





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
A__:    List[Any]  	=	(720, 1280)  # Height, Width
A__:    Union[str, Any]  	=	(0.4, 0.6)  # if height or width lower than this scale, drop it.
A__:    List[Any]  	=	1 / 100
A__:    Any  	=	''''''
A__:    List[Any]  	=	''''''
A__:    Union[str, Any]  	=	''''''
A__:    Union[str, Any]  	=	250



def 				lowerCAmelCase_ (							):
    UpperCamelCase__				, UpperCamelCase__:	Dict					=     get_dataset(A_    ,A_)
    for index in range(A_):
        UpperCamelCase__:	Any					=     random.sample(range(len(A_))    ,4)
        UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Union[str, Any]					=     update_image_and_anno(
            A_    ,A_    ,A_    ,A_    ,A_    ,filter_scale=A_    ,)

        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        UpperCamelCase__:	Tuple					=     random_chars(32)
        UpperCamelCase__:	Tuple					=     path.split(os.sep)[-1].rsplit("."    ,1)[0]
        UpperCamelCase__:	Optional[int]					=     F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
        cva.imwrite(F"{file_root}.jpg"    ,A_    ,[cva.IMWRITE_JPEG_QUALITY, 85])
        print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}")
        UpperCamelCase__:	Union[str, Any]					=     []
        for anno in new_annos:
            UpperCamelCase__:	int					=     anno[3] - anno[1]
            UpperCamelCase__:	Any					=     anno[4] - anno[2]
            UpperCamelCase__:	Optional[Any]					=     anno[1] + width / 2
            UpperCamelCase__:	Optional[int]					=     anno[2] + height / 2
            UpperCamelCase__:	Union[str, Any]					=     F"{anno[0]} {x_center} {y_center} {width} {height}"
            annos_list.append(A_)
        with open(F"{file_root}.txt"    ,"w") as outfile:
            outfile.write("\n".join(line for line in annos_list))



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Any					=     []
    UpperCamelCase__:	List[Any]					=     []
    for label_file in glob.glob(os.path.join(A_    ,"*.txt")):
        UpperCamelCase__:	List[str]					=     label_file.split(os.sep)[-1].rsplit("."    ,1)[0]
        with open(A_) as in_file:
            UpperCamelCase__:	Optional[int]					=     in_file.readlines()
        UpperCamelCase__:	Any					=     os.path.join(A_    ,F"{label_name}.jpg")

        UpperCamelCase__:	Optional[Any]					=     []
        for obj_list in obj_lists:
            UpperCamelCase__:	Union[str, Any]					=     obj_list.rstrip("\n").split(" ")
            UpperCamelCase__:	str					=     float(obj[1]) - float(obj[3]) / 2
            UpperCamelCase__:	List[str]					=     float(obj[2]) - float(obj[4]) / 2
            UpperCamelCase__:	List[str]					=     float(obj[1]) + float(obj[3]) / 2
            UpperCamelCase__:	List[str]					=     float(obj[2]) + float(obj[4]) / 2

            boxes.append([int(obj[0]), xmin, ymin, xmax, ymax])
        if not boxes:
            continue
        img_paths.append(A_)
        labels.append(A_)
    return img_paths, labels



def 				lowerCAmelCase_ (							A_    ,A_    ,A_    ,A_    ,A_    ,A_ = 0.0    ,):
    UpperCamelCase__:	List[str]					=     np.zeros([output_size[0], output_size[1], 3]    ,dtype=np.uinta)
    UpperCamelCase__:	Tuple					=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__:	Tuple					=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
    UpperCamelCase__:	str					=     int(scale_x * output_size[1])
    UpperCamelCase__:	Tuple					=     int(scale_y * output_size[0])

