

import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
				from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

				from .test_feature_extraction_speechta import floats_list


_a:      Tuple								=     get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")

@require_torch
class   __UpperCamelCase      (   unittest.TestCase  ):




	def  __A   (						self		:  str							):


		'''simple docstring'''







		UpperCAmelCase_    			=     tempfile.mkdtemp()

		UpperCAmelCase_    			=     SpeechTaTokenizer(lowerCAmelCase							)
		tokenizer.save_pretrained(self.tmpdirname							)

		UpperCAmelCase_    			=     {
		    "feature_size": 1,
		    "padding_value": 0.0,
		    "sampling_rate": 16_000,
		    "do_normalize": False,
		    "num_mel_bins": 80,
		    "hop_length": 16,
		    "win_length": 64,
		    "win_function": "hann_window",
		    "fmin": 80,
		    "fmax": 7_600,
		    "mel_floor": 1e-10,
		    "reduction_factor": 2,
		    "return_attention_mask": True,
		}

		UpperCAmelCase_    			=     os.path.join(self.tmpdirname						,		lowerCAmelCase							)
		with open(self.feature_extraction_file						,		"w"						,		encoding="utf-8"							) as fp:
			fp.write(json.dumps(lowerCAmelCase							) + "\n"							)




	def  __A   (						self		:  Union[str, Any]						,		**lowerCAmelCase		:  Any							):


		'''simple docstring'''







		return SpeechTaTokenizer.from_pretrained(self.tmpdirname						,		**lowerCAmelCase							)




	def  __A   (						self		:  Optional[Any]						,		**lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname						,		**lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		shutil.rmtree(self.tmpdirname							)




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.get_tokenizer()
		UpperCAmelCase_    			=     self.get_feature_extractor()

		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=lowerCAmelCase						,		feature_extractor=lowerCAmelCase							)

		processor.save_pretrained(self.tmpdirname							)
		UpperCAmelCase_    			=     SpeechTaProcessor.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		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=self.get_tokenizer()						,		feature_extractor=self.get_feature_extractor()							)
		processor.save_pretrained(self.tmpdirname							)

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

		UpperCAmelCase_    			=     SpeechTaProcessor.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'''







		UpperCAmelCase_    			=     self.get_feature_extractor()
		UpperCAmelCase_    			=     self.get_tokenizer()

		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=lowerCAmelCase						,		feature_extractor=lowerCAmelCase							)

		UpperCAmelCase_    			=     floats_list((3, 1_000)							)

		UpperCAmelCase_    			=     feature_extractor(audio=lowerCAmelCase						,		return_tensors="np"							)
		UpperCAmelCase_    			=     processor(audio=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'''







		UpperCAmelCase_    			=     self.get_feature_extractor()
		UpperCAmelCase_    			=     self.get_tokenizer()

		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=lowerCAmelCase						,		feature_extractor=lowerCAmelCase							)

		UpperCAmelCase_    			=     floats_list((3, 1_000)							)

		UpperCAmelCase_    			=     feature_extractor(audio_target=lowerCAmelCase						,		return_tensors="np"							)
		UpperCAmelCase_    			=     processor(audio_target=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'''







		UpperCAmelCase_    			=     self.get_feature_extractor()
		UpperCAmelCase_    			=     self.get_tokenizer()

		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=lowerCAmelCase						,		feature_extractor=lowerCAmelCase							)

		UpperCAmelCase_    			=     "This is a test string"

		UpperCAmelCase_    			=     processor(text=lowerCAmelCase							)
		UpperCAmelCase_    			=     tokenizer(lowerCAmelCase							)

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




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.get_feature_extractor()
		UpperCAmelCase_    			=     self.get_tokenizer()

		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=lowerCAmelCase						,		feature_extractor=lowerCAmelCase							)

		UpperCAmelCase_    			=     "This is a test string"

		UpperCAmelCase_    			=     processor(text_target=lowerCAmelCase							)
		UpperCAmelCase_    			=     tokenizer(lowerCAmelCase							)

