import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor

lowerCamelCase__			= random.Random()

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			List[str]=1.0				,     _SCREAMING_SNAKE_CASE       :			int=None				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]=None    ):
				"""simple docstring"""







				if rng is None:
								__a       =		global_rng

				__a       =		[]
				for batch_idx in range(shape[0]    ):
								values.append([]    )
								for _ in range(shape[1]    ):
												values[-1].append(rng.random() * scale    )

				return values






@require_torch
@require_torchaudio
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def __init__(   self		:		Dict      ,				__lowercase		:		List[Any]      ,				__lowercase		:		Dict=7      ,				__lowercase		:		Tuple=400      ,				__lowercase		:		int=2000      ,				__lowercase		:		Optional[int]=24      ,				__lowercase		:		List[str]=24      ,				__lowercase		:		Any=0.0      ,				__lowercase		:		Union[str, Any]=16000      ,				__lowercase		:		Tuple=True      ,				__lowercase		:		Any=True      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		batch_size
											__a       =		min_seq_length
											__a       =		max_seq_length
											__a       =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											__a       =		feature_size
											__a       =		num_mel_bins
											__a       =		padding_value
											__a       =		sampling_rate
											__a       =		return_attention_mask
											__a       =		do_normalize





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											return {
											    "feature_size": self.feature_size,
											    "num_mel_bins": self.num_mel_bins,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "return_attention_mask": self.return_attention_mask,
											    "do_normalize": self.do_normalize,
											}







							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		int=False      ,				__lowercase		:		List[str]=False	):

											'''simple docstring'''
											def _flatten(__lowercase		:		List[str]	):
															return list(itertools.chain(*__lowercase	)	)

											if equal_length:
															__a       =		[floats_list((self.max_seq_length, self.feature_size)	) for _ in range(self.batch_size	)]
											else:
															# make sure that inputs increase in size
															__a       =		[
															    floats_list((x, self.feature_size)	)
															    for x in range(self.min_seq_length      ,				self.max_seq_length      ,				self.seq_length_diff	)
															]
											if numpify:
															__a       =		[np.asarray(__lowercase	) for x in speech_inputs]
											return speech_inputs






@require_torch
@require_torchaudio
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      unittest.TestCase					):
							__lowerCamelCase	:	int    =SpeechaTextFeatureExtractor if is_speech_available() else None





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		SpeechaTextFeatureExtractionTester(self	)





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		int	):

											'''simple docstring'''
											self.assertTrue(np.all(np.mean(__lowercase      ,				axis=0	) < 1E-3	)	)
											self.assertTrue(np.all(np.abs(np.var(__lowercase      ,				axis=0	) - 1	) < 1E-3	)	)





							def 						UpperCamelCase_					(   self		:		Optional[int]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											# create three inputs of length 800, 1000, and 1200
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]
											__a       =		[np.asarray(__lowercase	) for speech_input in speech_inputs]

											# Test feature size
											__a       =		feature_extractor(__lowercase      ,				padding=__lowercase      ,				return_tensors="""np"""	).input_features
											self.assertTrue(input_features.ndim == 3	)
											self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size	)

											# Test not batched input
											__a       =		feature_extractor(speech_inputs[0]      ,				return_tensors="""np"""	).input_features
											__a       =		feature_extractor(np_speech_inputs[0]      ,				return_tensors="""np"""	).input_features
											self.assertTrue(np.allclose(__lowercase      ,				__lowercase      ,				atol=1E-3	)	)

											# Test batched
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""	).input_features
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""	).input_features
											for enc_seq_a, enc_seq_a in zip(__lowercase      ,				__lowercase	):
															self.assertTrue(np.allclose(__lowercase      ,				__lowercase      ,				atol=1E-3	)	)

											# Test 2-D numpy arrays are batched.
											__a       =		[floats_list((1, x)	)[0] for x in (800, 800, 800)]
											__a       =		np.asarray(__lowercase	)
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""	).input_features
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""np"""	).input_features
											for enc_seq_a, enc_seq_a in zip(__lowercase      ,				__lowercase	):
															self.assertTrue(np.allclose(__lowercase      ,				__lowercase      ,				atol=1E-3	)	)





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]

											__a       =		["""longest""", """max_length""", """do_not_pad"""]
											__a       =		[None, 16, None]
											for max_length, padding in zip(__lowercase      ,				__lowercase	):
															__a       =		feature_extractor(
															    __lowercase      ,				padding=__lowercase      ,				max_length=__lowercase      ,				return_attention_mask=__lowercase	)
															__a       =		inputs.input_features
															__a       =		inputs.attention_mask
															__a       =		[np.sum(__lowercase	) for x in attention_mask]

															self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
															self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
															self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)