    UpperCamelCase__:	List[Any]					=     []
    UpperCamelCase__:	List[str]					=     []
    for i, index in enumerate(A_):
        UpperCamelCase__:	Dict					=     all_img_list[index]
        path_list.append(A_)
        UpperCamelCase__:	List[Any]					=     all_annos[index]
        UpperCamelCase__:	List[Any]					=     cva.imread(A_)
        if i == 0:  # top-left
            UpperCamelCase__:	List[str]					=     cva.resize(A_    ,(divid_point_x, divid_point_y))
            UpperCamelCase__:	Dict					=     img
            for bbox in img_annos:
                UpperCamelCase__:	Union[str, Any]					=     bbox[1] * scale_x
                UpperCamelCase__:	str					=     bbox[2] * scale_y
                UpperCamelCase__:	Any					=     bbox[3] * scale_x
                UpperCamelCase__:	Optional[int]					=     bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
        elif i == 1:  # top-right
            UpperCamelCase__:	Dict					=     cva.resize(A_    ,(output_size[1] - divid_point_x, divid_point_y))
            UpperCamelCase__:	str					=     img
            for bbox in img_annos:
                UpperCamelCase__:	Any					=     scale_x + bbox[1] * (1 - scale_x)
                UpperCamelCase__:	List[str]					=     bbox[2] * scale_y
                UpperCamelCase__:	Optional[Any]					=     scale_x + bbox[3] * (1 - scale_x)
                UpperCamelCase__:	Any					=     bbox[4] * scale_y
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
        elif i == 2:  # bottom-left
            UpperCamelCase__:	Optional[Any]					=     cva.resize(A_    ,(divid_point_x, output_size[0] - divid_point_y))
            UpperCamelCase__:	Union[str, Any]					=     img
            for bbox in img_annos:
                UpperCamelCase__:	Optional[Any]					=     bbox[1] * scale_x
                UpperCamelCase__:	Optional[Any]					=     scale_y + bbox[2] * (1 - scale_y)
                UpperCamelCase__:	Optional[int]					=     bbox[3] * scale_x
                UpperCamelCase__:	str					=     scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])
        else:  # bottom-right
            UpperCamelCase__:	Optional[Any]					=     cva.resize(
                A_    ,(output_size[1] - divid_point_x, output_size[0] - divid_point_y))
            UpperCamelCase__:	Union[str, Any]					=     img
            for bbox in img_annos:
                UpperCamelCase__:	str					=     scale_x + bbox[1] * (1 - scale_x)
                UpperCamelCase__:	Union[str, Any]					=     scale_y + bbox[2] * (1 - scale_y)
                UpperCamelCase__:	Optional[int]					=     scale_x + bbox[3] * (1 - scale_x)
                UpperCamelCase__:	str					=     scale_y + bbox[4] * (1 - scale_y)
                new_anno.append([bbox[0], xmin, ymin, xmax, ymax])

    # Remove bounding box small than scale of filter
    if filter_scale > 0:
        UpperCamelCase__:	List[str]					=     [
            anno
            for anno in new_anno
            if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
        ]

    return output_img, new_anno, path_list[0]



def 				lowerCAmelCase_ (							A_):
    assert number_char > 1, "The number of character should greater than 1"
    UpperCamelCase__:	List[Any]					=     ascii_lowercase + digits
    return "".join(random.choice(A_) for _ in range(A_))


if __name__ == "__main__":
   main()
   print('''DONE ✅''')








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        _a    (    UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__  		=       None
      UpperCamelCase__  		=       None






      @property
      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          return self.feat_extract_tester.prepare_feat_extract_dict()






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     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:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     self.feat_extract_tester.prepare_inputs_for_common()
          UpperCamelCase__:	List[Any]					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	str					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Optional[int]					=     BatchFeature({input_name: speech_inputs}					)

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

          UpperCamelCase__:	Dict					=     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCamelCase					)
          UpperCamelCase__:	Any					=     BatchFeature({input_name: speech_inputs}	,    tensor_type="np"					)

          UpperCamelCase__:	List[str]					=     processed_features[input_name]

          if len(batch_features_input.shape					) < 3:
              UpperCamelCase__:	str					=     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:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCamelCase					)
          UpperCamelCase__:	Union[str, Any]					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	List[Any]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Optional[int]					=     BatchFeature({input_name: speech_inputs}	,    tensor_type="pt"					)

          UpperCamelCase__:	Dict					=     processed_features[input_name]

          if len(batch_features_input.shape					) < 3:
              UpperCamelCase__:	Tuple					=     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:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     self.feat_extract_tester.prepare_inputs_for_common(equal_length=__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	List[str]					=     feat_extract.model_input_names[0]

