






"""simple docstring"""


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       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	int			=						"""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
   _snake_case							:	Any			=						dict(zip(__A,   range(len(__A	)	)	)	)

   _snake_case							:	Optional[int]			=						{
       """unk_token""": """<unk>""",
       """bos_token""": """<s>""",
       """eos_token""": """</s>""",
   }
   _snake_case							:	Optional[int]			=						{
       """feature_size""": 1,
       """padding_value""": 0.0,
       """sampling_rate""": 16_000,
       """return_attention_mask""": False,
       """do_normalize""": True,
   }

   _snake_case							:	str			=						tempfile.mkdtemp()
   _snake_case							:	Optional[int]			=						os.path.join(self.tmpdirname,   VOCAB_FILES_NAMES["""vocab_file"""]	)
   _snake_case							:	List[str]			=						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
   _snake_case							:	Optional[int]			=						"""hf-internal-testing/ngram-beam-search-decoder"""


 def 				UpperCamelCase_     (       self:	Any,   **a_:	str	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.add_kwargs_tokens_map.copy()
   kwargs.update(__A	)
   return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,   **__A	)


 def 				UpperCamelCase_     (       self:	List[str],   **a_:	Optional[Any]	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Optional[Any],   **a_:	str	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   shutil.rmtree(self.tmpdirname	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.get_tokenizer()
   _snake_case							:	List[Any]			=						self.get_feature_extractor()
   _snake_case							:	int			=						self.get_decoder()

   _snake_case							:	Optional[int]			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   processor.save_pretrained(self.tmpdirname	)
   _snake_case							:	str			=						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 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						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
   _snake_case							:	List[str]			=						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 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						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 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.get_feature_extractor()
   _snake_case							:	List[str]			=						self.get_tokenizer()
   _snake_case							:	Union[str, Any]			=						self.get_decoder()

   _snake_case							:	Any			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   _snake_case							:	Union[str, Any]			=						floats_list((3, 1_000)	)

   _snake_case							:	str			=						feature_extractor(__A,   return_tensors="""np"""	)
   _snake_case							:	Optional[Any]			=						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 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	str			=						self.get_feature_extractor()
   _snake_case							:	List[Any]			=						self.get_tokenizer()
   _snake_case							:	Dict			=						self.get_decoder()

   _snake_case							:	Any			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   _snake_case							:	Union[str, Any]			=						"""This is a test string"""

   _snake_case							:	Any			=						processor(text=__A	)

   _snake_case							:	Tuple			=						tokenizer(__A	)

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


 def 				UpperCamelCase_     (       self:	List[Any],   a_:	int=(2, 10, 16),   a_:	List[Any]=77	):




   '''simple docstring'''



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


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						self.get_feature_extractor()
   _snake_case							:	Union[str, Any]			=						self.get_tokenizer()
   _snake_case							:	Union[str, Any]			=						self.get_decoder()

   _snake_case							:	List[Any]			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   _snake_case							:	str			=						self._get_dummy_logits(shape=(10, 16),   seed=13	)

   _snake_case							:	int			=						processor.decode(__A	)

   _snake_case							:	Tuple			=						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 				UpperCamelCase_     (       self:	List[Any],   a_:	Any	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.get_feature_extractor()
   _snake_case							:	int			=						self.get_tokenizer()
   _snake_case							:	Any			=						self.get_decoder()

   _snake_case							:	List[Any]			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   _snake_case							:	Optional[int]			=						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:
     _snake_case							:	Tuple			=						processor.batch_decode(__A	)
   else:
     with get_context(__A	).Pool() as pool:
       _snake_case							:	Optional[int]			=						processor.batch_decode(__A,   __A	)

   _snake_case							:	Optional[Any]			=						list(__A	)

   with get_context("""fork"""	).Pool() as p:
     _snake_case							:	Optional[Any]			=						decoder.decode_beams_batch(__A,   __A	)

   _snake_case	,      _snake_case	,      _snake_case							:	int			=						[], [], []
   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 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.get_feature_extractor()
   _snake_case							:	Union[str, Any]			=						self.get_tokenizer()
   _snake_case							:	Optional[int]			=						self.get_decoder()

