



import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			str			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			str=sys.maxsize)	->				Any:




         """simple docstring"""

         lowercase__     =  'bilinear'
         lowercase__     =  max_size
         lowercase__     =  short_edge_length



   def __call__(						self  :			Optional[Any]			,				lowerCAmelCase  :			Union[str, Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  []
         for img in imgs:
               lowercase__,	lowercase__     =  img.shape[:2]
               # later: provide list and randomly choose index for resize
               lowercase__     =  np.random.randint(self.short_edge_length[0]			,				self.short_edge_length[1] + 1)
               if size == 0:
                     return img
               lowercase__     =  size * 1.0 / min(_lowercase			,				_lowercase)
               if h < w:
                     lowercase__,	lowercase__     =  size, scale * w
               else:
                     lowercase__,	lowercase__     =  scale * h, size
               if max(_lowercase			,				_lowercase) > self.max_size:
                     lowercase__     =  self.max_size * 1.0 / max(_lowercase			,				_lowercase)
                     lowercase__     =  newh * scale
                     lowercase__     =  neww * scale
               lowercase__     =  int(neww + 0.5)
               lowercase__     =  int(newh + 0.5)

               if img.dtype == np.uinta:
                     lowercase__     =  Image.fromarray(_lowercase)
                     lowercase__     =  pil_image.resize((neww, newh)			,				PILImageResampling.BILINEAR)
                     lowercase__     =  np.asarray(_lowercase)
               else:
                     lowercase__     =  img.permute(2			,				0			,				1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                     lowercase__     =  nn.functional.interpolate(
                         _lowercase			,				(newh, neww)			,				mode=self.interp_method			,				align_corners=_lowercase).squeeze(0)
               img_augs.append(_lowercase)

         return img_augs



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			Union[str, Any]			,				lowerCAmelCase  :			int)	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]			,				cfg.INPUT.MAX_SIZE_TEST)
         lowercase__     =  cfg.INPUT.FORMAT
         lowercase__     =  cfg.SIZE_DIVISIBILITY
         lowercase__     =  cfg.PAD_VALUE
         lowercase__     =  cfg.INPUT.MAX_SIZE_TEST
         lowercase__     =  cfg.MODEL.DEVICE
         lowercase__     =  torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)			,				1			,				1)
         lowercase__     =  torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)			,				1			,				1)
         lowercase__     =  lambda lowerCAmelCase: (x - self.pixel_mean) / self.pixel_std


   def 			UpperCAmelCase		(						self  :			Any			,				lowerCAmelCase  :			str)	->				Any:




         """simple docstring"""

         lowercase__     =  tuple(max(_lowercase) for s in zip(*[img.shape for img in images]))
         lowercase__     =  [im.shape[-2:] for im in images]
         lowercase__     =  [
             nn.functional.pad(
                 _lowercase			,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,				value=self.pad_value			,				)
             for size, im in zip(_lowercase			,				_lowercase)
         ]

         return torch.stack(_lowercase), torch.tensor(_lowercase)



   def __call__(						self  :			Tuple			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Dict=False)	->				Optional[int]:




         """simple docstring"""

         with torch.no_grad():
               if not isinstance(_lowercase			,				_lowercase):
                     lowercase__     =  [images]
               if single_image:
                     assert len(_lowercase) == 1
               for i in range(len(_lowercase)):
                     if isinstance(images[i]			,				torch.Tensor):
                           images.insert(_lowercase			,				images.pop(_lowercase).to(self.device).float())
                     elif not isinstance(images[i]			,				torch.Tensor):
                           images.insert(
                               _lowercase			,				torch.as_tensor(img_tensorize(images.pop(_lowercase)			,				input_format=self.input_format))
                               .to(self.device)
                               .float()			,				)
            # resize smallest edge
               lowercase__     =  torch.tensor([im.shape[:2] for im in images])
               lowercase__     =  self.aug(_lowercase)
               # transpose images and convert to torch tensors
               # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
               # now normalize before pad to avoid useless arithmetic
               lowercase__     =  [self.normalizer(_lowercase) for x in images]
               # now pad them to do the following operations
               lowercase__,	lowercase__     =  self.pad(_lowercase)
               # Normalize

               if self.size_divisibility > 0:
                     raise NotImplementedError()
               # pad
               lowercase__     =  torch.true_divide(_lowercase			,				_lowercase)
               if single_image:
                     return images[0], sizes[0], scales_yx[0]
               else:
                     return images, sizes, scales_yx

