
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch


snake_case__  							=							random.Random()

def        lowerCamelCase_      (			UpperCAmelCase_							:				int							,			UpperCAmelCase_							:				Union[str, Any]=1.0							,			UpperCAmelCase_							:				Optional[Any]=None							,			UpperCAmelCase_							:				Optional[Any]=None       )     ->   int:

						if rng is None:
												lowercase       :    Any											=  global_rng

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

						return values







@require_torch
class 							UpperCAmelCase	(   unittest.TestCase					):

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					Dict ,      lowerCAmelCase      :					Optional[Any]=7 ,      lowerCAmelCase      :					List[Any]=400 ,      lowerCAmelCase      :					Optional[int]=2000 ,      lowerCAmelCase      :					Optional[Any]=1 ,      lowerCAmelCase      :					List[Any]=0.0 ,      lowerCAmelCase      :					Any=2_4000 ,      lowerCAmelCase      :					List[Any]=True ,      ):
											lowercase       :    Dict											=  parent
											lowercase       :    Tuple											=  batch_size
											lowercase       :    str											=  min_seq_length
											lowercase       :    Optional[Any]											=  max_seq_length
											lowercase       :    int											=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
											lowercase       :    List[Any]											=  feature_size
											lowercase       :    int											=  padding_value
											lowercase       :    Optional[Any]											=  sampling_rate
											lowercase       :    List[str]											=  return_attention_mask

					def        _lowerCAmelCase	(			self      :					str					):
											return {
											    "feature_size": self.feature_size,
											    "padding_value": self.padding_value,
											    "sampling_rate": self.sampling_rate,
											    "return_attention_mask": self.return_attention_mask,
											}




					def        _lowerCAmelCase	(			self      :					List[str] ,      lowerCAmelCase      :					Any=False ,      lowerCAmelCase      :					Any=False					):







											def _flatten(lowerCAmelCase      :					Optional[Any]					):
																	return list(itertools.chain(*__A					)					)

											if equal_length:
																	lowercase       :    Optional[int]											=  floats_list((self.batch_size, self.max_seq_length)					)
											else:
																	# make sure that inputs increase in size
																	lowercase       :    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:
																	lowercase       :    int											=  [np.asarray(__A					) for x in audio_inputs]

											return audio_inputs







@require_torch
class 							UpperCAmelCase	(   a__						, unittest.TestCase					):
					a__: Union[str, Any]			  =				EncodecFeatureExtractor

					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    str											=  EnCodecFeatureExtractionTester(self					)

					def        _lowerCAmelCase	(			self      :					List[Any]					):
											lowercase       :    Optional[int]											=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
											# create three inputs of length 800, 1000, and 1200
											lowercase       :    Optional[Any]											=  [floats_list((1, x)					)[0] for x in range(800 ,      1400 ,      200					)]
											lowercase       :    Optional[Any]											=  [np.asarray(__A					) for audio_input in audio_inputs]

											# Test not batched input
											lowercase       :    List[str]											=  feat_extract(audio_inputs[0] ,      return_tensors='''np'''					).input_values
											lowercase       :    Union[str, Any]											=  feat_extract(np_audio_inputs[0] ,      return_tensors='''np'''					).input_values
											self.assertTrue(np.allclose(__A ,      __A ,      atol=1E-3					)					)

											# Test batched
											lowercase       :    Optional[int]											=  feat_extract(__A ,      padding=__A ,      return_tensors='''np'''					).input_values
											lowercase       :    List[str]											=  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					)					)

					def        _lowerCAmelCase	(			self      :					Any					):
											lowercase       :    Optional[Any]											=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
											lowercase       :    Tuple											=  np.random.rand(100					).astype(np.floataa					)
											lowercase       :    Any											=  np_audio_inputs.tolist()

											for inputs in [py_audio_inputs, np_audio_inputs]:
																	lowercase       :    int											=  feature_extractor.pad([{'''input_values''': inputs}] ,      return_tensors='''np'''					)
																	self.assertTrue(np_processed.input_values.dtype == np.floataa					)
																	lowercase       :    List[Any]											=  feature_extractor.pad([{'''input_values''': inputs}] ,      return_tensors='''pt'''					)
																	self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)

					def        _lowerCAmelCase	(			self      :					List[str] ,      lowerCAmelCase      :					int					):
											from datasets import load_dataset

											lowercase       :    List[Any]											=  load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,      '''clean''' ,      split='''validation'''					)
											# automatic decoding with librispeech
											lowercase       :    str											=  ds.sort('''id'''					).select(range(__A					)					)[:num_samples]["audio"]

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

					def        _lowerCAmelCase	(			self      :					Any					):
											lowercase       :    Dict											=  torch.tensor(
											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]					)
											# fmt: on
											lowercase       :    Union[str, Any]											=  self._load_datasamples(1					)
											lowercase       :    str											=  EncodecFeatureExtractor()
											lowercase       :    Optional[int]											=  feature_extractor(__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(input_values.shape ,      (1, 1, 9_3680)					)
											self.assertTrue(torch.allclose(input_values[0, 0, :30] ,      __A ,      atol=1E-6					)					)

