






import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


__magic_name__:  List[Any]  	=			random.Random()

def 					UpperCamelCase		( _A,					_A=1.0,					_A=None,					_A=None				):






						"""simple docstring"""







						if rng is None:
												__magic_name__    :     str     		=      global_rng

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

						return values





@require_torch
class  snake_case__      (							unittest.TestCase   ):
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=4_00    ,     lowerCAmelCase__=20_00    ,     lowerCAmelCase__=1    ,     lowerCAmelCase__=0.0    ,     lowerCAmelCase__=2_40_00    ,     lowerCAmelCase__=True    ,     )							->   Optional[int]:
							__magic_name__    :     str     		=      parent
							__magic_name__    :     Union[str, Any]     		=      batch_size
							__magic_name__    :     List[str]     		=      min_seq_length
							__magic_name__    :     Tuple     		=      max_seq_length
							__magic_name__    :     Tuple     		=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__magic_name__    :     List[str]     		=      feature_size
							__magic_name__    :     List[str]     		=      padding_value
							__magic_name__    :     List[str]     		=      sampling_rate
							__magic_name__    :     Union[str, Any]     		=      return_attention_mask
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}







	def     __magic_name__       (       self    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=False				)							->   Any:
							def _flatten(lowerCAmelCase__				):
													return list(itertools.chain(*lowerCAmelCase__				)				)

							if equal_length:
													__magic_name__    :     List[str]     		=      floats_list((self.batch_size, self.max_seq_length)				)
							else:
													# make sure that inputs increase in size
													__magic_name__    :     str     		=      [
													    _flatten(floats_list((x, self.feature_size)				)				)
													    for x in range(self.min_seq_length    ,     self.max_seq_length    ,     self.seq_length_diff				)
													]

							if numpify:
													__magic_name__    :     str     		=      [np.asarray(lowerCAmelCase__				) for x in audio_inputs]

							return audio_inputs





@require_torch
class  snake_case__      (							_lowerCAmelCase		,						unittest.TestCase   ):
	lowercase__		:  str							   =					EncodecFeatureExtractor
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     int     		=      EnCodecFeatureExtractionTester(self				)
	def     __magic_name__       (       self				)							->   List[Any]:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							__magic_name__    :     int     		=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							# create three inputs of length 800, 1000, and 1200
							__magic_name__    :     Optional[int]     		=      [floats_list((1, x)				)[0] for x in range(8_00    ,     14_00    ,     2_00				)]
							__magic_name__    :     List[Any]     		=      [np.asarray(lowerCAmelCase__				) for audio_input in audio_inputs]

							# Test not batched input
							__magic_name__    :     Optional[int]     		=      feat_extract(audio_inputs[0]    ,     return_tensors="""np"""				).input_values
							__magic_name__    :     Any     		=      feat_extract(np_audio_inputs[0]    ,     return_tensors="""np"""				).input_values
							self.assertTrue(np.allclose(lowerCAmelCase__    ,     lowerCAmelCase__    ,     atol=1e-3				)				)

							# Test batched
							__magic_name__    :     Any     		=      feat_extract(lowerCAmelCase__    ,     padding=lowerCAmelCase__    ,     return_tensors="""np"""				).input_values
							__magic_name__    :     Union[str, Any]     		=      feat_extract(lowerCAmelCase__    ,     padding=lowerCAmelCase__    ,     return_tensors="""np"""				).input_values
							for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__    ,     lowerCAmelCase__				):
													self.assertTrue(np.allclose(lowerCAmelCase__    ,     lowerCAmelCase__    ,     atol=1e-3				)				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__    :     Any     		=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
							__magic_name__    :     int     		=      np.random.rand(1_00				).astype(np.floataa				)
							__magic_name__    :     List[Any]     		=      np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
													__magic_name__    :     Optional[int]     		=      feature_extractor.pad([{"""input_values""": inputs}]    ,     return_tensors="""np"""				)
													self.assertTrue(np_processed.input_values.dtype == np.floataa				)
													__magic_name__    :     Dict     		=      feature_extractor.pad([{"""input_values""": inputs}]    ,     return_tensors="""pt"""				)
													self.assertTrue(pt_processed.input_values.dtype == torch.floataa				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__				)							->   List[Any]:
							from datasets import load_dataset

