






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							:   Dict							=  random.Random()

if is_torch_available():
	import torch





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							Optional[Any]=1.0	,	__snake_case     :							Optional[int]=None	,	__snake_case     :							int=None      )     -> Optional[Any]:





				'''simple docstring'''


				if rng is None:
								snake_case__      :List[str]         =   global_rng

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

				return values


class 					_snake_case						(			unittest.TestCase ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=7					,UpperCamelCase=400					,UpperCamelCase=2_000					,UpperCamelCase=1					,UpperCamelCase=0.0					,UpperCamelCase=16_000					,UpperCamelCase=True					,UpperCamelCase=True					,)						->    Any:
					snake_case__      :List[str]         =   parent
					snake_case__      :Optional[Any]         =   batch_size
					snake_case__      :List[str]         =   min_seq_length
					snake_case__      :str         =   max_seq_length
					snake_case__      :Union[str, Any]         =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					snake_case__      :List[str]         =   feature_size
					snake_case__      :Optional[Any]         =   padding_value
					snake_case__      :List[Any]         =   sampling_rate
					snake_case__      :Optional[Any]         =   return_attention_mask
					snake_case__      :List[str]         =   do_normalize






	def    lowerCAmelCase_							(		self       )						->    Any:
					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					,UpperCamelCase=False					,UpperCamelCase=False       )						->    Optional[Any]:
					def _flatten(UpperCamelCase       ):
									return list(itertools.chain(*UpperCamelCase       )       )

					if equal_length:
									snake_case__      :Any         =   floats_list((self.batch_size, self.max_seq_length)       )
					else:
									# make sure that inputs increase in size
									snake_case__      :Union[str, 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:
									snake_case__      :List[str]         =   [np.asarray(UpperCamelCase       ) for x in speech_inputs]

					return speech_inputs




@require_torch
@require_torchaudio
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   ASTFeatureExtractor






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					snake_case__      :Optional[int]         =   ASTFeatureExtractionTester(self       )






	def    lowerCAmelCase_							(		self       )						->    int:
					# Tests that all call wrap to encode_plus and batch_encode_plus
					snake_case__      :Optional[Any]         =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
					# create three inputs of length 800, 1000, and 1200
					snake_case__      :List[Any]         =   [floats_list((1, x)       )[0] for x in range(800					,1_400					,200       )]
					snake_case__      :Optional[int]         =   [np.asarray(UpperCamelCase       ) for speech_input in speech_inputs]

					# Test not batched input
					snake_case__      :List[str]         =   feat_extract(speech_inputs[0]					,return_tensors="np"       ).input_values
					snake_case__      :List[str]         =   feat_extract(np_speech_inputs[0]					,return_tensors="np"       ).input_values
					self.assertTrue(np.allclose(UpperCamelCase					,UpperCamelCase					,atol=1E-3       )       )

					# Test batched
					snake_case__      :Dict         =   feat_extract(UpperCamelCase					,padding=UpperCamelCase					,return_tensors="np"       ).input_values
					snake_case__      :List[Any]         =   feat_extract(UpperCamelCase					,padding=UpperCamelCase					,return_tensors="np"       ).input_values
					for enc_seq_a, enc_seq_a in zip(UpperCamelCase					,UpperCamelCase       ):
									self.assertTrue(np.allclose(UpperCamelCase					,UpperCamelCase					,atol=1E-3       )       )

					# Test 2-D numpy arrays are batched.
					snake_case__      :Tuple         =   [floats_list((1, x)       )[0] for x in (800, 800, 800)]
					snake_case__      :Dict         =   np.asarray(UpperCamelCase       )
					snake_case__      :Tuple         =   feat_extract(UpperCamelCase					,return_tensors="np"       ).input_values
					snake_case__      :Optional[Any]         =   feat_extract(UpperCamelCase					,return_tensors="np"       ).input_values
					for enc_seq_a, enc_seq_a in zip(UpperCamelCase					,UpperCamelCase       ):
									self.assertTrue(np.allclose(UpperCamelCase					,UpperCamelCase					,atol=1E-3       )       )






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					import torch

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

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    str:
					from datasets import load_dataset

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

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






	@require_torch
	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					# fmt: off
					snake_case__      :Optional[int]         =   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

					snake_case__      :Union[str, Any]         =   self._load_datasamples(1       )
					snake_case__      :Any         =   ASTFeatureExtractor()
					snake_case__      :str         =   feature_extractor(UpperCamelCase					,return_tensors="pt"       ).input_values
					self.assertEquals(input_values.shape					,(1, 1_024, 128)       )
					self.assertTrue(torch.allclose(input_values[0, 0, :30]					,UpperCamelCase					,atol=1E-4       )       )







from __future__ import annotations


class 					_snake_case						:






	def __init__(		self					,UpperCamelCase       )						->    None:
					snake_case__      :Union[str, Any]         =   data
					snake_case__      :Node | None         =   None
					snake_case__      :Node | None         =   None





def 					lowercase_ (    __snake_case     :							Node | None      )     -> None:  # In Order traversal of the tree





				'''simple docstring'''


				if tree:
								display(tree.left      )
								print(tree.data      )
								display(tree.right      )





def 					lowercase_ (    __snake_case     :							Node | None      )     -> int:





				'''simple docstring'''


				return 1 + max(depth_of_tree(tree.left      )	,	depth_of_tree(tree.right      )      ) if tree else 0





def 					lowercase_ (    __snake_case     :							Node      )     -> bool:





				'''simple docstring'''


				if not tree:
								return True
				if tree.left and tree.right:
								return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
				else:
								return not tree.left and not tree.right





def 					lowercase_ (    )     -> None:  # Main function for testing.





				'''simple docstring'''


				snake_case__      :Dict         =   Node(1      )
				snake_case__      :int         =   Node(2      )
				snake_case__      :Optional[Any]         =   Node(3      )
				snake_case__      :Tuple         =   Node(4      )
				snake_case__      :str         =   Node(5      )
				snake_case__      :Optional[Any]         =   Node(6      )
				snake_case__      :List[Any]         =   Node(7      )
				snake_case__      :List[str]         =   Node(8      )
				snake_case__      :Tuple         =   Node(9      )

				print(is_full_binary_tree(__snake_case      )      )
				print(depth_of_tree(__snake_case      )      )
				print("Tree is: "      )
				display(__snake_case      )


if __name__ == "__main__":
	main()