

'''simple docstring'''



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 						lowerCAmelCase__   (      a					):



 """simple docstring"""



 lowerCAmelCase__  					=      None
 lowerCAmelCase__  					=      None
 @property
 def       UpperCAmelCase__		(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return self.feat_extract_tester.prepare_feat_extract_dict()
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.feature_extraction_class(**self.feat_extract_dict			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""feature_size"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""sampling_rate"""			)			)
      self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE					,							"""padding_value"""			)			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Tuple:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)

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

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

      __SCREAMING_SNAKE_CASE     =      processed_features[input_name]

      if len(batch_features_input.shape			) < 3:
           __SCREAMING_SNAKE_CASE     =      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			)  -> Dict:


      """simple docstring"""




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

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

      __SCREAMING_SNAKE_CASE     =      processed_features[input_name]

      if len(batch_features_input.shape			) < 3:
           __SCREAMING_SNAKE_CASE     =      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					:       int			)  -> List[str]:


      """simple docstring"""




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

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

      __SCREAMING_SNAKE_CASE     =      processed_features[input_name]

      if len(batch_features_input.shape			) < 3:
           __SCREAMING_SNAKE_CASE     =      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[str]					,							__SCREAMING_SNAKE_CASE					:       Dict=False			)  -> Optional[int]:


      """simple docstring"""




      def _inputs_have_equal_length(__SCREAMING_SNAKE_CASE					:       Union[str, Any]			):
           __SCREAMING_SNAKE_CASE     =      len(input[0]			)
           for input_slice in input[1:]:
                if len(__SCREAMING_SNAKE_CASE			) != length:
                     return False
           return True

      def _inputs_are_equal(__SCREAMING_SNAKE_CASE					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]			):
           if len(__SCREAMING_SNAKE_CASE			) != len(__SCREAMING_SNAKE_CASE			):
                return False

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

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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)

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

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

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

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

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

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

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

      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
      self.assertTrue(_inputs_are_equal(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)			)
      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
      __SCREAMING_SNAKE_CASE     =      feat_extract.pad(__SCREAMING_SNAKE_CASE					,							pad_to_multiple_of=10			)
      __SCREAMING_SNAKE_CASE     =      input_a[input_name]

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

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

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

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

      __SCREAMING_SNAKE_CASE     =      pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
      self.assertTrue(all(len(__SCREAMING_SNAKE_CASE			) == 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
      __SCREAMING_SNAKE_CASE     =      (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					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       str=False			)  -> Optional[int]:


      """simple docstring"""




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

      def _inputs_are_equal(__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       int			):
           if len(__SCREAMING_SNAKE_CASE			) != len(__SCREAMING_SNAKE_CASE			):
                return False

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

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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)

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

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

      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)

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

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

      self.assertTrue(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
      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(__SCREAMING_SNAKE_CASE			)			)

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

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

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

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

      # 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(__SCREAMING_SNAKE_CASE			)			)
      self.assertTrue(len(input_a[-1]			) == len(speech_inputs[-1]			)			)

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

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

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

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

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

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

      # retrieve expected_length as multiple of pad_to_multiple_of
      __SCREAMING_SNAKE_CASE     =      len(speech_inputs[0]			)
      if expected_length % pad_to_multiple_of != 0:
           __SCREAMING_SNAKE_CASE     =      ((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(__SCREAMING_SNAKE_CASE			)			)
      self.assertFalse(_inputs_have_equal_length(__SCREAMING_SNAKE_CASE			)			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[Any]:


      """simple docstring"""




      self._check_padding(numpify=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> Union[str, Any]:


      """simple docstring"""




      self._check_padding(numpify=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> int:


      """simple docstring"""




      self._check_truncation(numpify=__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       int			)  -> Optional[Any]:


      """simple docstring"""




      self._check_truncation(numpify=__SCREAMING_SNAKE_CASE			)
 @require_torch
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)

      __SCREAMING_SNAKE_CASE     =      feat_extract.pad(__SCREAMING_SNAKE_CASE					,							padding="""longest"""					,							return_tensors="""np"""			)[input_name]
      __SCREAMING_SNAKE_CASE     =      feat_extract.pad(__SCREAMING_SNAKE_CASE					,							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					:       Optional[int]			)  -> int:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)

      __SCREAMING_SNAKE_CASE     =      feat_extract.pad(__SCREAMING_SNAKE_CASE					,							padding="""longest"""					,							return_tensors="""np"""			)[input_name]
      __SCREAMING_SNAKE_CASE     =      feat_extract.pad(__SCREAMING_SNAKE_CASE					,							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					:       Union[str, Any]			)  -> Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)

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


 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Tuple:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     =      BatchFeature({input_name: speech_inputs}			)
      __SCREAMING_SNAKE_CASE     =      min(__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      feat_extract.pad(
          __SCREAMING_SNAKE_CASE					,							padding="""max_length"""					,							max_length=__SCREAMING_SNAKE_CASE					,							truncation=__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			)
      self.assertIn("""attention_mask"""					,							__SCREAMING_SNAKE_CASE			)
      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]			)









