

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
				from huggingface_hub import snapshot_download
				from pyctcdecode import BeamSearchDecoderCTC

				from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
				from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
				from transformers import WavaVecaForCTC

@require_pyctcdecode
class 						__UpperCamelCase    (					unittest.TestCase						):




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
		UpperCAmelCase_       				=			dict(zip(__snake_case		,	range(len(__snake_case							)							)							)							)

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

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

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

		# load decoder from hub
		UpperCAmelCase_       				=			'''hf-internal-testing/ngram-beam-search-decoder'''




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


		'''simple docstring'''







		UpperCAmelCase_       				=			self.add_kwargs_tokens_map.copy()
		kwargs.update(__snake_case							)
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,	**__snake_case							)




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


		'''simple docstring'''







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




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


		'''simple docstring'''







		return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,	**__snake_case							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		shutil.rmtree(self.tmpdirname							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		processor.save_pretrained(self.tmpdirname							)
		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname							)

		# tokenizer
		self.assertEqual(processor.tokenizer.get_vocab()		,	tokenizer.get_vocab()							)
		self.assertIsInstance(processor.tokenizer		,	__snake_case							)

		# feature extractor
		self.assertEqual(processor.feature_extractor.to_json_string()		,	feature_extractor.to_json_string()							)
		self.assertIsInstance(processor.feature_extractor		,	__snake_case							)

		# decoder
		self.assertEqual(processor.decoder._alphabet.labels		,	decoder._alphabet.labels							)
		self.assertEqual(
		    processor.decoder.model_container[decoder._model_key]._unigram_set		,	decoder.model_container[decoder._model_key]._unigram_set		,	)
		self.assertIsInstance(processor.decoder		,	__snake_case							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(
		    tokenizer=self.get_tokenizer()		,	feature_extractor=self.get_feature_extractor()		,	decoder=self.get_decoder()							)
		processor.save_pretrained(self.tmpdirname							)

		# make sure that error is thrown when decoder alphabet doesn't match
		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained(
		    self.tmpdirname		,	alpha=5.0		,	beta=3.0		,	score_boundary=-7.0		,	unk_score_offset=3							)

		# decoder
		self.assertEqual(processor.language_model.alpha		,	5.0							)
		self.assertEqual(processor.language_model.beta		,	3.0							)
		self.assertEqual(processor.language_model.score_boundary		,	-7.0							)
		self.assertEqual(processor.language_model.unk_score_offset		,	3							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.get_tokenizer()
		# add token to trigger raise
		tokenizer.add_tokens(["xx"]							)
		with self.assertRaisesRegex(__snake_case		,	"include"							):
			WavaVecaProcessorWithLM(
			    tokenizer=__snake_case		,	feature_extractor=self.get_feature_extractor()		,	decoder=self.get_decoder()							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		UpperCAmelCase_       				=			floats_list((3, 1_000)							)

		UpperCAmelCase_       				=			feature_extractor(__snake_case		,	return_tensors="np"							)
		UpperCAmelCase_       				=			processor(__snake_case		,	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	:						Any							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		UpperCAmelCase_       				=			'''This is a test string'''

		UpperCAmelCase_       				=			processor(text=__snake_case							)

		UpperCAmelCase_       				=			tokenizer(__snake_case							)

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




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						List[str]=(2, 10, 16)		,	lowerCAmelCase	:						Tuple=77							):


		'''simple docstring'''







		np.random.seed(__snake_case							)
		return np.random.rand(*__snake_case							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		UpperCAmelCase_       				=			self._get_dummy_logits(shape=(10, 16)		,	seed=13							)

		UpperCAmelCase_       				=			processor.decode(__snake_case							)

		UpperCAmelCase_       				=			decoder.decode_beams(__snake_case							)[0]

		self.assertEqual(decoded_decoder[0]		,	decoded_processor.text							)
		self.assertEqual("</s> <s> </s>"		,	decoded_processor.text							)
		self.assertEqual(decoded_decoder[-2]		,	decoded_processor.logit_score							)
		self.assertEqual(decoded_decoder[-1]		,	decoded_processor.lm_score							)




	@parameterized.expand([[None], ["fork"], ["spawn"]]							)
	def       __A					(		self	:						Any		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		UpperCAmelCase_       				=			self._get_dummy_logits()

		# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
		#       otherwise, the LM won't be available to the pool's sub-processes.
		# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
		if pool_context is None:
			UpperCAmelCase_       				=			processor.batch_decode(__snake_case							)
		else:
			with get_context(__snake_case							).Pool() as pool:
				UpperCAmelCase_       				=			processor.batch_decode(__snake_case		,	__snake_case							)

		UpperCAmelCase_       				=			list(__snake_case							)

		with get_context("fork"							).Pool() as p:
			UpperCAmelCase_       				=			decoder.decode_beams_batch(__snake_case		,	__snake_case							)

