




from typing import Dict, 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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCAmelCase						:   int		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""pixel_values"""]
      def __init__(			self   :List[str] ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Optional[Dict[str, int]] = None ,      lowerCamelCase   :PILImageResampling = PILImageResampling.BICUBIC ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Union[int, float] = 1 / 255 ,      lowerCamelCase   :Dict[str, int] = None ,      lowerCamelCase   :bool = True ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      **lowerCamelCase   :Tuple ,      )     ->			None:
       super().__init__(**lowerCamelCase      )
       UpperCAmelCase__							 =						size if size is not None else {"height": 224, "width": 224}
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase      )
       UpperCAmelCase__							 =						crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      default_to_square=lowerCamelCase ,      param_name="crop_size"      )

       UpperCAmelCase__							 =						do_resize
       UpperCAmelCase__							 =						do_rescale
       UpperCAmelCase__							 =						do_normalize
       UpperCAmelCase__							 =						do_center_crop
       UpperCAmelCase__							 =						crop_size
       UpperCAmelCase__							 =						size
       UpperCAmelCase__							 =						resample
       UpperCAmelCase__							 =						rescale_factor
       UpperCAmelCase__							 =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
       UpperCAmelCase__							 =						image_std if image_std is not None else IMAGENET_DEFAULT_STD
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Dict[str, int] ,      lowerCamelCase   :PILImageResampling = PILImageResampling.BILINEAR ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :Tuple ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase      )

       if "shortest_edge" in size:
        UpperCAmelCase__							 =						get_resize_output_image_size(lowerCamelCase ,      size=size["shortest_edge"] ,      default_to_square=lowerCamelCase      )
        # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
       elif "height" in size and "width" in size:
        UpperCAmelCase__							 =						(size["height"], size["width"])
       else:
        raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''      )
       return resize(lowerCamelCase ,      size=lowerCamelCase ,      resample=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Dict[str, int] ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :List[Any] ,      )     ->			np.ndarray:
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase      )
       if "height" not in size or "width" not in size:
        raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''      )
       return center_crop(lowerCamelCase ,      size=(size["height"], size["width"]) ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :float ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :List[str]      )     ->			np.ndarray:
       return rescale(lowerCamelCase ,      scale=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :np.ndarray ,      lowerCamelCase   :Union[float, List[float]] ,      lowerCamelCase   :Union[float, List[float]] ,      lowerCamelCase   :Optional[Union[str, ChannelDimension]] = None ,      **lowerCamelCase   :Dict ,      )     ->			np.ndarray:
       return normalize(lowerCamelCase ,      mean=lowerCamelCase ,      std=lowerCamelCase ,      data_format=lowerCamelCase ,      **lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :str ,      lowerCamelCase   :ImageInput ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Dict[str, int] = None ,      lowerCamelCase   :PILImageResampling = None ,      lowerCamelCase   :bool = None ,      lowerCamelCase   :int = None ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[float] = None ,      lowerCamelCase   :Optional[bool] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[float, List[float]]] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :Union[str, ChannelDimension] = ChannelDimension.FIRST ,      **lowerCamelCase   :str ,      )     ->			BatchFeature:
       UpperCAmelCase__							 =						do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase__							 =						do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase__							 =						do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase__							 =						do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCAmelCase__							 =						crop_size if crop_size is not None else self.crop_size
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase ,      param_name="crop_size" ,      default_to_square=lowerCamelCase      )
       UpperCAmelCase__							 =						resample if resample is not None else self.resample
       UpperCAmelCase__							 =						rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase__							 =						image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase__							 =						image_std if image_std is not None else self.image_std

       UpperCAmelCase__							 =						size if size is not None else self.size
       UpperCAmelCase__							 =						get_size_dict(lowerCamelCase      )

       if not is_batched(lowerCamelCase      ):
        UpperCAmelCase__							 =						[images]

       if not valid_images(lowerCamelCase      ):
        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."      )