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

          UpperCamelCase__:	int					=     processed_features[input_name]

          if len(batch_features_input.shape					) < 3:
              UpperCamelCase__:	int					=     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:			List[Any]	,    __lowerCamelCase:			int=False					):
          '''simple docstring'''


          def _inputs_have_equal_length(__lowerCamelCase:			Optional[int]					):
              UpperCamelCase__:	Optional[int]					=     len(input[0]					)
              for input_slice in input[1:]:
                  if len(__lowerCamelCase					) != length:
                      return False
              return True

          def _inputs_are_equal(__lowerCamelCase:			List[str]	,    __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__:	List[Any]					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	Optional[int]					=     self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCamelCase					)
          UpperCamelCase__:	Optional[int]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Dict					=     BatchFeature({input_name: speech_inputs}					)

          UpperCamelCase__:	Dict					=     self.feat_extract_tester.seq_length_diff
          UpperCamelCase__:	List[Any]					=     self.feat_extract_tester.max_seq_length + pad_diff
          UpperCamelCase__:	List[str]					=     self.feat_extract_tester.min_seq_length
          UpperCamelCase__:	str					=     self.feat_extract_tester.batch_size
          UpperCamelCase__:	Tuple					=     self.feat_extract_tester.feature_size

          # test padding for List[int] + numpy
          UpperCamelCase__:	int					=     feat_extract.pad(__lowerCamelCase	,    padding=__lowerCamelCase					)
          UpperCamelCase__:	List[str]					=     input_a[input_name]

          UpperCamelCase__:	Dict					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"					)
          UpperCamelCase__:	int					=     input_a[input_name]

          UpperCamelCase__:	int					=     feat_extract.pad(__lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[-1]					)					)
          UpperCamelCase__:	Union[str, Any]					=     input_a[input_name]

          UpperCamelCase__:	Any					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    return_tensors="np"					)
          UpperCamelCase__:	Dict					=     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__:	Optional[Any]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=__lowerCamelCase	,    return_tensors="np"					)
          UpperCamelCase__:	Tuple					=     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__:	int					=     feat_extract.pad(__lowerCamelCase	,    pad_to_multiple_of=10					)
          UpperCamelCase__:	List[str]					=     input_a[input_name]

          UpperCamelCase__:	Optional[int]					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    pad_to_multiple_of=10					)
          UpperCamelCase__:	int					=     input_a[input_name]

          UpperCamelCase__:	Tuple					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    pad_to_multiple_of=10	,    max_length=__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     input_a[input_name]

          UpperCamelCase__:	Optional[int]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    pad_to_multiple_of=10	,    max_length=__lowerCamelCase	,    return_tensors="np"	,    )
          UpperCamelCase__:	Any					=     input_a[input_name]

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

          UpperCamelCase__:	Dict					=     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__:	List[str]					=     (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:			Union[str, Any]	,    __lowerCamelCase:			Dict=False					):
          '''simple docstring'''


          def _inputs_have_equal_length(__lowerCamelCase:			Dict					):
              UpperCamelCase__:	Optional[int]					=     len(input[0]					)
              for input_slice in input[1:]:
                  if len(__lowerCamelCase					) != length:
                      return False
              return True

          def _inputs_are_equal(__lowerCamelCase:			Tuple	,    __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__:	Any					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	Optional[Any]					=     self.feat_extract_tester.prepare_inputs_for_common(numpify=__lowerCamelCase					)
          UpperCamelCase__:	Optional[Any]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	List[str]					=     BatchFeature({input_name: speech_inputs}					)

          # truncate to smallest
          UpperCamelCase__:	List[str]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[0]					)	,    truncation=__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     input_a[input_name]

          UpperCamelCase__:	Optional[Any]					=     feat_extract.pad(__lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[0]					)					)
          UpperCamelCase__:	str					=     input_a[input_name]

          self.assertTrue(_inputs_have_equal_length(__lowerCamelCase					)					)
          self.assertFalse(_inputs_have_equal_length(__lowerCamelCase					)					)

          # truncate to smallest with np
          UpperCamelCase__:	Optional[Any]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[0]					)	,    return_tensors="np"	,    truncation=__lowerCamelCase	,    )
          UpperCamelCase__:	Dict					=     input_a[input_name]

