






"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


UpperCAmelCase_       :							List[Any]             =				random.Random()

if is_torch_available():
							import torch






def 					_lowerCAmelCase(a   :    Tuple			,      a   :    List[str]=1.0			,      a   :    List[Any]=None			,      a   :    int=None  )      ->       Optional[int]:
	if rng is None:
		_SCREAMING_SNAKE_CASE						=global_rng

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

	return values


class 	__UpperCAmelCase		(				unittest.TestCase	):


	'''simple docstring'''





	def __init__(     self      ,      _A      ,      _A=7      ,      _A=4_0_0      ,      _A=2_0_0_0      ,      _A=1      ,      _A=0.0      ,      _A=1_6_0_0_0      ,      _A=True      ,      _A=True      ,      ):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=parent
		_SCREAMING_SNAKE_CASE						=batch_size
		_SCREAMING_SNAKE_CASE						=min_seq_length
		_SCREAMING_SNAKE_CASE						=max_seq_length
		_SCREAMING_SNAKE_CASE						=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
		_SCREAMING_SNAKE_CASE						=feature_size
		_SCREAMING_SNAKE_CASE						=padding_value
		_SCREAMING_SNAKE_CASE						=sampling_rate
		_SCREAMING_SNAKE_CASE						=return_attention_mask
		_SCREAMING_SNAKE_CASE						=do_normalize


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		return {
		    "feature_size": self.feature_size,
		    "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      ,      _A=False      ,      _A=False					):



		'''simple docstring'''

		def _flatten(_A					):
			return list(itertools.chain(*_A					)					)

		if equal_length:
			_SCREAMING_SNAKE_CASE						=floats_list((self.batch_size, self.max_seq_length)					)
		else:
			# make sure that inputs increase in size
			_SCREAMING_SNAKE_CASE						=[
			    _flatten(floats_list((x, self.feature_size)					)					)
			    for x in range(self.min_seq_length      ,      self.max_seq_length      ,      self.seq_length_diff					)
			]

		if numpify:
			_SCREAMING_SNAKE_CASE						=[np.asarray(_A					) for x in speech_inputs]

		return speech_inputs




@require_torch
@require_torchaudio
class 	__UpperCAmelCase		(				snake_case_,  unittest.TestCase	):


	'''simple docstring'''





	lowercase		:	List[Any]							     =				ASTFeatureExtractor


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=ASTFeatureExtractionTester(self					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
		# create three inputs of length 800, 1000, and 1200
		_SCREAMING_SNAKE_CASE						=[floats_list((1, x)					)[0] for x in range(8_0_0      ,      1_4_0_0      ,      2_0_0					)]
		_SCREAMING_SNAKE_CASE						=[np.asarray(_A					) for speech_input in speech_inputs]

		# Test not batched input
		_SCREAMING_SNAKE_CASE						=feat_extract(speech_inputs[0]      ,      return_tensors='''np'''					).input_values
		_SCREAMING_SNAKE_CASE						=feat_extract(np_speech_inputs[0]      ,      return_tensors='''np'''					).input_values
		self.assertTrue(np.allclose(_A      ,      _A      ,      atol=1E-3					)					)

		# Test batched
		_SCREAMING_SNAKE_CASE						=feat_extract(_A      ,      padding=_A      ,      return_tensors='''np'''					).input_values
		_SCREAMING_SNAKE_CASE						=feat_extract(_A      ,      padding=_A      ,      return_tensors='''np'''					).input_values
		for enc_seq_a, enc_seq_a in zip(_A      ,      _A					):
			self.assertTrue(np.allclose(_A      ,      _A      ,      atol=1E-3					)					)

		# Test 2-D numpy arrays are batched.
		_SCREAMING_SNAKE_CASE						=[floats_list((1, x)					)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
		_SCREAMING_SNAKE_CASE						=np.asarray(_A					)
		_SCREAMING_SNAKE_CASE						=feat_extract(_A      ,      return_tensors='''np'''					).input_values
		_SCREAMING_SNAKE_CASE						=feat_extract(_A      ,      return_tensors='''np'''					).input_values
		for enc_seq_a, enc_seq_a in zip(_A      ,      _A					):
			self.assertTrue(np.allclose(_A      ,      _A      ,      atol=1E-3					)					)


	@require_torch
	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		import torch

		_SCREAMING_SNAKE_CASE						=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
		_SCREAMING_SNAKE_CASE						=np.random.rand(1_0_0					).astype(np.floataa					)
		_SCREAMING_SNAKE_CASE						=np_speech_inputs.tolist()

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


	def 				UpperCamelCase_  (     self      ,      _A					):



		'''simple docstring'''

		from datasets import load_dataset

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

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







	@require_torch
	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=torch.tensor(
		    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
		     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
		     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
		     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]					)
		# fmt: on

		_SCREAMING_SNAKE_CASE						=self._load_datasamples(1					)
		_SCREAMING_SNAKE_CASE						=ASTFeatureExtractor()
		_SCREAMING_SNAKE_CASE						=feature_extractor(_A      ,      return_tensors='''pt'''					).input_values
		self.assertEquals(input_values.shape      ,      (1, 1_0_2_4, 1_2_8)					)
		self.assertTrue(torch.allclose(input_values[0, 0, :3_0]      ,      _A      ,      atol=1E-4					)					)












from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				list(range(len(a_)))
							snake_case_           =				[v / w for v, w in zip(a_				,	a_)]
							index.sort(key=lambda a_: ratio[i]				,	reverse=a_)

							snake_case_           =				0
							snake_case_           =				[0] * len(a_)
							for i in index:
														if weight[i] <= capacity:
																					snake_case_           =				1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					snake_case_           =				capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()





