import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import SpeechaTextFeatureExtractor

__SCREAMING_SNAKE_CASE						:   List[str]         =			random.Random()
def      snake_case						(__lowercase ,      __lowercase=1.0 ,      __lowercase=None ,      __lowercase=None						)	->	List[Any]:

						'''simple docstring'''



						if rng is None:
												_snake_case						:					List[str]    =   global_rng

						_snake_case						:					Dict    =   []
						for batch_idx in range(shape[0]						):
												values.append([]						)
												for _ in range(shape[1]						):
																		values[-1].append(rng.random() * scale						)

						return values



@require_torch
@require_torchaudio
class        lowercase_   (					unittest.TestCase		):



							def __init__(						self				,    lowercase_				,    lowercase_=7				,    lowercase_=400				,    lowercase_=2_000				,    lowercase_=24				,    lowercase_=24				,    lowercase_=0.0				,    lowercase_=16_000				,    lowercase_=True				,    lowercase_=True				,    ):
													_snake_case						:					Optional[int]    =   parent
													_snake_case						:					Tuple    =   batch_size
													_snake_case						:					int    =   min_seq_length
													_snake_case						:					str    =   max_seq_length
													_snake_case						:					str    =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													_snake_case						:					List[Any]    =   feature_size
													_snake_case						:					List[str]    =   num_mel_bins
													_snake_case						:					Optional[Any]    =   padding_value
													_snake_case						:					str    =   sampling_rate
													_snake_case						:					Dict    =   return_attention_mask
													_snake_case						:					Dict    =   do_normalize



							def 	UpperCamelCase	(						self    ):
													return {
													    "feature_size": self.feature_size,
													    "num_mel_bins": self.num_mel_bins,
													    "padding_value": self.padding_value,
													    "sampling_rate": self.sampling_rate,
													    "return_attention_mask": self.return_attention_mask,
													    "do_normalize": self.do_normalize,
													}



							def 	UpperCamelCase	(						self				,    lowercase_=False				,    lowercase_=False    ):
													def _flatten(lowercase_    ):
																			return list(itertools.chain(*lowercase_    )    )

													if equal_length:
																			_snake_case						:					List[str]    =   [floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
													else:
																			# make sure that inputs increase in size
																			_snake_case						:					Dict    =   [
																			    floats_list((x, self.feature_size)    )
																			    for x in range(self.min_seq_length				,    self.max_seq_length				,    self.seq_length_diff    )
																			]
													if numpify:
																			_snake_case						:					Optional[Any]    =   [np.asarray(lowercase_    ) for x in speech_inputs]
													return speech_inputs




@require_torch
@require_torchaudio
class        lowercase_   (					__snake_case						,			unittest.TestCase		):
							_lowerCamelCase						=		SpeechaTextFeatureExtractor if is_speech_available() else None



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   SpeechaTextFeatureExtractionTester(self    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													self.assertTrue(np.all(np.mean(lowercase_				,    axis=0    ) < 1e-3    )    )
													self.assertTrue(np.all(np.abs(np.var(lowercase_				,    axis=0    ) - 1    ) < 1e-3    )    )



							def 	UpperCamelCase	(						self    ):
													# Tests that all call wrap to encode_plus and batch_encode_plus
													_snake_case						:					Tuple    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													# create three inputs of length 800, 1000, and 1200
													_snake_case						:					List[Any]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Union[str, Any]    =   [np.asarray(lowercase_    ) for speech_input in speech_inputs]

													# Test feature size
													_snake_case						:					str    =   feature_extractor(lowercase_				,    padding=lowercase_				,    return_tensors="np"    ).input_features
													self.assertTrue(input_features.ndim == 3    )
													self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size    )

													# Test not batched input
													_snake_case						:					Tuple    =   feature_extractor(speech_inputs[0]				,    return_tensors="np"    ).input_features
													_snake_case						:					Dict    =   feature_extractor(np_speech_inputs[0]				,    return_tensors="np"    ).input_features
													self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )

													# Test batched
													_snake_case						:					Optional[Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_features
													_snake_case						:					List[str]    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_features
													for enc_seq_a, enc_seq_a in zip(lowercase_				,    lowercase_    ):
																			self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )

													# Test 2-D numpy arrays are batched.
													_snake_case						:					Union[str, Any]    =   [floats_list((1, x)    )[0] for x in (800, 800, 800)]
													_snake_case						:					Optional[Any]    =   np.asarray(lowercase_    )
													_snake_case						:					List[str]    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_features
													_snake_case						:					int    =   feature_extractor(lowercase_				,    return_tensors="np"    ).input_features
													for enc_seq_a, enc_seq_a in zip(lowercase_				,    lowercase_    ):
																			self.assertTrue(np.allclose(lowercase_				,    lowercase_				,    atol=1e-3    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					List[Any]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]

													_snake_case						:					str    =   ["longest", "max_length", "do_not_pad"]
													_snake_case						:					str    =   [None, 16, None]
													for max_length, padding in zip(lowercase_				,    lowercase_    ):
																			_snake_case						:					Dict    =   feature_extractor(
																			    lowercase_				,    padding=lowercase_				,    max_length=lowercase_				,    return_attention_mask=lowercase_    )
																			_snake_case						:					Dict    =   inputs.input_features
																			_snake_case						:					Any    =   inputs.attention_mask
																			_snake_case						:					Dict    =   [np.sum(lowercase_    ) for x in attention_mask]

																			self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
																			self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
																			self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Optional[Any]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]

													_snake_case						:					Optional[Any]    =   ["longest", "max_length", "do_not_pad"]
													_snake_case						:					List[str]    =   [None, 16, None]
													for max_length, padding in zip(lowercase_				,    lowercase_    ):
																			_snake_case						:					Optional[Any]    =   feature_extractor(
																			    lowercase_				,    max_length=lowercase_				,    padding=lowercase_				,    return_tensors="np"				,    return_attention_mask=lowercase_    )
																			_snake_case						:					int    =   inputs.input_features
																			_snake_case						:					Union[str, Any]    =   inputs.attention_mask
																			_snake_case						:					List[str]    =   [np.sum(lowercase_    ) for x in attention_mask]

																			self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]    )
																			self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6    )
																			self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]    )
																			self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6    )
																			self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[Any]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Any    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					List[Any]    =   feature_extractor(
													    lowercase_				,    padding="max_length"				,    max_length=4				,    truncation=lowercase_				,    return_tensors="np"				,    return_attention_mask=lowercase_				,    )
													_snake_case						:					Any    =   inputs.input_features
													_snake_case						:					List[str]    =   inputs.attention_mask
													_snake_case						:					Union[str, Any]    =   np.sum(attention_mask == 1				,    axis=1    )

													self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
													self._check_zero_mean_unit_variance(input_features[1]    )
													self._check_zero_mean_unit_variance(input_features[2]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Union[str, Any]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Optional[int]    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Any    =   feature_extractor(
													    lowercase_				,    padding="longest"				,    max_length=4				,    truncation=lowercase_				,    return_tensors="np"				,    return_attention_mask=lowercase_				,    )
													_snake_case						:					List[str]    =   inputs.input_features
													_snake_case						:					List[str]    =   inputs.attention_mask
													_snake_case						:					Tuple    =   np.sum(attention_mask == 1				,    axis=1    )

													self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
													self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
													self._check_zero_mean_unit_variance(input_features[2]    )

													# make sure that if max_length < longest -> then pad to max_length
													self.assertEqual(input_features.shape				,    (3, 4, 24)    )