          UpperCamelCase__:	Optional[int]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[0]					)	,    return_tensors="np"					)
          UpperCamelCase__:	Union[str, Any]					=     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__:	List[Any]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[1]					)	,    truncation=__lowerCamelCase	,    return_tensors="np"	,    )
          UpperCamelCase__:	str					=     input_a[input_name]

          UpperCamelCase__:	Union[str, Any]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[1]					)	,    truncation=__lowerCamelCase					)
          UpperCamelCase__:	Tuple					=     input_a[input_name]

          UpperCamelCase__:	Optional[int]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[1]					)	,    return_tensors="np"					)
          UpperCamelCase__:	Union[str, Any]					=     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__:	Tuple					=     12
          UpperCamelCase__:	Optional[int]					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[0]					)	,    pad_to_multiple_of=__lowerCamelCase	,    truncation=__lowerCamelCase	,    )
          UpperCamelCase__:	str					=     input_a[input_name]

          UpperCamelCase__:	str					=     feat_extract.pad(
              __lowerCamelCase	,    padding="max_length"	,    max_length=len(speech_inputs[0]					)	,    pad_to_multiple_of=__lowerCamelCase	,    )
          UpperCamelCase__:	List[Any]					=     input_a[input_name]

          # retrieve expected_length as multiple of pad_to_multiple_of
          UpperCamelCase__:	Optional[int]					=     len(speech_inputs[0]					)
          if expected_length % pad_to_multiple_of != 0:
              UpperCamelCase__:	int					=     ((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:			Tuple					):
          '''simple docstring'''


          self._check_padding(numpify=__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			str					):
          '''simple docstring'''


          self._check_padding(numpify=__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          self._check_truncation(numpify=__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          self._check_truncation(numpify=__lowerCamelCase					)






      @require_torch
      def 			UpperCAmelCase_       (		self:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	Optional[int]					=     self.feat_extract_tester.prepare_inputs_for_common()
          UpperCamelCase__:	Union[str, Any]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Tuple					=     BatchFeature({input_name: speech_inputs}					)

          UpperCamelCase__:	Tuple					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    return_tensors="np"					)[input_name]
          UpperCamelCase__:	int					=     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[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Any					=     self.feature_extraction_class(**self.feat_extract_dict					)
          UpperCamelCase__:	Union[str, Any]					=     self.feat_extract_tester.prepare_inputs_for_common()
          UpperCamelCase__:	str					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	List[Any]					=     BatchFeature({input_name: speech_inputs}					)

          UpperCamelCase__:	Union[str, Any]					=     feat_extract.pad(__lowerCamelCase	,    padding="longest"	,    return_tensors="np"					)[input_name]
          UpperCamelCase__:	List[Any]					=     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:			str					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     self.feat_extract_dict
          UpperCamelCase__:	List[str]					=     True
          UpperCamelCase__:	Optional[Any]					=     self.feature_extraction_class(**__lowerCamelCase					)
          UpperCamelCase__:	Union[str, Any]					=     self.feat_extract_tester.prepare_inputs_for_common()
          UpperCamelCase__:	int					=     [len(__lowerCamelCase					) for x in speech_inputs]
          UpperCamelCase__:	List[Any]					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Dict					=     BatchFeature({input_name: speech_inputs}					)

          UpperCamelCase__:	List[Any]					=     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:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     self.feat_extract_dict
          UpperCamelCase__:	List[Any]					=     True
          UpperCamelCase__:	Optional[int]					=     self.feature_extraction_class(**__lowerCamelCase					)
          UpperCamelCase__:	Any					=     self.feat_extract_tester.prepare_inputs_for_common()
          UpperCamelCase__:	List[Any]					=     [len(__lowerCamelCase					) for x in speech_inputs]
          UpperCamelCase__:	str					=     feat_extract.model_input_names[0]

          UpperCamelCase__:	Dict					=     BatchFeature({input_name: speech_inputs}					)
          UpperCamelCase__:	Optional[int]					=     min(__lowerCamelCase					)

          UpperCamelCase__:	Tuple					=     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]					)