					def        _lowerCAmelCase	(			self      :					int					):
											lowercase       :    Optional[Any]											=  torch.tensor(
											    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
											     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
											     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
											     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
											     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
											     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]					)
											# fmt: on
											lowercase       :    Union[str, Any]											=  self._load_datasamples(1					)
											lowercase       :    str											=  [np.tile(input_audio[0][None] ,      reps=(2, 1)					)]
											input_audio[0][1] *= 0.5
											lowercase       :    Optional[int]											=  EncodecFeatureExtractor(feature_size=2					)
											lowercase       :    str											=  feature_extractor(__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(input_values.shape ,      (1, 2, 9_3680)					)
											self.assertTrue(torch.allclose(input_values[0, 0, :30] ,      __A ,      atol=1E-6					)					)
											self.assertTrue(torch.allclose(input_values[0, 1, :30] ,      EXPECTED_INPUT_VALUES * 0.5 ,      atol=1E-6					)					)




					def        _lowerCAmelCase	(			self      :					List[str]					):
											lowercase       :    str											=  self._load_datasamples(2					)
											# would be easier if the stride was like
											lowercase       :    List[Any]											=  EncodecFeatureExtractor(feature_size=1 ,      chunk_length_s=1 ,      overlap=0.01					)

											# pad and trunc raise an error ?
											with self.assertRaisesRegex(
											    __A ,      '''^Both padding and truncation were set. Make sure you only set one.$''' ,      ):
																	lowercase       :    Dict											=  feature_extractor(
																	    __A ,      padding='''max_length''' ,      truncation=__A ,      return_tensors='''pt'''					).input_values

											# truncate to chunk
											lowercase       :    Optional[Any]											=  feature_extractor(__A ,      truncation=__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (2, 1, 7_1520)					)  # 2 chunks

											# force truncate to max_length
											lowercase       :    List[str]											=  feature_extractor(
											    __A ,      truncation=__A ,      max_length=4_8000 ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (2, 1, 4_8000)					)

											# pad to chunk
											lowercase       :    List[str]											=  feature_extractor(__A ,      padding=__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(padded_outputs.shape ,      (2, 1, 9_5280)					)

											# pad to chunk
											lowercase       :    Optional[Any]											=  feature_extractor(__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (2, 1, 9_5280)					)

											# force pad to max length
											lowercase       :    int											=  feature_extractor(
											    __A ,      padding='''max_length''' ,      max_length=10_0000 ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (2, 1, 10_0000)					)

											# force no pad
											with self.assertRaisesRegex(
											    __A ,      '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$''' ,      ):
																	lowercase       :    Optional[Any]											=  feature_extractor(__A ,      padding=__A ,      return_tensors='''pt'''					).input_values

											lowercase       :    int											=  feature_extractor(input_audio[0] ,      padding=__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (1, 1, 9_3680)					)

											# no pad if no chunk_length_s
											lowercase       :    Any											=  None
											with self.assertRaisesRegex(
											    __A ,      '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$''' ,      ):
																	lowercase       :    int											=  feature_extractor(__A ,      padding=__A ,      return_tensors='''pt'''					).input_values

											lowercase       :    Any											=  feature_extractor(input_audio[0] ,      padding=__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (1, 1, 9_3680)					)

											# no pad if no overlap
											lowercase       :    List[Any]											=  2
											lowercase       :    Any											=  None
											with self.assertRaisesRegex(
											    __A ,      '''^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$''' ,      ):
																	lowercase       :    Optional[int]											=  feature_extractor(__A ,      padding=__A ,      return_tensors='''pt'''					).input_values

											lowercase       :    List[str]											=  feature_extractor(input_audio[0] ,      padding=__A ,      return_tensors='''pt'''					).input_values
											self.assertEquals(truncated_outputs.shape ,      (1, 1, 9_3680)					)




class 							UpperCAmelCase	:

					def __init__(			self      :					Any ,      lowerCAmelCase      :					int					):
											lowercase       :    Tuple											=  data
											lowercase       :    BinaryTreeNode | None											=  None
											lowercase       :    BinaryTreeNode | None											=  None





def        lowerCamelCase_      (			UpperCAmelCase_							:				BinaryTreeNode | None							,			UpperCAmelCase_							:				int       ):
						if node is None:
												lowercase       :    Union[str, Any]											=  BinaryTreeNode(UpperCAmelCase_       )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowercase       :    List[Any]											=  insert(node.left_child							,			UpperCAmelCase_       )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowercase       :    Dict											=  insert(node.right_child							,			UpperCAmelCase_       )
						return node

def        lowerCamelCase_      (			UpperCAmelCase_							:				None | BinaryTreeNode       ):  # if node is None,return
						if node:
												lowercase       :    Optional[Any]											=  inorder(node.left_child       )
												lowercase       :    Tuple											=  [*inorder_array, node.data]
												lowercase       :    Optional[Any]											=  inorder_array + inorder(node.right_child       )
						else:
												lowercase       :    Optional[int]											=  []
						return inorder_array

def        lowerCamelCase_      (			):
						lowercase       :    Any											=  insert(UpperCAmelCase_							,			15       )
						insert(UpperCAmelCase_							,			10       )
						insert(UpperCAmelCase_							,			25       )
						insert(UpperCAmelCase_							,			6       )
						insert(UpperCAmelCase_							,			14       )
						insert(UpperCAmelCase_							,			20       )
						insert(UpperCAmelCase_							,			60       )
						return root

def        lowerCamelCase_      (			):
						# main function
						lowercase       :    Any											=  make_tree()
						print('''Printing values of binary search tree in Inorder Traversal.'''       )
						inorder(UpperCAmelCase_       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()


