
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		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[int]:
										lowercase         =      "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
										lowercase         =      dict(zip(A_		,    range(len(A_     )     )     )     )

										lowercase         =      {
										    "unk_token": "<unk>",
										    "bos_token": "<s>",
										    "eos_token": "</s>",
										}
										lowercase         =      {
										    "feature_size": 1,
										    "padding_value": 0.0,
										    "sampling_rate": 16000,
										    "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		,    A_     )
										with open(self.vocab_file		,    "w"		,    encoding="utf-8"     ) as fp:
														fp.write(json.dumps(A_     ) + "\n"     )

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

										# load decoder from hub
										lowercase         =      "hf-internal-testing/ngram-beam-search-decoder"





						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    **lowerCAmelCase__							:List[str]     )  ->str:
										lowercase         =      self.add_kwargs_tokens_map.copy()
										kwargs.update(A_     )
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,    **A_     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]		,    **lowerCAmelCase__							:int     )  ->List[Any]:
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,    **A_     )





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    **lowerCAmelCase__							:Union[str, Any]     )  ->List[Any]:
										return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,    **A_     )





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->int:
										shutil.rmtree(self.tmpdirname     )





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->str:
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

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

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

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

										# 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		,    A_     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Union[str, Any]:
										lowercase         =      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
										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Tuple:
										lowercase         =      self.get_tokenizer()
										# add token to trigger raise
										tokenizer.add_tokens(["xx"]     )
										with self.assertRaisesRegex(A_		,    "include"     ):
														WavaVecaProcessorWithLM(
														    tokenizer=A_		,    feature_extractor=self.get_feature_extractor()		,    decoder=self.get_decoder()     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->int:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

										lowercase         =      floats_list((3, 1000)     )

										lowercase         =      feature_extractor(A_		,    return_tensors="np"     )
										lowercase         =      processor(A_		,    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        SCREAMING_SNAKE_CASE(			self							:int     )  ->Dict:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

										lowercase         =      "This is a test string"

										lowercase         =      processor(text=A_     )

										lowercase         =      tokenizer(A_     )

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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:List[Any]=(2, 10, 16)		,    lowerCAmelCase__							:Union[str, Any]=77     )  ->Dict:
										np.random.seed(A_     )
										return np.random.rand(*A_     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Optional[int]:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

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

										lowercase         =      processor.decode(A_     )

										lowercase         =      decoder.decode_beams(A_     )[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        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->List[Any]:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

										lowercase         =      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:
														lowercase         =      processor.batch_decode(A_     )
										else:
														with get_context(A_     ).Pool() as pool:
																		lowercase         =      processor.batch_decode(A_		,    A_     )

										lowercase         =      list(A_     )

										with get_context("fork"     ).Pool() as p:
														lowercase         =      decoder.decode_beams_batch(A_		,    A_     )

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





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->List[str]:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

										lowercase         =      self._get_dummy_logits()

										lowercase         =      15
										lowercase         =      -20.0
										lowercase         =      -4.0

										lowercase         =      processor.batch_decode(
										    A_		,    beam_width=A_		,    beam_prune_logp=A_		,    token_min_logp=A_		,    )
										lowercase         =      decoded_processor_out.text

										lowercase         =      list(A_     )

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

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

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

										self.assertTrue(np.array_equal(A_		,    decoded_processor_out.logit_score     )     )
										self.assertTrue(np.allclose([-20.0_54, -18.4_47]		,    A_		,    atol=1E-3     )     )

										self.assertTrue(np.array_equal(A_		,    decoded_processor_out.lm_score     )     )
										self.assertTrue(np.allclose([-15.5_54, -13.94_74]		,    A_		,    atol=1E-3     )     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Optional[int]:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

										lowercase         =      self._get_dummy_logits()

										lowercase         =      2.0
										lowercase         =      5.0
										lowercase         =      -20.0
										lowercase         =      True

										lowercase         =      processor.batch_decode(
										    A_		,    alpha=A_		,    beta=A_		,    unk_score_offset=A_		,    lm_score_boundary=A_		,    )
										lowercase         =      decoded_processor_out.text

										lowercase         =      list(A_     )
										decoder.reset_params(
										    alpha=A_		,    beta=A_		,    unk_score_offset=A_		,    lm_score_boundary=A_		,    )

										with get_context("fork"     ).Pool() as pool:
														lowercase         =      decoder.decode_beams_batch(
														    A_		,    A_		,    )

