





'''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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Dict     =    None
	a      :			Optional[int]     =    None


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







				'''simple docstring'''





				return self.feat_extract_tester.prepare_feat_extract_dict()


	def   _UpperCAmelCase       (self	)							->							Dict:







				'''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	)							->							List[Any]:







				'''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	)							->							str:







				'''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	)							->							List[str]:







				'''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       ,_lowerCamelCase=False	)							->							Dict:







				'''simple docstring'''





				def _inputs_have_equal_length(_lowerCamelCase	):
							__lowercase			  =							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

				__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       ,_lowerCamelCase=False	)							->							Optional[Any]:







				'''simple docstring'''





				def _inputs_have_equal_length(_lowerCamelCase	):
							__lowercase			  =							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

				__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[Any]:







				'''simple docstring'''





				self._check_padding(numpify=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				self._check_padding(numpify=_lowerCamelCase	)


	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				self._check_truncation(numpify=_lowerCamelCase	)


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







				'''simple docstring'''





				self._check_truncation(numpify=_lowerCamelCase	)


	@require_torch
	def   _UpperCAmelCase       (self	)							->							int:







				'''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	)							->							List[Any]:







				'''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	)							->							List[Any]:







				'''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	)							->							List[Any]:







				'''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]	)












'''simple docstring'''



# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)





