
'''simple docstring'''



class      A							:



			def __init__(   self    ,       lowerCamelCase__    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    val
									# Number of nodes in left subtree
									lowercase__								=    0
									lowercase__								=    None
									lowercase__								=    None
									lowercase__								=    None



			def A__	(   self    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



									assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

									if self.val < other.val:
															lowercase__								=    self.right
															lowercase__								=    None
															if self.right:
																					lowercase__								=    other
															lowercase__								=    other
															lowercase__								=    self.left_tree_size * 2 + 1
															return self
									else:
															lowercase__								=    other.right
															lowercase__								=    None
															if other.right:
																					lowercase__								=    self
															lowercase__								=    self
															lowercase__								=    other.left_tree_size * 2 + 1
															return other





class      A							:



			def __init__(   self    ,       lowerCamelCase__=None    ,       lowerCamelCase__=None    ,       lowerCamelCase__=0    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    heap_size
									lowercase__								=    bottom_root
									lowercase__								=    min_node



			def A__	(   self    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



									if other.size == 0:
															return None
									if self.size == 0:
															lowercase__								=    other.size
															lowercase__								=    other.bottom_root
															lowercase__								=    other.min_node
															return None
									# Update size
									lowercase__								=    self.size + other.size

									# Update min.node
									if self.min_node.val > other.min_node.val:
															lowercase__								=    other.min_node
									# Merge

									# Order roots by left_subtree_size
									lowercase__								=    []
									lowercase__	,  lowercase__								=    self.bottom_root, other.bottom_root
									while i or j:
															if i and ((not j) or i.left_tree_size < j.left_tree_size):
																					combined_roots_list.append((i, True)    )
																					lowercase__								=    i.parent
															else:
																					combined_roots_list.append((j, False)    )
																					lowercase__								=    j.parent
        # Insert links between them
									for i in range(len(lowerCamelCase__    ) - 1    ):
															if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
																					lowercase__								=    combined_roots_list[i + 1][0]
																					lowercase__								=    combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
									lowercase__								=    combined_roots_list[0][0]
									while i.parent:
															if (
															    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
															) or (
															    i.left_tree_size == i.parent.left_tree_size
															    and i.left_tree_size != i.parent.parent.left_tree_size
															):
																					# Neighbouring Nodes
																					lowercase__								=    i.left
																					lowercase__								=    i.parent.parent

																					# Merging trees
																					lowercase__								=    i.merge_trees(i.parent    )

																					# Updating links
																					lowercase__								=    previous_node
																					lowercase__								=    next_node
																					if previous_node:
																											lowercase__								=    i
																					if next_node:
																											lowercase__								=    i
															else:
																					lowercase__								=    i.parent
        # Updating self.bottom_root
									while i.left:
															lowercase__								=    i.left
									lowercase__								=    i

									# Update other
									lowercase__								=    self.size
									lowercase__								=    self.bottom_root
									lowercase__								=    self.min_node

									# Return the merged heap
									return self



			def A__	(   self    ,       lowerCamelCase__    )     ->						Optional[int]:




									'''simple docstring'''



									if self.size == 0:
															lowercase__								=    Node(lowerCamelCase__    )
															lowercase__								=    1
															lowercase__								=    self.bottom_root
									else:
															# Create new node
															lowercase__								=    Node(lowerCamelCase__    )

															# Update size
															self.size += 1

															# update min_node
															if val < self.min_node.val:
																					lowercase__								=    new_node
															# Put new_node as a bottom_root in heap
															lowercase__								=    new_node
															lowercase__								=    self.bottom_root
															lowercase__								=    new_node

															# Consecutively merge roots with same left_tree_size
															while (
															    self.bottom_root.parent
															    and self.bottom_root.left_tree_size
															    == self.bottom_root.parent.left_tree_size
															):
																					# Next node
																					lowercase__								=    self.bottom_root.parent.parent

																					# Merge
																					lowercase__								=    self.bottom_root.merge_trees(self.bottom_root.parent    )

																					# Update Links
																					lowercase__								=    next_node
																					lowercase__								=    None
																					if next_node:
																											lowercase__								=    self.bottom_root



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									return self.min_node.val



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									return self.size == 0



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



									lowercase__								=    self.min_node.val

									# Last element in heap corner case
									if self.size == 1:
															# Update size
															lowercase__								=    0

															# Update bottom root
															lowercase__								=    None

															# Update min_node
															lowercase__								=    None

															return min_value
									# No right subtree corner case
									# The structure of the tree implies that this should be the bottom root
									# and there is at least one other root
									if self.min_node.right is None:
															# Update size
															self.size -= 1

															# Update bottom root
															lowercase__								=    self.bottom_root.parent
															lowercase__								=    None

															# Update min_node
															lowercase__								=    self.bottom_root
															lowercase__								=    self.bottom_root.parent
															while i:
																					if i.val < self.min_node.val:
																											lowercase__								=    i
																					lowercase__								=    i.parent
															return min_value
									# General case
									# Find the BinomialHeap of the right subtree of min_node
									lowercase__								=    self.min_node.right
									lowercase__								=    None
									lowercase__								=    bottom_of_new
									lowercase__								=    1

									# Size, min_node and bottom_root
									while bottom_of_new.left:
															lowercase__								=    size_of_new * 2 + 1
															lowercase__								=    bottom_of_new.left
															if bottom_of_new.val < min_of_new.val:
																					lowercase__								=    bottom_of_new
        # Corner case of single root on top left path
									if (not self.min_node.left) and (not self.min_node.parent):
															lowercase__								=    size_of_new
															lowercase__								=    bottom_of_new
															lowercase__								=    min_of_new
															# print("Single root, multiple nodes case")
															return min_value
									# Remaining cases
									# Construct heap of right subtree
									lowercase__								=    BinomialHeap(
									    bottom_root=lowerCamelCase__    ,       min_node=lowerCamelCase__    ,       heap_size=lowerCamelCase__    )

