
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):
							# to overwrite at feature extractactor specific tests
							__lowercase      		=       None
							__lowercase      		=       None


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self.feat_extract_tester.prepare_feat_extract_dict()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

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

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

								_snake_case   						=		processed_features[input_name]

								if len(batch_features_input.shape  ) < 3:
									_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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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

								_snake_case   						=		processed_features[input_name]

								if len(batch_features_input.shape  ) < 3:
									_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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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

								_snake_case   						=		processed_features[input_name]

								if len(batch_features_input.shape  ) < 3:
									_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  lowerCamelCase				(       self    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






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

								def _inputs_are_equal(lowerCAmelCase_    ,   lowerCAmelCase_  ):
									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

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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

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

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

								_snake_case   						=		feat_extract.pad(lowerCAmelCase_    ,   padding='longest'  )
								_snake_case   						=		input_a[input_name]

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

								_snake_case   						=		feat_extract.pad(lowerCAmelCase_    ,   padding='longest'    ,   return_tensors='np'  )
								_snake_case   						=		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]

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

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

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

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

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

								_snake_case   						=		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
								_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  lowerCamelCase				(       self    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






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

								def _inputs_are_equal(lowerCAmelCase_    ,   lowerCAmelCase_  ):
									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

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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

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

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

								self.assertTrue(_inputs_have_equal_length(lowerCAmelCase_  )  )
								self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_  )  )

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

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

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

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

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

								# retrieve expected_length as multiple of pad_to_multiple_of
								_snake_case   						=		len(speech_inputs[0]  )
								if expected_length % pad_to_multiple_of != 0:
									_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(lowerCAmelCase_  )  )
								self.assertFalse(_inputs_have_equal_length(lowerCAmelCase_  )  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self._check_padding(numpify=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self._check_padding(numpify=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self._check_truncation(numpify=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self._check_truncation(numpify=lowerCAmelCase_  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

								_snake_case   						=		feat_extract.pad(lowerCAmelCase_    ,   padding='longest'    ,   return_tensors='np'  )[input_name]
								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

								_snake_case   						=		feat_extract.pad(lowerCAmelCase_    ,   padding='longest'    ,   return_tensors='np'  )[input_name]
								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

								_snake_case   						=		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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )
								_snake_case   						=		min(lowerCAmelCase_  )

								_snake_case   						=		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]  )





import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard







@slow
@require_flax
class       lowerCamelCase__   (       unittest.TestCase):





     def      __A					(self       )     ->   List[Any]:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()





     def      __A					(self       )     ->   Optional[Any]:
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
          '''/sd2-inpaint/init_image.png'''       )
      _lowercase							      =load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'''       )

      _lowercase							      ='''xvjiarui/stable-diffusion-2-inpainting'''
      _lowercase      ,    _lowercase							      =FlaxStableDiffusionInpaintPipeline.from_pretrained(UpperCAmelCase		,     safety_checker=UpperCAmelCase       )

      _lowercase							      ='''Face of a yellow cat, high resolution, sitting on a park bench'''

      _lowercase							      =jax.random.PRNGKey(0       )
      _lowercase							      =5_0

      _lowercase							      =jax.device_count()
      _lowercase							      =num_samples * [prompt]
      _lowercase							      =num_samples * [init_image]
      _lowercase							      =num_samples * [mask_image]
      _lowercase      ,    _lowercase      ,    _lowercase							      =pipeline.prepare_inputs(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )

      # shard inputs and rng
      _lowercase							      =replicate(UpperCAmelCase       )
      _lowercase							      =jax.random.split(UpperCAmelCase		,     jax.device_count()       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )
      _lowercase							      =shard(UpperCAmelCase       )

      _lowercase							      =pipeline(
          UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     jit=UpperCAmelCase       )

      _lowercase							      =output.images.reshape(UpperCAmelCase		,     5_1_2		,     5_1_2		,     3       )

      _lowercase							      =images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

      _lowercase							      =jnp.asarray(jax.device_get(image_slice.flatten()       )       )
      _lowercase							      =jnp.array(
          [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084]       )
      print(f"output_slice: {output_slice}"       )
      assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2