		UpperCAmelCase_       				=			[], [], []
		for beams in decoded_beams:
			texts_decoder.append(beams[0][0]							)
			logit_scores_decoder.append(beams[0][-2]							)
			lm_scores_decoder.append(beams[0][-1]							)

		self.assertListEqual(__snake_case		,	decoded_processor.text							)
		self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]		,	decoded_processor.text							)
		self.assertListEqual(__snake_case		,	decoded_processor.logit_score							)
		self.assertListEqual(__snake_case		,	decoded_processor.lm_score							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		UpperCAmelCase_       				=			self._get_dummy_logits()

		UpperCAmelCase_       				=			15
		UpperCAmelCase_       				=			-20.0
		UpperCAmelCase_       				=			-4.0

		UpperCAmelCase_       				=			processor.batch_decode(
		    __snake_case		,	beam_width=__snake_case		,	beam_prune_logp=__snake_case		,	token_min_logp=__snake_case		,	)
		UpperCAmelCase_       				=			decoded_processor_out.text

		UpperCAmelCase_       				=			list(__snake_case							)

		with get_context("fork"							).Pool() as pool:
			UpperCAmelCase_       				=			decoder.decode_beams_batch(
			    __snake_case		,	__snake_case		,	beam_width=__snake_case		,	beam_prune_logp=__snake_case		,	token_min_logp=__snake_case		,	)

		UpperCAmelCase_       				=			[d[0][0] for d in decoded_decoder_out]
		UpperCAmelCase_       				=			[d[0][2] for d in decoded_decoder_out]
		UpperCAmelCase_       				=			[d[0][3] for d in decoded_decoder_out]

		self.assertListEqual(__snake_case		,	__snake_case							)
		self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]		,	__snake_case							)

		self.assertTrue(np.array_equal(__snake_case		,	decoded_processor_out.logit_score							)							)
		self.assertTrue(np.allclose([-20.054, -18.447]		,	__snake_case		,	atol=1e-3							)							)

		self.assertTrue(np.array_equal(__snake_case		,	decoded_processor_out.lm_score							)							)
		self.assertTrue(np.allclose([-15.554, -13.9_474]		,	__snake_case		,	atol=1e-3							)							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

		UpperCAmelCase_       				=			self._get_dummy_logits()

		UpperCAmelCase_       				=			2.0
		UpperCAmelCase_       				=			5.0
		UpperCAmelCase_       				=			-20.0
		UpperCAmelCase_       				=			True

		UpperCAmelCase_       				=			processor.batch_decode(
		    __snake_case		,	alpha=__snake_case		,	beta=__snake_case		,	unk_score_offset=__snake_case		,	lm_score_boundary=__snake_case		,	)
		UpperCAmelCase_       				=			decoded_processor_out.text

		UpperCAmelCase_       				=			list(__snake_case							)
		decoder.reset_params(
		    alpha=__snake_case		,	beta=__snake_case		,	unk_score_offset=__snake_case		,	lm_score_boundary=__snake_case		,	)

		with get_context("fork"							).Pool() as pool:
			UpperCAmelCase_       				=			decoder.decode_beams_batch(
			    __snake_case		,	__snake_case		,	)

		UpperCAmelCase_       				=			[d[0][0] for d in decoded_decoder_out]

		self.assertListEqual(__snake_case		,	__snake_case							)
		self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]		,	__snake_case							)
		UpperCAmelCase_       				=			processor.decoder.model_container[processor.decoder._model_key]
		self.assertEqual(lm_model.alpha		,	2.0							)
		self.assertEqual(lm_model.beta		,	5.0							)
		self.assertEqual(lm_model.unk_score_offset		,	-20.0							)
		self.assertEqual(lm_model.score_boundary		,	__snake_case							)




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)

		UpperCAmelCase_       				=			processor.decoder.model_container[processor.decoder._model_key]
		UpperCAmelCase_       				=			Path(language_model._kenlm_model.path.decode("utf-8"							)							).parent.parent.absolute()

		UpperCAmelCase_       				=			os.listdir(__snake_case							)
		UpperCAmelCase_       				=			['''alphabet.json''', '''language_model''']

		downloaded_decoder_files.sort()
		expected_decoder_files.sort()

		# test that only decoder relevant files from
		# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
		# are downloaded and none of the rest (e.g. README.md, ...)
		self.assertListEqual(__snake_case		,	__snake_case							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			snapshot_download("hf-internal-testing/processor_with_lm"							)

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained(__snake_case							)

		UpperCAmelCase_       				=			processor.decoder.model_container[processor.decoder._model_key]
		UpperCAmelCase_       				=			Path(language_model._kenlm_model.path.decode("utf-8"							)							).parent.parent.absolute()

		UpperCAmelCase_       				=			os.listdir(__snake_case							)
		UpperCAmelCase_       				=			os.listdir(__snake_case							)

		local_decoder_files.sort()
		expected_decoder_files.sort()

		# test that both decoder form hub and local files in cache are the same
		self.assertListEqual(__snake_case		,	__snake_case							)




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


		'''simple docstring'''







		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)
		UpperCAmelCase_       				=			AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"							)

		UpperCAmelCase_       				=			floats_list((3, 1_000)							)

		UpperCAmelCase_       				=			processor_wavaveca(__snake_case		,	return_tensors="np"							)
		UpperCAmelCase_       				=			processor_auto(__snake_case		,	return_tensors="np"							)