							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]

											__a       =		["""longest""", """max_length""", """do_not_pad"""]
											__a       =		[None, 16, None]
											for max_length, padding in zip(__lowercase      ,				__lowercase	):
															__a       =		feature_extractor(
															    __lowercase      ,				max_length=__lowercase      ,				padding=__lowercase      ,				return_tensors="""np"""      ,				return_attention_mask=__lowercase	)
															__a       =		inputs.input_features
															__a       =		inputs.attention_mask
															__a       =		[np.sum(__lowercase	) for x in attention_mask]

															self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]	)
															self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6	)
															self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]	)
															self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6	)
															self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]	)





							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]
											__a       =		feature_extractor(
											    __lowercase      ,				padding="""max_length"""      ,				max_length=4      ,				truncation=__lowercase      ,				return_tensors="""np"""      ,				return_attention_mask=__lowercase      ,				)
											__a       =		inputs.input_features
											__a       =		inputs.attention_mask
											__a       =		np.sum(attention_mask == 1      ,				axis=1	)

											self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
											self._check_zero_mean_unit_variance(input_features[1]	)
											self._check_zero_mean_unit_variance(input_features[2]	)





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]
											__a       =		feature_extractor(
											    __lowercase      ,				padding="""longest"""      ,				max_length=4      ,				truncation=__lowercase      ,				return_tensors="""np"""      ,				return_attention_mask=__lowercase      ,				)
											__a       =		inputs.input_features
											__a       =		inputs.attention_mask
											__a       =		np.sum(attention_mask == 1      ,				axis=1	)

											self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
											self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
											self._check_zero_mean_unit_variance(input_features[2]	)

											# make sure that if max_length < longest -> then pad to max_length
											self.assertEqual(input_features.shape      ,				(3, 4, 24)	)

											__a       =		[floats_list((1, x)	)[0] for x in range(800      ,				1400      ,				200	)]
											__a       =		feature_extractor(
											    __lowercase      ,				padding="""longest"""      ,				max_length=16      ,				truncation=__lowercase      ,				return_tensors="""np"""      ,				return_attention_mask=__lowercase      ,				)
											__a       =		inputs.input_features
											__a       =		inputs.attention_mask
											__a       =		np.sum(attention_mask == 1      ,				axis=1	)

											self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]	)
											self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]	)
											self._check_zero_mean_unit_variance(input_features[2]	)

											# make sure that if max_length < longest -> then pad to max_length
											self.assertEqual(input_features.shape      ,				(3, 6, 24)	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											import torch

											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__a       =		np.random.rand(100      ,				32	).astype(np.floataa	)
											__a       =		np_speech_inputs.tolist()

											for inputs in [py_speech_inputs, np_speech_inputs]:
															__a       =		feature_extractor.pad([{"""input_features""": inputs}]      ,				return_tensors="""np"""	)
															self.assertTrue(np_processed.input_features.dtype == np.floataa	)
															__a       =		feature_extractor.pad([{"""input_features""": inputs}]      ,				return_tensors="""pt"""	)
															self.assertTrue(pt_processed.input_features.dtype == torch.floataa	)





							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											from datasets import load_dataset

											__a       =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,				"""clean"""      ,				split="""validation"""	)
											# automatic decoding with librispeech
											__a       =		ds.sort("""id"""	).select(range(__lowercase	)	)[:num_samples]["""audio"""]

											return [x["array"] for x in speech_samples]







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		np.array([
											    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
											    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
											    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
											]	)
											# fmt: on

											__a       =		self._load_datasamples(1	)
											__a       =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
											__a       =		feature_extractor(__lowercase      ,				return_tensors="""pt"""	).input_features
											self.assertEquals(input_features.shape      ,				(1, 584, 24)	)
											self.assertTrue(np.allclose(input_features[0, 0, :30]      ,				__lowercase      ,				atol=1E-4	)	)





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				if isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ) and isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
								__a       =		len(set_a.intersection(_SCREAMING_SNAKE_CASE    )    )

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
								else:
												__a       =		len(set_a.union(_SCREAMING_SNAKE_CASE    )    )

								return intersection / union

				if isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ) and isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ):
								__a       =		[element for element in set_a if element in set_b]

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
												return len(_SCREAMING_SNAKE_CASE    ) / union
								else:
												__a       =		set_a + [element for element in set_b if element not in set_a]
												return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )

								return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )
				return None


if __name__ == "__main__":
						lowerCamelCase__			= {"""a""", """b""", """c""", """d""", """e"""}
						lowerCamelCase__			= {"""c""", """d""", """e""", """f""", """h""", """i"""}
						print(jaccard_similarity(set_a, set_b))
