				)

     SCREAMING_SNAKE_CASE__      							=				to_channel_dimension_format(a__	,     a__				)
     return image




   def      __a						(   self      :    Optional[Any]	,     _lowercase      :    ImageInput	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    PILImageResampling = None	,     _lowercase      :    bool = None	,     _lowercase      :    Dict[str, int] = None	,     _lowercase      :    bool = None	,     _lowercase      :    float = None	,     _lowercase      :    bool = None	,     _lowercase      :    bool = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[float, List[float]]] = None	,     _lowercase      :    Optional[Union[str, TensorType]] = None	,     _lowercase      :    ChannelDimension = ChannelDimension.FIRST	,     **_lowercase      :    str	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__      							=				resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__      							=				do_center_crop if do_center_crop is not None else self.do_center_crop
     SCREAMING_SNAKE_CASE__      							=				do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__      							=				rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__      							=				offset if offset is not None else self.offset
     SCREAMING_SNAKE_CASE__      							=				do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__      							=				image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__      							=				image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE__      							=				size if size is not None else self.size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(a__	,     default_to_square=a__				)
     SCREAMING_SNAKE_CASE__      							=				crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE__      							=				get_size_dict(a__	,     param_name="""crop_size"""				)

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

     SCREAMING_SNAKE_CASE__      							=				make_batched(a__				)

     SCREAMING_SNAKE_CASE__      							=				[
         [
             self._preprocess_image(
                 image=a__	,     do_resize=a__	,     size=a__	,     resample=a__	,     do_center_crop=a__	,     crop_size=a__	,     do_rescale=a__	,     rescale_factor=a__	,     offset=a__	,     do_normalize=a__	,     image_mean=a__	,     image_std=a__	,     data_format=a__	,     )
             for img in video
         ]
         for video in videos
     ]

     SCREAMING_SNAKE_CASE__      							=				{"""pixel_values""": videos}
     return BatchFeature(data=a__	,     tensor_type=a__				)
import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


__lowerCamelCase      :			List[Any]							       =		get_tests_dir('''fixtures''')
__lowerCamelCase      :			Optional[int]							       =		get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
__lowerCamelCase      :			Any							       =		get_tests_dir('''fixtures/dummy-config.json''')







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				0




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""facebook/wav2vec2-base-960h"""				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdirname:
       SCREAMING_SNAKE_CASE__      							=				WavaVecaConfig()

       # remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				).to_dict()

       config_dict.pop("""feature_extractor_type"""				)
       SCREAMING_SNAKE_CASE__      							=				WavaVecaFeatureExtractor(**_lowercase				)

       # save in new folder
       model_config.save_pretrained(_lowercase				)
       config.save_pretrained(_lowercase				)

       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)

       # make sure private variable is not incorrectly saved
       SCREAMING_SNAKE_CASE__      							=				json.loads(config.to_json_string()				)
       self.assertTrue("""_processor_class""" not in dict_as_saved				)

     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
     self.assertIsInstance(_lowercase	,     _lowercase				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     """bert-base is not a local folder and is not a valid model identifier"""				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""bert-base"""				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase	,     revision="""aaaaaa"""				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     with self.assertRaisesRegex(
         _lowercase	,     """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""	,     ):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained("""hf-internal-testing/config-no-model"""				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""				)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(_lowercase				):
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
         """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
     self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)

     # Test feature extractor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
       feature_extractor.save_pretrained(_lowercase				)
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase	,     trust_remote_code=_lowercase				)
     self.assertEqual(reloaded_feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     try:
       AutoConfig.register("""custom"""	,     _lowercase				)
       AutoFeatureExtractor.register(_lowercase	,     _lowercase				)
       # Trying to register something existing in the Transformers library will raise an error
       with self.assertRaises(_lowercase				):
         AutoFeatureExtractor.register(_lowercase	,     _lowercase				)

       # Now that the config is registered, it can be used as any other config with the auto-API
       SCREAMING_SNAKE_CASE__      							=				CustomFeatureExtractor.from_pretrained(_lowercase				)
       with tempfile.TemporaryDirectory() as tmp_dir:
         feature_extractor.save_pretrained(_lowercase				)
         SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(_lowercase				)
         self.assertIsInstance(_lowercase	,     _lowercase				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
         del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]




   def      __a						(   self      :    Any				):



     """simple docstring"""






     class __snake_case    (						lowerCamelCase_		):
        lowerCAmelCase_														=							True

     try:
       AutoConfig.register("""custom"""	,     _lowercase				)
       AutoFeatureExtractor.register(_lowercase	,     _lowercase				)
       # If remote code is not set, the default is to use local
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(feature_extractor.is_local				)

       # If remote code is disabled, we load the local one.
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(feature_extractor.is_local				)

       # If remote is enabled, we load from the Hub
       SCREAMING_SNAKE_CASE__      							=				AutoFeatureExtractor.from_pretrained(
           """hf-internal-testing/test_dynamic_feature_extractor"""	,     trust_remote_code=_lowercase				)
       self.assertEqual(feature_extractor.__class__.__name__	,     """NewFeatureExtractor"""				)
       self.assertTrue(not hasattr(_lowercase	,     """is_local"""				)				)

     finally:
       if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
       if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
         del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