   _snake_case							:	Tuple			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   _snake_case							:	str			=						self._get_dummy_logits()

   _snake_case							:	int			=						15
   _snake_case							:	List[str]			=						-20.0
   _snake_case							:	Union[str, Any]			=						-4.0

   _snake_case							:	str			=						processor.batch_decode(
       __A,   beam_width=__A,   beam_prune_logp=__A,   token_min_logp=__A,   )
   _snake_case							:	List[Any]			=						decoded_processor_out.text

   _snake_case							:	Union[str, Any]			=						list(__A	)

   with get_context("""fork"""	).Pool() as pool:
     _snake_case							:	Union[str, Any]			=						decoder.decode_beams_batch(
         __A,   __A,   beam_width=__A,   beam_prune_logp=__A,   token_min_logp=__A,   )

   _snake_case							:	Union[str, Any]			=						[d[0][0] for d in decoded_decoder_out]
   _snake_case							:	List[Any]			=						[d[0][2] for d in decoded_decoder_out]
   _snake_case							:	Dict			=						[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([-20.054, -18.447],   __A,   atol=1E-3	)	)

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


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.get_feature_extractor()
   _snake_case							:	Union[str, Any]			=						self.get_tokenizer()
   _snake_case							:	Optional[int]			=						self.get_decoder()

   _snake_case							:	str			=						WavaVecaProcessorWithLM(tokenizer=__A,   feature_extractor=__A,   decoder=__A	)

   _snake_case							:	int			=						self._get_dummy_logits()

   _snake_case							:	Optional[int]			=						2.0
   _snake_case							:	str			=						5.0
   _snake_case							:	List[str]			=						-20.0
   _snake_case							:	Optional[Any]			=						True

   _snake_case							:	Optional[int]			=						processor.batch_decode(
       __A,   alpha=__A,   beta=__A,   unk_score_offset=__A,   lm_score_boundary=__A,   )
   _snake_case							:	Union[str, Any]			=						decoded_processor_out.text

   _snake_case							:	List[str]			=						list(__A	)
   decoder.reset_params(
       alpha=__A,   beta=__A,   unk_score_offset=__A,   lm_score_boundary=__A,   )

   with get_context("""fork"""	).Pool() as pool:
     _snake_case							:	Optional[int]			=						decoder.decode_beams_batch(
         __A,   __A,   )

   _snake_case							:	List[str]			=						[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	)
   _snake_case							:	str			=						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 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""	)

   _snake_case							:	Optional[int]			=						processor.decoder.model_container[processor.decoder._model_key]
   _snake_case							:	Union[str, Any]			=						Path(language_model._kenlm_model.path.decode("""utf-8"""	)	).parent.parent.absolute()

   _snake_case							:	Dict			=						os.listdir(__A	)
   _snake_case							:	Optional[Any]			=						["""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 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						snapshot_download("""hf-internal-testing/processor_with_lm"""	)

   _snake_case							:	List[Any]			=						WavaVecaProcessorWithLM.from_pretrained(__A	)

   _snake_case							:	Optional[int]			=						processor.decoder.model_container[processor.decoder._model_key]
   _snake_case							:	Any			=						Path(language_model._kenlm_model.path.decode("""utf-8"""	)	).parent.parent.absolute()

   _snake_case							:	Union[str, Any]			=						os.listdir(__A	)
   _snake_case							:	Tuple			=						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 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""	)
   _snake_case							:	Dict			=						AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""	)

   _snake_case							:	Union[str, Any]			=						floats_list((3, 1_000)	)

   _snake_case							:	Optional[int]			=						processor_wavaveca(__A,   return_tensors="""np"""	)
   _snake_case							:	Optional[int]			=						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	)

   _snake_case							:	int			=						self._get_dummy_logits()

   _snake_case							:	Optional[int]			=						processor_wavaveca.batch_decode(__A	)
   _snake_case							:	Optional[Any]			=						processor_auto.batch_decode(__A	)