									# Update size
									lowercase__								=    self.size - 1 - size_of_new

									# Neighbour nodes
									lowercase__								=    self.min_node.left
									lowercase__								=    self.min_node.parent

									# Initialize new bottom_root and min_node
									lowercase__								=    previous_node or next_node
									lowercase__								=    next_node

									# Update links of previous_node and search below for new min_node and
									# bottom_root
									if previous_node:
															lowercase__								=    next_node

															# Update bottom_root and search for min_node below
															lowercase__								=    previous_node
															lowercase__								=    previous_node
															while self.bottom_root.left:
																					lowercase__								=    self.bottom_root.left
																					if self.bottom_root.val < self.min_node.val:
																											lowercase__								=    self.bottom_root
									if next_node:
															lowercase__								=    previous_node

															# Search for new min_node above min_node
															lowercase__								=    next_node
															while i:
																					if i.val < self.min_node.val:
																											lowercase__								=    i
																					lowercase__								=    i.parent
        # Merge heaps
									self.merge_heaps(lowerCamelCase__    )

									return min_value



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.bottom_root
									while top_root.parent:
															lowercase__								=    top_root.parent
									# preorder
									lowercase__								=    []
									self.__traversal(lowerCamelCase__    ,       lowerCamelCase__    )
									return heap_pre_order



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__=0    )     ->						Dict:




									'''simple docstring'''



									if curr_node:
															preorder.append((curr_node.val, level)    )
															self.__traversal(curr_node.left    ,       lowerCamelCase__    ,       level + 1    )
															self.__traversal(curr_node.right    ,       lowerCamelCase__    ,       level + 1    )
									else:
															preorder.append(("""#""", level)    )



			def __str__(   self    )     ->						Dict:




									'''simple docstring'''



									if self.is_empty():
															return ""
									lowercase__								=    self.pre_order()

									return "\n".join(("""-""" * level + str(lowerCamelCase__    )) for value, level in preorder_heap    )


# Unit Tests
if __name__ == "__main__":
				import doctest

				doctest.testmod()








'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list





class      A							(    unittest.TestCase			):



			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									lowercase__								=    """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """    )
									lowercase__								=    dict(zip(lowerCamelCase__    ,       range(len(lowerCamelCase__    )    )    )    )

									lowercase__								=    {
									    """pad_token""": """<pad>""",
									    """unk_token""": """<unk>""",
									    """bos_token""": """<s>""",
									    """eos_token""": """</s>""",
									}
									lowercase__								=    {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16_000,
									    """return_attention_mask""": False,
									    """do_normalize""": True,
									}

									lowercase__								=    tempfile.mkdtemp()
									lowercase__								=    os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]    )
									lowercase__								=    os.path.join(self.tmpdirname    ,       lowerCamelCase__    )
									with open(self.vocab_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )

									with open(self.feature_extraction_file    ,       """w"""    ,       encoding="""utf-8"""    ) as fp:
															fp.write(json.dumps(lowerCamelCase__    ) + """\n"""    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						int:




									'''simple docstring'''



									lowercase__								=    self.add_kwargs_tokens_map.copy()
									kwargs.update(lowerCamelCase__    )
									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    ,       **lowerCamelCase__    )     ->						Dict:




									'''simple docstring'''



									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,       **lowerCamelCase__    )



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									shutil.rmtree(self.tmpdirname    )



			def A__	(   self    )     ->						str:




									'''simple docstring'''



									lowercase__								=    self.get_tokenizer()
									lowercase__								=    self.get_feature_extractor()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									processor.save_pretrained(self.tmpdirname    )
									lowercase__								=    WavaVecaProcessor.from_pretrained(self.tmpdirname    )

									self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer.get_vocab()    )
									self.assertIsInstance(processor.tokenizer    ,       lowerCamelCase__    )

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor()    )
									processor.save_pretrained(self.tmpdirname    )

									lowercase__								=    self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    )
									lowercase__								=    self.get_feature_extractor(do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									lowercase__								=    WavaVecaProcessor.from_pretrained(
									    self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=lowerCamelCase__    ,       padding_value=1.0    )

									self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab()    )
									self.assertIsInstance(processor.tokenizer    ,       lowerCamelCase__    )

									self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string()    )
									self.assertIsInstance(processor.feature_extractor    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    floats_list((3, 1_000)    )

									lowercase__								=    feature_extractor(lowerCamelCase__    ,       return_tensors="""np"""    )
									lowercase__								=    processor(lowerCamelCase__    ,       return_tensors="""np"""    )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1e-2    )



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    """This is a test string"""

									lowercase__								=    processor(text=lowerCamelCase__    )

									lowercase__								=    tokenizer(lowerCamelCase__    )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key]    )



			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									lowercase__								=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowercase__								=    processor.batch_decode(lowerCamelCase__    )
									lowercase__								=    tokenizer.batch_decode(lowerCamelCase__    )

									self.assertListEqual(lowerCamelCase__    ,       lowerCamelCase__    )



			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    self.get_feature_extractor()
									lowercase__								=    self.get_tokenizer()

									lowercase__								=    WavaVecaProcessor(tokenizer=lowerCamelCase__    ,       feature_extractor=lowerCamelCase__    )

									self.assertListEqual(
									    processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg="""`processor` and `feature_extractor` model input names do not match"""    ,       )