       # All transformations expect numpy arrays.
       UpperCAmelCase__							 =						[to_numpy_array(lowerCamelCase      ) for image in images]

       if do_resize:
        UpperCAmelCase__							 =						[self.resize(image=lowerCamelCase ,      size=lowerCamelCase ,      resample=lowerCamelCase      ) for image in images]

       if do_center_crop:
        UpperCAmelCase__							 =						[self.center_crop(image=lowerCamelCase ,      size=lowerCamelCase      ) for image in images]

       if do_rescale:
        UpperCAmelCase__							 =						[self.rescale(image=lowerCamelCase ,      scale=lowerCamelCase      ) for image in images]

       if do_normalize:
        UpperCAmelCase__							 =						[self.normalize(image=lowerCamelCase ,      mean=lowerCamelCase ,      std=lowerCamelCase      ) for image in images]

       UpperCAmelCase__							 =						[to_channel_dimension_format(lowerCamelCase ,      lowerCamelCase      ) for image in images]

       UpperCAmelCase__							 =						{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase ,      tensor_type=lowerCamelCase      )











import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class 			_UpperCamelCase      (				lowerCAmelCase		):
      # to overwrite at feature extractactor specific tests
      UpperCAmelCase_        =		None
      UpperCAmelCase_        =		None
      @property
      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       return self.feat_extract_tester.prepare_feat_extract_dict()
      def 	UpperCAmelCase_						(			self   :Any      )     ->			str:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       self.assertTrue(hasattr(lowerCamelCase ,      "feature_size"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "sampling_rate"      )      )
       self.assertTrue(hasattr(lowerCamelCase ,      "padding_value"      )      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       self.assertTrue(all(len(lowerCamelCase      ) == len(lowerCamelCase      ) for x, y in zip(lowerCamelCase ,      processed_features[input_name]      )      )      )

       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="np"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="pt"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCamelCase      )
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs} ,      tensor_type="tf"      )

       UpperCAmelCase__							 =						processed_features[input_name]

       if len(batch_features_input.shape      ) < 3:
        UpperCAmelCase__							 =						batch_features_input[:, :, None]

       self.assertTrue(
           batch_features_input.shape
           == (self.feat_extract_tester.batch_size, len(speech_inputs[0]      ), self.feat_extract_tester.feature_size)      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Union[str, Any]      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Dict ,      lowerCamelCase   :Optional[Any]      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						self.feat_extract_tester.seq_length_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.max_seq_length + pad_diff
       UpperCAmelCase__							 =						self.feat_extract_tester.min_seq_length
       UpperCAmelCase__							 =						self.feat_extract_tester.batch_size
       UpperCAmelCase__							 =						self.feat_extract_tester.feature_size

       # test padding for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest"      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[-1]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       # max_length parameter has to be provided when setting `padding="max_length"`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length"      )[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )
       self.assertTrue(len(input_a[0]      ) == pad_min_length      )
       self.assertTrue(len(input_a[1]      ) == pad_min_length + pad_diff      )
       self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]      ))      )
       self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size      )

       # test padding for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      pad_to_multiple_of=10      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      pad_to_multiple_of=10 ,      max_length=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(all(len(lowerCamelCase      ) % 10 == 0 for x in input_a      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       UpperCAmelCase__							 =						pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
       self.assertTrue(all(len(lowerCamelCase      ) == expected_mult_pad_length for x in input_a      )      )
       self.assertEqual(input_a.shape[:2] ,      (batch_size, expected_mult_pad_length)      )

       if feature_size > 1:
        self.assertTrue(input_a.shape[2] == feature_size      )

       # Check padding value is correct
       UpperCAmelCase__							 =						(np.ones(self.feat_extract_tester.feature_size      ) * feat_extract.padding_value).sum()
       self.assertTrue(
           abs(np.asarray(input_a[0]      )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[1]      )[pad_min_length + pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(
               np.asarray(input_a[2]      )[pad_min_length + 2 * pad_diff :].sum()
               - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)      )
           < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)      ) < 1e-3      )
       self.assertTrue(
           abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)      )
           < 1e-3      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :int=False      )     ->			str:
       def _inputs_have_equal_length(lowerCamelCase   :Any      ):
        UpperCAmelCase__							 =						len(input[0]      )
        for input_slice in input[1:]:
         if len(lowerCamelCase      ) != length:
          return False
        return True

       def _inputs_are_equal(lowerCamelCase   :Optional[int] ,      lowerCamelCase   :str      ):
        if len(lowerCamelCase      ) != len(lowerCamelCase      ):
         return False

        for input_slice_a, input_slice_a in zip(lowerCamelCase ,      lowerCamelCase      ):
         if not np.allclose(np.asarray(lowerCamelCase      ) ,      np.asarray(lowerCamelCase      ) ,      atol=1e-3      ):
          return False
        return True