'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 						lowerCAmelCase__   (      a ,					a ,					a ,					unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      StableDiffusionInpaintPipeline
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCAmelCase__  					=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__  					=      frozenset(
     []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowerCAmelCase__  					=      frozenset([]					)
 def       UpperCAmelCase__		(		self					:       str			)  -> List[Any]:


      """simple docstring"""




      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      UNetaDConditionModel(
          block_out_channels=(32, 64)					,							layers_per_block=2					,							sample_size=32					,							in_channels=9					,							out_channels=4					,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")					,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")					,							cross_attention_dim=32					,							attention_head_dim=(2, 4)					,							use_linear_projection=__SCREAMING_SNAKE_CASE					,							)
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE			)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      AutoencoderKL(
          block_out_channels=[32, 64]					,							in_channels=3					,							out_channels=3					,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]					,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]					,							latent_channels=4					,							sample_size=128					,							)
      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      CLIPTextConfig(
          bos_token_id=0					,							eos_token_id=2					,							hidden_size=32					,							intermediate_size=37					,							layer_norm_eps=1E-05					,							num_attention_heads=4					,							num_hidden_layers=5					,							pad_token_id=1					,							vocab_size=1_000					,							hidden_act="""gelu"""					,							projection_dim=512					,							)
      __SCREAMING_SNAKE_CASE     =      CLIPTextModel(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

      __SCREAMING_SNAKE_CASE     =      {
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components
 def       UpperCAmelCase__		(		self					:       Any					,							__SCREAMING_SNAKE_CASE					:       Tuple					,							__SCREAMING_SNAKE_CASE					:       List[Any]=0			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)					,							rng=random.Random(__SCREAMING_SNAKE_CASE			)			).to(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      image.cpu().permute(0					,							2					,							3					,							1			)[0]
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE			)			).convert("""RGB"""			).resize((64, 64)			)
      __SCREAMING_SNAKE_CASE     =      Image.fromarray(np.uinta(image + 4			)			).convert("""RGB"""			).resize((64, 64)			)
      if str(__SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
           __SCREAMING_SNAKE_CASE     =      torch.manual_seed(__SCREAMING_SNAKE_CASE			)
      else:
           __SCREAMING_SNAKE_CASE     =      torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": init_image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
      }
      return inputs
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE     =      self.get_dummy_components()
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline(**__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe.to(__SCREAMING_SNAKE_CASE			)
      sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.get_dummy_inputs(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      sd_pipe(**__SCREAMING_SNAKE_CASE			).images
      __SCREAMING_SNAKE_CASE     =      image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)
      __SCREAMING_SNAKE_CASE     =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]			)

      assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      super().test_inference_batch_single_identical(expected_max_diff=3E-3			)







@slow
@require_torch_gpu
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> str:


      """simple docstring"""




      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(__SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE			)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 9E-3
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
          """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							safety_checker=__SCREAMING_SNAKE_CASE					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							output_type="""np"""					,							)
      __SCREAMING_SNAKE_CASE     =      output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image			).max() < 5E-1


 def       UpperCAmelCase__		(		self					:       Tuple			)  -> Any:


      """simple docstring"""




      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-inpaint/init_image.png"""			)
      __SCREAMING_SNAKE_CASE     =      load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""			)

      __SCREAMING_SNAKE_CASE     =      """stabilityai/stable-diffusion-2-inpainting"""
      __SCREAMING_SNAKE_CASE     =      PNDMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE					,							subfolder="""scheduler"""			)
      __SCREAMING_SNAKE_CASE     =      StableDiffusionInpaintPipeline.from_pretrained(
          __SCREAMING_SNAKE_CASE					,							safety_checker=__SCREAMING_SNAKE_CASE					,							scheduler=__SCREAMING_SNAKE_CASE					,							torch_dtype=torch.floataa					,							)
      pipe.to(__SCREAMING_SNAKE_CASE			)
      pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
      pipe.enable_attention_slicing(1			)
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE     =      """Face of a yellow cat, high resolution, sitting on a park bench"""

      __SCREAMING_SNAKE_CASE     =      torch.manual_seed(0			)
      __SCREAMING_SNAKE_CASE     =      pipe(
          prompt=__SCREAMING_SNAKE_CASE					,							image=__SCREAMING_SNAKE_CASE					,							mask_image=__SCREAMING_SNAKE_CASE					,							generator=__SCREAMING_SNAKE_CASE					,							num_inference_steps=2					,							output_type="""np"""					,							)

      __SCREAMING_SNAKE_CASE     =      torch.cuda.max_memory_allocated()
      # make sure that less than 2.65 GB is allocated
      assert mem_bytes < 2.65 * 10**9