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




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.get_feature_extractor()
		UpperCAmelCase_    			=     self.get_tokenizer()

		UpperCAmelCase_    			=     SpeechTaProcessor(tokenizer=lowerCAmelCase						,		feature_extractor=lowerCAmelCase							)

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

		UpperCAmelCase_    			=     processor.batch_decode(lowerCAmelCase							)
		UpperCAmelCase_    			=     tokenizer.batch_decode(lowerCAmelCase							)

		self.assertListEqual(lowerCAmelCase						,		lowerCAmelCase							)




	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_    			=     self.get_feature_extractor()
		UpperCAmelCase_    			=     self.get_tokenizer()

		UpperCAmelCase_    			=     SpeechTaProcessor(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"						,		)


# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint

class   __UpperCamelCase      :




	def __init__(						self		:  Optional[Any]						,		lowerCAmelCase		:  Any						,		lowerCAmelCase		:  Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     key
		UpperCAmelCase_    			=     freq




	def __str__(						self		:  List[str]							):


		'''simple docstring'''







		return F"Node(key={self.key}, freq={self.freq})"



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A    ,       A    ,       A      ):
	if i > j or i < 0 or j > len(A      ) - 1:
		return

	UpperCAmelCase_    			=     root[i][j]
	if parent == -1:  # root does not have a parent
		print(F"{key[node]} is the root of the binary search tree."      )
	elif is_left:
		print(F"{key[node]} is the left child of key {parent}."      )
	else:
		print(F"{key[node]} is the right child of key {parent}."      )

	print_binary_search_tree(A    ,       A    ,       A    ,       node - 1    ,       key[node]    ,       A      )
	print_binary_search_tree(A    ,       A    ,       node + 1    ,       A    ,       key[node]    ,       A      )



def  __lowerCAmelCase (  A      ):
	nodes.sort(key=lambda A      : node.key      )

	UpperCAmelCase_    			=     len(A      )

	UpperCAmelCase_    			=     [nodes[i].key for i in range(A      )]
	UpperCAmelCase_    			=     [nodes[i].freq for i in range(A      )]

	# This 2D array stores the overall tree cost (which's as minimized as possible);
	# for a single key, cost is equal to frequency of the key.
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
	# array
	UpperCAmelCase_    			=     [[freqs[i] if i == j else 0 for j in range(A      )] for i in range(A      )]
	# stores tree roots that will be used later for constructing binary search tree
	UpperCAmelCase_    			=     [[i if i == j else 0 for j in range(A      )] for i in range(A      )]

	for interval_length in range(2    ,       n + 1      ):
		for i in range(n - interval_length + 1      ):
			UpperCAmelCase_    			=     i + interval_length - 1

			UpperCAmelCase_    			=     sys.maxsize  # set the value to "infinity"
			UpperCAmelCase_    			=     total[i][j - 1] + freqs[j]

			# Apply Knuth's optimization
			# Loop without optimization: for r in range(i, j + 1):
			for r in range(root[i][j - 1]    ,       root[i + 1][j] + 1      ):  # r is a temporal root
				UpperCAmelCase_    			=     dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
				UpperCAmelCase_    			=     dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
				UpperCAmelCase_    			=     left + total[i][j] + right

				if dp[i][j] > cost:
					UpperCAmelCase_    			=     cost
					UpperCAmelCase_    			=     r

	print("Binary search tree nodes:"      )
	for node in nodes:
		print(A      )

	print(F"\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."      )
	print_binary_search_tree(A    ,       A    ,       0    ,       n - 1    ,       -1    ,       A      )



def  __lowerCAmelCase (  ):
	# A sample binary search tree
	UpperCAmelCase_    			=     [Node(A    ,       randint(1    ,       50      )      ) for i in range(10    ,       0    ,       -1      )]
	find_optimal_binary_search_tree(A      )


if __name__ == "__main__":
				main()