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


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.get_feature_extractor()
   _snake_case							:	int			=						self.get_tokenizer()
   _snake_case							:	Any			=						self.get_decoder()

   _snake_case							:	Optional[Any]			=						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 				UpperCamelCase_     (       a_:	Any,   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						[d[key] for d in offsets]
   return retrieved_list


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Any			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""	)
   _snake_case							:	List[str]			=						self._get_dummy_logits()[0]

   _snake_case							:	int			=						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 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""	)
   _snake_case							:	Tuple			=						self._get_dummy_logits()

   _snake_case							:	Dict			=						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 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   import torch

   _snake_case							:	Tuple			=						load_dataset("""common_voice""",   """en""",   split="""train""",   streaming=__A	)
   _snake_case							:	Optional[Any]			=						ds.cast_column("""audio""",   datasets.Audio(sampling_rate=16_000	)	)
   _snake_case							:	int			=						iter(__A	)
   _snake_case							:	Union[str, Any]			=						next(__A	)

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

   with torch.no_grad():
     _snake_case							:	Any			=						model(__A	).logits.cpu().numpy()

   _snake_case							:	Tuple			=						processor.decode(logits[0],   output_word_offsets=__A	)

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

   _snake_case							:	List[Any]			=						"""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
   _snake_case							:	List[str]			=						torch.tensor(self.get_from_offsets(__A,   """start_time"""	)	)
   _snake_case							:	str			=						torch.tensor(self.get_from_offsets(__A,   """end_time"""	)	)

   # fmt: off
   _snake_case							:	List[Any]			=						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]	)
   _snake_case							:	Dict			=						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(__A,   __A,   atol=0.01	)	)
   self.assertTrue(torch.allclose(__A,   __A,   atol=0.01	)	)








"""simple docstring"""


from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

A_      			=       (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A_      			=       [ord(letter) for letter in string.ascii_lowercase]
A_      			=       {ord(char) for char in VALID_CHARS}

A_      			=       ["the", "be", "to", "of", "and", "in", "that", "have"]


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      tuple[int, ...]					):
  """simple docstring"""

  _snake_case							:	str			=						""
  _snake_case							:	int
  _snake_case							:	int
  _snake_case							:	int

  for keychar, cipherchar in zip(cycle(snake_case__					)	,     snake_case__					):
    _snake_case							:	List[str]			=						cipherchar ^ keychar
    if decodedchar not in VALID_INTS:
      return None
    decoded += chr(snake_case__					)

  return decoded


def       UpperCAmelCase__ (snake_case__							:      list[int]					):
  """simple docstring"""

  _snake_case							:	list[str]			=						[]
  for key in product(snake_case__	,     repeat=3					):
    _snake_case							:	List[Any]			=						try_key(snake_case__	,     snake_case__					)
    if encoded is not None:
      possibles.append(snake_case__					)
  return possibles


def       UpperCAmelCase__ (snake_case__							:      list[str]	,     snake_case__							:      str					):
  """simple docstring"""

  return [possible for possible in possibles if common_word in possible.lower()]


def       UpperCAmelCase__ (snake_case__							:      str = "p059_cipher.txt"					):
  """simple docstring"""

  _snake_case							:	list[int]
  _snake_case							:	list[str]
  _snake_case							:	str
  _snake_case							:	str
  _snake_case							:	str			=						Path(snake_case__					).parent.joinpath(snake_case__					).read_text(encoding="""utf-8"""					)

  _snake_case							:	List[Any]			=						[int(snake_case__					) for number in data.strip().split(""","""					)]

  _snake_case							:	Optional[Any]			=						filter_valid_chars(snake_case__					)
  for common_word in COMMON_WORDS:
    _snake_case							:	Union[str, Any]			=						filter_common_word(snake_case__	,     snake_case__					)
    if len(snake_case__					) == 1:
      break

  _snake_case							:	Optional[int]			=						possibles[0]
  return sum(ord(snake_case__					) for char in decoded_text					)


if __name__ == "__main__":
     print(F'''{solution() = }''')