def   _lowerCAmelCase   (						A__						,  A__	):
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes

def   _lowerCAmelCase   (						A__						,  A__	):
      assert torch.isfinite(UpperCamelCase__	).all(), "Box tensor contains infinite or NaN!"
      lowercase__,	lowercase__     =  box_size
      tensor[:, 0].clamp_(min=0						,  max=UpperCamelCase__	)
      tensor[:, 1].clamp_(min=0						,  max=UpperCamelCase__	)
      tensor[:, 2].clamp_(min=0						,  max=UpperCamelCase__	)
      tensor[:, 3].clamp_(min=0						,  max=UpperCamelCase__	)





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	):

   '''simple docstring'''




   A    :  List[Any]       				=    None
   A    :  Optional[int]       				=    None


   @property
   def 			UpperCAmelCase		(						self  :			str)	->				Union[str, Any]:




         """simple docstring"""

         return self.feat_extract_tester.prepare_feat_extract_dict()


   def 			UpperCAmelCase		(						self  :			int)	->				Any:




         """simple docstring"""

         lowercase__     =  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  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

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

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='np')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  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)	->				int:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='pt')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  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])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common(equal_length=lowerCAmelCase)
         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs}			,				tensor_type='tf')

         lowercase__     =  processed_features[input_name]

         if len(batch_features_input.shape) < 3:
               lowercase__     =  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  :			str			,				lowerCAmelCase  :			str=False)	->				Union[str, Any]:




         """simple docstring"""

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

         def _inputs_are_equal(lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Tuple):
               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

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

         lowercase__     =  BatchFeature({input_name: speech_inputs})

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

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

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest')
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[-1]))
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')
         lowercase__     =  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]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=lowerCAmelCase			,				return_tensors='np')
         lowercase__     =  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
         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				pad_to_multiple_of=10)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				pad_to_multiple_of=10			,				max_length=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

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

         lowercase__     =  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
         lowercase__     =  (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  :			Tuple			,				lowerCAmelCase  :			Dict=False)	->				str:




         """simple docstring"""

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

         def _inputs_are_equal(lowerCAmelCase  :			str			,				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

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

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         # truncate to smallest
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0]))
         lowercase__     =  input_a[input_name]

         self.assertTrue(_inputs_have_equal_length(lowerCAmelCase))
         self.assertFalse(_inputs_have_equal_length(lowerCAmelCase))

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

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				return_tensors='np')
         lowercase__     =  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
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase			,				return_tensors='np'			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				truncation=lowerCAmelCase)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[1])			,				return_tensors='np')
         lowercase__     =  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
         lowercase__     =  12
         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				truncation=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         lowercase__     =  feat_extract.pad(
             lowerCAmelCase			,				padding='max_length'			,				max_length=len(speech_inputs[0])			,				pad_to_multiple_of=lowerCAmelCase			,				)
         lowercase__     =  input_a[input_name]

         # retrieve expected_length as multiple of pad_to_multiple_of
         lowercase__     =  len(speech_inputs[0])
         if expected_length % pad_to_multiple_of != 0:
               lowercase__     =  ((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  :			List[str])	->				List[str]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Optional[Any]:




         """simple docstring"""

         self._check_padding(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				int:




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				Dict:




         """simple docstring"""

         self._check_truncation(numpify=lowerCAmelCase)


   @require_torch
   def 			UpperCAmelCase		(						self  :			Dict)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  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  :			str)	->				str:




         """simple docstring"""

         lowercase__     =  self.feature_extraction_class(**self.feat_extract_dict)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  feat_extract.pad(lowerCAmelCase			,				padding='longest'			,				return_tensors='np')[input_name]
         lowercase__     =  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  :			Optional[Any])	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})

         lowercase__     =  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  :			str)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.feat_extract_dict
         lowercase__     =  True
         lowercase__     =  self.feature_extraction_class(**lowerCAmelCase)
         lowercase__     =  self.feat_extract_tester.prepare_inputs_for_common()
         lowercase__     =  [len(lowerCAmelCase) for x in speech_inputs]
         lowercase__     =  feat_extract.model_input_names[0]

         lowercase__     =  BatchFeature({input_name: speech_inputs})
         lowercase__     =  min(lowerCAmelCase)

         lowercase__     =  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])
