
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


lowerCamelCase__          =		random.Random()

if is_torch_available():
					import torch
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=1.0       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None     )				->		Union[str, Any]:



			if rng is None:
						lowerCAmelCase__      :      int         =		global_rng

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

			return values







class      A__							(     unittest.TestCase	):






				def __init__(						self					:      Dict    ,     a					:      Optional[int]    ,     a					:      Tuple=7    ,     a					:      Optional[Any]=400    ,     a					:      Optional[int]=2_000    ,     a					:      Any=1    ,     a					:      List[Any]=0.0    ,     a					:      Tuple=16_000    ,     a					:      Any=True    ,     a					:      List[Any]=True    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		parent
							lowerCAmelCase__      :      Dict         =		batch_size
							lowerCAmelCase__      :      int         =		min_seq_length
							lowerCAmelCase__      :      str         =		max_seq_length
							lowerCAmelCase__      :      Union[str, Any]         =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCAmelCase__      :      str         =		feature_size
							lowerCAmelCase__      :      Optional[Any]         =		padding_value
							lowerCAmelCase__      :      Union[str, Any]         =		sampling_rate
							lowerCAmelCase__      :      List[Any]         =		return_attention_mask
							lowerCAmelCase__      :      str         =		do_normalize






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''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 							_lowerCamelCase		(						self					:      Tuple    ,     a					:      List[Any]=False    ,     a					:      str=False   ):






							'''simple docstring'''







							def _flatten(a					:      Tuple   ):
										return list(itertools.chain(*a   )   )

							if equal_length:
										lowerCAmelCase__      :      Optional[int]         =		floats_list((self.batch_size, self.max_seq_length)   )
							else:
										# make sure that inputs increase in size
										lowerCAmelCase__      :      Optional[Any]         =		[
										    _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:
										lowerCAmelCase__      :      List[str]         =		[np.asarray(a   ) for x in speech_inputs]

							return speech_inputs







@require_torch
@require_torchaudio
class      A__							(     snake_case__						,   unittest.TestCase	):
				lowercase			    =  ASTFeatureExtractor






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		ASTFeatureExtractionTester(self   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Dict         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase__      :      Union[str, Any]         =		[floats_list((1, x)   )[0] for x in range(800    ,     1_400    ,     200   )]
							lowerCAmelCase__      :      Union[str, Any]         =		[np.asarray(a   ) for speech_input in speech_inputs]

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

							# Test batched
							lowerCAmelCase__      :      int         =		feat_extract(a    ,     padding=a    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      Optional[int]         =		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.
							lowerCAmelCase__      :      Dict         =		[floats_list((1, x)   )[0] for x in (800, 800, 800)]
							lowerCAmelCase__      :      List[Any]         =		np.asarray(a   )
							lowerCAmelCase__      :      Any         =		feat_extract(a    ,     return_tensors='np'   ).input_values
							lowerCAmelCase__      :      Optional[int]         =		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 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							import torch

							lowerCAmelCase__      :      int         =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							lowerCAmelCase__      :      Optional[int]         =		np.random.rand(100   ).astype(np.floataa   )
							lowerCAmelCase__      :      Optional[Any]         =		np_speech_inputs.tolist()

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






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      Any   ):






							'''simple docstring'''







							from datasets import load_dataset

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

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






				@require_torch
				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      int         =		torch.tensor(
							    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
							     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
							     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
							     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]   )
							# fmt: on

							lowerCAmelCase__      :      Optional[int]         =		self._load_datasamples(1   )
							lowerCAmelCase__      :      List[str]         =		ASTFeatureExtractor()
							lowerCAmelCase__      :      Tuple         =		feature_extractor(a    ,     return_tensors='pt'   ).input_values
							self.assertEquals(input_values.shape    ,     (1, 1_024, 128)   )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,     a    ,     atol=1E-4   )   )







class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{}  # Mapping from char to TrieNode
							_UpperCAmelCase									=			False

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							for word in words:
													self.insert(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self
							for char in word:
													if char not in curr.nodes:
																			_UpperCAmelCase									=			TrieNode()
													_UpperCAmelCase									=			curr.nodes[char]
							_UpperCAmelCase									=			True

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self
							for char in word:
													if char not in curr.nodes:
																			return False
													_UpperCAmelCase									=			curr.nodes[char]
							return curr.is_leaf

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							def _delete(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    ) -> bool:
													if index == len(UpperCAmelCase    ):
																			# If word does not exist
																			if not curr.is_leaf:
																									return False
																			_UpperCAmelCase									=			False
																			return len(curr.nodes    ) == 0
													_UpperCAmelCase									=			word[index]
													_UpperCAmelCase									=			curr.nodes.get(UpperCAmelCase    )
													# If char not in current trie node
													if not char_node:
																			return False
													# Flag to check if node can be deleted
													_UpperCAmelCase									=			_delete(UpperCAmelCase       ,    UpperCAmelCase       ,    index + 1    )
													if delete_curr:
																			del curr.nodes[char]
																			return len(curr.nodes    ) == 0
													return delete_curr

							_delete(self       ,    UpperCAmelCase       ,    0    )



def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						if node.is_leaf:
												print(__lowerCAmelCase ,		end=' '	)

						for key, value in node.nodes.items():
												print_words(__lowerCAmelCase ,		word + key	)


def __A    (      )->  bool:





						"""simple docstring"""



						_UpperCAmelCase									=			'banana bananas bandana band apple all beast'.split()
						_UpperCAmelCase									=			TrieNode()
						root.insert_many(__lowerCAmelCase	)
						# print_words(root, "")
						assert all(root.find(__lowerCAmelCase	) for word in words	)
						assert root.find('banana'	)
						assert not root.find('bandanas'	)
						assert not root.find('apps'	)
						assert root.find('apple'	)
						assert root.find('all'	)
						root.delete('all'	)
						assert not root.find('all'	)
						root.delete('banana'	)
						assert not root.find('banana'	)
						assert root.find('bananas'	)
						return True


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  None:





						"""simple docstring"""



						print(str(__lowerCAmelCase	) ,		'works!' if passes else 'doesn\'t work :('	)


def __A    (      )->  None:





						"""simple docstring"""



						assert test_trie()


def __A    (      )->  None:





						"""simple docstring"""



						print_results('Testing trie functionality' ,		test_trie()	)


if __name__ == "__main__":
		main()