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

										self.assertListEqual(A_		,    A_     )
										self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]		,    A_     )
										lowercase         =      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		,    A_     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Union[str, Any]:
										lowercase         =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"     )

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

										lowercase         =      os.listdir(A_     )
										lowercase         =      ["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(A_		,    A_     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Tuple:
										lowercase         =      snapshot_download("hf-internal-testing/processor_with_lm"     )

										lowercase         =      WavaVecaProcessorWithLM.from_pretrained(A_     )

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

										lowercase         =      os.listdir(A_     )
										lowercase         =      os.listdir(A_     )

										local_decoder_files.sort()
										expected_decoder_files.sort()

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





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]     )  ->Any:
										lowercase         =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"     )
										lowercase         =      AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"     )

										lowercase         =      floats_list((3, 1000)     )

										lowercase         =      processor_wavaveca(A_		,    return_tensors="np"     )
										lowercase         =      processor_auto(A_		,    return_tensors="np"     )

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

										lowercase         =      self._get_dummy_logits()

										lowercase         =      processor_wavaveca.batch_decode(A_     )
										lowercase         =      processor_auto.batch_decode(A_     )

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





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[Any]:
										lowercase         =      self.get_feature_extractor()
										lowercase         =      self.get_tokenizer()
										lowercase         =      self.get_decoder()

										lowercase         =      WavaVecaProcessorWithLM(tokenizer=A_		,    feature_extractor=A_		,    decoder=A_     )

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





						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:List[Any]     )  ->Optional[Any]:
										lowercase         =      [d[key] for d in offsets]
										return retrieved_list





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Union[str, Any]:
										lowercase         =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"     )
										lowercase         =      self._get_dummy_logits()[0]

										lowercase         =      processor.decode(A_		,    output_word_offsets=A_     )
										# 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(A_		,    A_     )     )

										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        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->str:
										lowercase         =      WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"     )
										lowercase         =      self._get_dummy_logits()

										lowercase         =      processor.batch_decode(A_		,    output_word_offsets=A_     )

										# 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(A_		,    A_     )     )

										self.assertListEqual(
										    [" ".join(self.get_from_offsets(A_		,    "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        SCREAMING_SNAKE_CASE(			self							:Any     )  ->Union[str, Any]:
										import torch

										lowercase         =      load_dataset("common_voice"		,    "en"		,    split="train"		,    streaming=A_     )
										lowercase         =      ds.cast_column("audio"		,    datasets.Audio(sampling_rate=16000     )     )
										lowercase         =      iter(A_     )
										lowercase         =      next(A_     )

										lowercase         =      AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"     )
										lowercase         =      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
										lowercase         =      processor(sample["audio"]["array"]		,    return_tensors="pt"     ).input_values

										with torch.no_grad():
														lowercase         =      model(A_     ).logits.cpu().numpy()

										lowercase         =      processor.decode(logits[0]		,    output_word_offsets=A_     )

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

										lowercase         =      "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(A_		,    "word"     )     )		,    A_     )
										self.assertEqual(" ".join(self.get_from_offsets(A_		,    "word"     )     )		,    output.text     )

										# output times
										lowercase         =      torch.tensor(self.get_from_offsets(A_		,    "start_time"     )     )
										lowercase         =      torch.tensor(self.get_from_offsets(A_		,    "end_time"     )     )

										# fmt: off
										lowercase         =      torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]     )
										lowercase         =      torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]     )
										# fmt: on

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




"""simple docstring"""

class  lowerCamelCase__    :
      def __init__(		self						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str   ,							A_						:						Any				):






       '''simple docstring'''


       __lowercase						=						None
       __lowercase						=						None
       __lowercase						=						graph

       self._normalize_graph(A_   ,							A_				)
       __lowercase						=						len(A_				)
       __lowercase						=						None
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str   ,							A_						:						List[Any]				):






       '''simple docstring'''


       if sources is int:
        __lowercase						=						[sources]
       if sinks is int:
        __lowercase						=						[sinks]

       if len(A_				) == 0 or len(A_				) == 0:
        return

       __lowercase						=						sources[0]
       __lowercase						=						sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(A_				) > 1 or len(A_				) > 1:
        __lowercase						=						0
        for i in sources:
         max_input_flow += sum(self.graph[i]				)