													_snake_case						:					Any    =   [floats_list((1, x)    )[0] for x in range(800				,    1_400				,    200    )]
													_snake_case						:					Optional[Any]    =   feature_extractor(
													    lowercase_				,    padding="longest"				,    max_length=16				,    truncation=lowercase_				,    return_tensors="np"				,    return_attention_mask=lowercase_				,    )
													_snake_case						:					Union[str, Any]    =   inputs.input_features
													_snake_case						:					int    =   inputs.attention_mask
													_snake_case						:					int    =   np.sum(attention_mask == 1				,    axis=1    )

													self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]    )
													self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]    )
													self._check_zero_mean_unit_variance(input_features[2]    )

													# make sure that if max_length < longest -> then pad to max_length
													self.assertEqual(input_features.shape				,    (3, 6, 24)    )



							def 	UpperCamelCase	(						self    ):
													import torch

													_snake_case						:					Any    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Any    =   np.random.rand(100				,    32    ).astype(np.floataa    )
													_snake_case						:					str    =   np_speech_inputs.tolist()

													for inputs in [py_speech_inputs, np_speech_inputs]:
																			_snake_case						:					Union[str, Any]    =   feature_extractor.pad([{"input_features": inputs}]				,    return_tensors="np"    )
																			self.assertTrue(np_processed.input_features.dtype == np.floataa    )
																			_snake_case						:					List[Any]    =   feature_extractor.pad([{"input_features": inputs}]				,    return_tensors="pt"    )
																			self.assertTrue(pt_processed.input_features.dtype == torch.floataa    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													from datasets import load_dataset

													_snake_case						:					Tuple    =   load_dataset("hf-internal-testing/librispeech_asr_dummy"				,    "clean"				,    split="validation"    )
													# automatic decoding with librispeech
													_snake_case						:					Tuple    =   ds.sort("id"    ).select(range(lowercase_    )    )[:num_samples]["audio"]

													return [x["array"] for x in speech_samples]



							def 	UpperCamelCase	(						self    ):
													# fmt: off
													_snake_case						:					Any    =   np.array([
													    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
													    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
													    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
													]    )
													# fmt: on

													_snake_case						:					Any    =   self._load_datasamples(1    )
													_snake_case						:					Optional[Any]    =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
													_snake_case						:					Tuple    =   feature_extractor(lowercase_				,    return_tensors="pt"    ).input_features
													self.assertEquals(input_features.shape				,    (1, 584, 24)    )
													self.assertTrue(np.allclose(input_features[0, 0, :30]				,    lowercase_				,    atol=1e-4    )    )
from __future__ import annotations

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

__SCREAMING_SNAKE_CASE						:   str         =			(
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE						:   list[int]         =			[ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE						:   set[int]         =			{ord(char) for char in VALID_CHARS}

__SCREAMING_SNAKE_CASE						:   list[str]         =			["the", "be", "to", "of", "and", "in", "that", "have"]
def  snake_case						(__lowercase ,      __lowercase						)	->	str | None:

						'''simple docstring'''



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

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

						return decoded
def  snake_case						(__lowercase						)	->	list[str]:

						'''simple docstring'''



						_snake_case						:					list[str]    =   []
						for key in product(__lowercase ,      repeat=3						):
												_snake_case						:					Union[str, Any]    =   try_key(__lowercase ,      __lowercase						)
												if encoded is not None:
																		possibles.append(__lowercase						)
						return possibles
def  snake_case						(__lowercase ,      __lowercase						)	->	list[str]:

						'''simple docstring'''



						return [possible for possible in possibles if common_word in possible.lower()]
def  snake_case						(__lowercase = "p059_cipher.txt"						)	->	int:

						'''simple docstring'''



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

						_snake_case						:					Dict    =   [int(__lowercase						) for number in data.strip().split(","						)]

						_snake_case						:					Tuple    =   filter_valid_chars(__lowercase						)
						for common_word in COMMON_WORDS:
												_snake_case						:					Optional[int]    =   filter_common_word(__lowercase ,      __lowercase						)
												if len(__lowercase						) == 1:
																		break

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


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