       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common(numpify=lowerCamelCase      )
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       # truncate to smallest
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      )      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to smallest with np
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np" ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(input_a.shape[1] == len(speech_inputs[0]      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )

       # truncate to middle
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase ,      return_tensors="np" ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      truncation=lowerCamelCase      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[1]      ) ,      return_tensors="np"      )
       UpperCAmelCase__							 =						input_a[input_name]

       self.assertTrue(input_a.shape[1] == len(speech_inputs[1]      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(_inputs_are_equal(lowerCamelCase ,      lowerCamelCase      )      )

       # since truncation forces padding to be smaller than longest input
       # function can't return `np.ndarray`, but has to return list
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertTrue(len(input_a[-1]      ) == len(speech_inputs[-1]      )      )

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # padding has to be max_length when setting `truncation=True`
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="longest" ,      truncation=lowerCamelCase      )[input_name]

       # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
       with self.assertRaises(lowerCamelCase      ):
        feat_extract.pad(lowerCamelCase ,      padding="max_length" ,      truncation=lowerCamelCase      )[input_name]

       # test truncation for `pad_to_multiple_of` for List[int] + numpy
       UpperCAmelCase__							 =						12
       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      truncation=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=len(speech_inputs[0]      ) ,      pad_to_multiple_of=lowerCamelCase ,      )
       UpperCAmelCase__							 =						input_a[input_name]

       # retrieve expected_length as multiple of pad_to_multiple_of
       UpperCAmelCase__							 =						len(speech_inputs[0]      )
       if expected_length % pad_to_multiple_of != 0:
        UpperCAmelCase__							 =						((len(speech_inputs[0]      ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

       self.assertTrue(len(input_a[0]      ) == expected_length      )
       self.assertTrue(_inputs_have_equal_length(lowerCamelCase      )      )
       self.assertFalse(_inputs_have_equal_length(lowerCamelCase      )      )
      def 	UpperCAmelCase_						(			self   :int      )     ->			List[str]:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			int:
       self._check_padding(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :str      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			str:
       self._check_truncation(numpify=lowerCamelCase      )
      @require_torch
      def 	UpperCAmelCase_						(			self   :int      )     ->			Any:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="pt"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_pt.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      @require_tf
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Optional[Any]:
       UpperCAmelCase__							 =						self.feature_extraction_class(**self.feat_extract_dict      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )[input_name]
       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="tf"      )[input_name]

       self.assertTrue(abs(input_np.astype(np.floataa      ).sum() - input_tf.numpy().astype(np.floataa      ).sum()      ) < 1e-2      )
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )

       UpperCAmelCase__							 =						feat_extract.pad(lowerCamelCase ,      padding="longest" ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(list(processed.attention_mask.shape      ) ,      list(processed[input_name].shape[:2]      )      )
       self.assertListEqual(processed.attention_mask.sum(-1      ).tolist() ,      lowerCamelCase      )







      def 	UpperCAmelCase_						(			self   :int      )     ->			int:
       UpperCAmelCase__							 =						self.feat_extract_dict
       UpperCAmelCase__							 =						True
       UpperCAmelCase__							 =						self.feature_extraction_class(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.feat_extract_tester.prepare_inputs_for_common()
       UpperCAmelCase__							 =						[len(lowerCamelCase      ) for x in speech_inputs]
       UpperCAmelCase__							 =						feat_extract.model_input_names[0]

       UpperCAmelCase__							 =						BatchFeature({input_name: speech_inputs}      )
       UpperCAmelCase__							 =						min(lowerCamelCase      )

       UpperCAmelCase__							 =						feat_extract.pad(
           lowerCamelCase ,      padding="max_length" ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      return_tensors="np"      )
       self.assertIn("attention_mask" ,      lowerCamelCase      )
       self.assertListEqual(
           list(processed_pad.attention_mask.shape      ) ,      [processed_pad[input_name].shape[0], max_length]      )
       self.assertListEqual(
           processed_pad.attention_mask[:, :max_length].sum(-1      ).tolist() ,      [max_length for x in speech_inputs]      )