        __lowercase						=						len(self.graph				) + 1
        for room in self.graph:
         room.insert(0   ,							0				)
        self.graph.insert(0   ,							[0] * size				)
        for i in sources:
         __lowercase						=						max_input_flow
        __lowercase						=						0

        __lowercase						=						len(self.graph				) + 1
        for room in self.graph:
         room.append(0				)
        self.graph.append([0] * size				)
        for i in sinks:
         __lowercase						=						max_input_flow
        __lowercase						=						size - 1
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       if self.maximum_flow_algorithm is None:
        raise Exception("""You need to set maximum flow algorithm before."""				)
       if self.source_index is None or self.sink_index is None:
        return 0

       self.maximum_flow_algorithm.execute()
       return self.maximum_flow_algorithm.getMaximumFlow()






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						algorithm(self				)






class  lowerCamelCase__    :
      def __init__(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						flow_network
       __lowercase						=						flow_network.verticesCount
       __lowercase						=						flow_network.sourceIndex
       __lowercase						=						flow_network.sinkIndex
       # it's just a reference, so you shouldn't change
       # it in your algorithms, use deep copy before doing that
       __lowercase						=						flow_network.graph
       __lowercase						=						False
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       if not self.executed:
        self._algorithm()
        __lowercase						=						True






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






       '''simple docstring'''


       pass






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Union[str, Any]   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       # use this to save your result
       __lowercase						=						-1






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       if not self.executed:
        raise Exception("""You should execute algorithm before using its result!"""				)

       return self.maximum_flow






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						List[str]   ,							A_						:						Tuple				):






       '''simple docstring'''


       super().__init__(A_				)

       __lowercase						=						[[0] * self.verticies_count for i in range(self.verticies_count				)]

       __lowercase						=						[0] * self.verticies_count
       __lowercase						=						[0] * self.verticies_count
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.verticies_count

       # push some substance to graph
       for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]				):
        self.preflow[self.source_index][nextvertex_index] += bandwidth
        self.preflow[nextvertex_index][self.source_index] -= bandwidth
        self.excesses[nextvertex_index] += bandwidth

       # Relabel-to-front selection rule
       __lowercase						=						[
           i
           for i in range(self.verticies_count				)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       __lowercase						=						0
       while i < len(A_				):
        __lowercase						=						vertices_list[i]
        __lowercase						=						self.heights[vertex_index]
        self.process_vertex(A_				)
        if self.heights[vertex_index] > previous_height:
         # if it was relabeled, swap elements
         # and start from 0 index
         vertices_list.insert(0   ,							vertices_list.pop(A_				)				)
         __lowercase						=						0
        else:
         i += 1

       __lowercase						=						sum(self.preflow[self.source_index]				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       while self.excesses[vertex_index] > 0:
        for neighbour_index in range(self.verticies_count				):
         # if it's neighbour and current vertex is higher
         if (
             self.graph[vertex_index][neighbour_index]
             - self.preflow[vertex_index][neighbour_index]
             > 0
             and self.heights[vertex_index] > self.heights[neighbour_index]
         ):
          self.push(A_   ,							A_				)

        self.relabel(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Union[str, Any]   ,							A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						min(
           self.excesses[from_index]   ,							self.graph[from_index][to_index] - self.preflow[from_index][to_index]   ,							)
       self.preflow[from_index][to_index] += preflow_delta
       self.preflow[to_index][from_index] -= preflow_delta
       self.excesses[from_index] -= preflow_delta
       self.excesses[to_index] += preflow_delta






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						None
       for to_index in range(self.verticies_count				):
        if (
            self.graph[vertex_index][to_index]
            - self.preflow[vertex_index][to_index]
            > 0
        ) and (min_height is None or self.heights[to_index] < min_height):
         __lowercase						=						self.heights[to_index]

       if min_height is not None:
        __lowercase						=						min_height + 1


if __name__ == "__main__":
       UpperCAmelCase__					=[0]
       UpperCAmelCase__					=[3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       UpperCAmelCase__					=[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       UpperCAmelCase__					=FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       UpperCAmelCase__					=flow_network.find_maximum_flow()

       print(f"""maximum flow is {maximum_flow}""")