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

		UpperCAmelCase_       				=			self._get_dummy_logits()

		UpperCAmelCase_       				=			processor_wavaveca.batch_decode(__snake_case							)
		UpperCAmelCase_       				=			processor_auto.batch_decode(__snake_case							)

		self.assertListEqual(decoded_wavaveca.text		,	decoded_auto.text							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







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

		UpperCAmelCase_       				=			WavaVecaProcessorWithLM(tokenizer=__snake_case		,	feature_extractor=__snake_case		,	decoder=__snake_case							)

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




	@staticmethod
	def       __A					(		lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[d[key] for d in offsets]
		return retrieved_list




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)
		UpperCAmelCase_       				=			self._get_dummy_logits()[0]

		UpperCAmelCase_       				=			processor.decode(__snake_case		,	output_word_offsets=__snake_case							)
		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()							)		,	4							)
		self.assertTrue("text" in outputs							)
		self.assertTrue("word_offsets" in outputs							)
		self.assertTrue(isinstance(__snake_case		,	__snake_case							)							)

		self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]		,	"word"							)							)		,	outputs.text							)
		self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]		,	"word"							)		,	["<s>", "<s>", "</s>"]							)
		self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]		,	"start_offset"							)		,	[0, 2, 4]							)
		self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]		,	"end_offset"							)		,	[1, 3, 5]							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)
		UpperCAmelCase_       				=			self._get_dummy_logits()

		UpperCAmelCase_       				=			processor.batch_decode(__snake_case		,	output_word_offsets=__snake_case							)

		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()							)		,	4							)
		self.assertTrue("text" in outputs							)
		self.assertTrue("word_offsets" in outputs							)
		self.assertTrue(isinstance(__snake_case		,	__snake_case							)							)

		self.assertListEqual(
		    [" ".join(self.get_from_offsets(__snake_case		,	"word"							)							) for o in outputs["word_offsets"]]		,	outputs.text							)
		self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]		,	"word"							)		,	["<s>", "<s>", "</s>"]							)
		self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]		,	"start_offset"							)		,	[0, 2, 4]							)
		self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]		,	"end_offset"							)		,	[1, 3, 5]							)




	@slow
	@require_torch
	@require_torchaudio
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		import torch

		UpperCAmelCase_       				=			load_dataset("common_voice"		,	"en"		,	split="train"		,	streaming=__snake_case							)
		UpperCAmelCase_       				=			ds.cast_column("audio"		,	datasets.Audio(sampling_rate=16_000							)							)
		UpperCAmelCase_       				=			iter(__snake_case							)
		UpperCAmelCase_       				=			next(__snake_case							)

		UpperCAmelCase_       				=			AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"							)
		UpperCAmelCase_       				=			WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"							)

		# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
		UpperCAmelCase_       				=			processor(sample["audio"]["array"]		,	return_tensors="pt"							).input_values

		with torch.no_grad():
			UpperCAmelCase_       				=			model(__snake_case							).logits.cpu().numpy()

		UpperCAmelCase_       				=			processor.decode(logits[0]		,	output_word_offsets=__snake_case							)

		UpperCAmelCase_       				=			model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
		UpperCAmelCase_       				=			[
		    {
		        '''start_time''': d['''start_offset'''] * time_offset,
		        '''end_time''': d['''end_offset'''] * time_offset,
		        '''word''': d['''word'''],
		    }
		    for d in output['''word_offsets''']
		]

		UpperCAmelCase_       				=			'''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

		# output words
		self.assertEqual(" ".join(self.get_from_offsets(__snake_case		,	"word"							)							)		,	__snake_case							)
		self.assertEqual(" ".join(self.get_from_offsets(__snake_case		,	"word"							)							)		,	output.text							)

		# output times
		UpperCAmelCase_       				=			torch.tensor(self.get_from_offsets(__snake_case		,	"start_time"							)							)
		UpperCAmelCase_       				=			torch.tensor(self.get_from_offsets(__snake_case		,	"end_time"							)							)

		# fmt: off
		UpperCAmelCase_       				=			torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]							)
		UpperCAmelCase_       				=			torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]							)
		# fmt: on

		self.assertTrue(torch.allclose(__snake_case		,	__snake_case		,	atol=0.01							)							)
		self.assertTrue(torch.allclose(__snake_case		,	__snake_case		,	atol=0.01							)							)


from __future__ import annotations

from collections import Counter
from random import random

class 						__UpperCamelCase    :




	def __init__(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			{}




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


		'''simple docstring'''







		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		if nodea not in self.connections:
			self.add_node(lowerCAmelCase							)
		UpperCAmelCase_       				=			probability




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		return list(self.connections							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			random()

		for dest in self.connections[node]:
			current_probability += self.connections[node][dest]
			if current_probability > random_value:
				return dest
		return ""



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(A       ,     A       ,     A    )

	UpperCAmelCase_       				=			Counter(graph.get_nodes()    )
	UpperCAmelCase_       				=			start

	for _ in range(A    ):
		UpperCAmelCase_       				=			graph.transition(A    )
		visited[node] += 1

	return visited


if __name__ == "__main__":
				import doctest

				doctest.testmod()