							__magic_name__    :     List[str]     		=      load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,     """clean"""    ,     split="""validation"""				)
							# automatic decoding with librispeech
							__magic_name__    :     int     		=      ds.sort("""id"""				).select(range(lowerCAmelCase__				)				)[:num_samples]["""audio"""]

							return [x["array"] for x in audio_samples]
	def     __magic_name__       (       self				)							->   Tuple:
							# fmt: off
							__magic_name__    :     Optional[Any]     		=      torch.tensor(
							    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
							     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
							     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
							     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
							     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
							     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]				)
							# fmt: on
							__magic_name__    :     Union[str, Any]     		=      self._load_datasamples(1				)
							__magic_name__    :     Tuple     		=      EncodecFeatureExtractor()
							__magic_name__    :     Union[str, Any]     		=      feature_extractor(lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(input_values.shape    ,     (1, 1, 9_36_80)				)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,     lowerCAmelCase__    ,     atol=1e-6				)				)
	def     __magic_name__       (       self				)							->   Any:
							# fmt: off
							__magic_name__    :     List[str]     		=      torch.tensor(
							    [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
							     3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
							     2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
							     4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
							     7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
							     4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]				)
							# fmt: on
							__magic_name__    :     List[str]     		=      self._load_datasamples(1				)
							__magic_name__    :     Optional[int]     		=      [np.tile(input_audio[0][None]    ,     reps=(2, 1)				)]
							input_audio[0][1] *= 0.5
							__magic_name__    :     Union[str, Any]     		=      EncodecFeatureExtractor(feature_size=2				)
							__magic_name__    :     int     		=      feature_extractor(lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(input_values.shape    ,     (1, 2, 9_36_80)				)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,     lowerCAmelCase__    ,     atol=1e-6				)				)
							self.assertTrue(torch.allclose(input_values[0, 1, :30]    ,     EXPECTED_INPUT_VALUES * 0.5    ,     atol=1e-6				)				)







	def     __magic_name__       (       self				)							->   Optional[int]:
							__magic_name__    :     Optional[int]     		=      self._load_datasamples(2				)
							# would be easier if the stride was like
							__magic_name__    :     Optional[Any]     		=      EncodecFeatureExtractor(feature_size=1    ,     chunk_length_s=1    ,     overlap=0.0_1				)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    lowerCAmelCase__    ,     """^Both padding and truncation were set. Make sure you only set one.$"""    ,     ):
													__magic_name__    :     int     		=      feature_extractor(
													    lowerCAmelCase__    ,     padding="""max_length"""    ,     truncation=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values

							# truncate to chunk
							__magic_name__    :     Optional[int]     		=      feature_extractor(lowerCAmelCase__    ,     truncation=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (2, 1, 7_15_20)				)  # 2 chunks

							# force truncate to max_length
							__magic_name__    :     int     		=      feature_extractor(
							    lowerCAmelCase__    ,     truncation=lowerCAmelCase__    ,     max_length=4_80_00    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (2, 1, 4_80_00)				)

							# pad to chunk
							__magic_name__    :     Dict     		=      feature_extractor(lowerCAmelCase__    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(padded_outputs.shape    ,     (2, 1, 9_52_80)				)

							# pad to chunk
							__magic_name__    :     Optional[Any]     		=      feature_extractor(lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (2, 1, 9_52_80)				)

							# force pad to max length
							__magic_name__    :     Optional[Any]     		=      feature_extractor(
							    lowerCAmelCase__    ,     padding="""max_length"""    ,     max_length=10_00_00    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (2, 1, 10_00_00)				)

							# force no pad
							with self.assertRaisesRegex(
							    lowerCAmelCase__    ,     """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,     ):
													__magic_name__    :     Tuple     		=      feature_extractor(lowerCAmelCase__    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values

							__magic_name__    :     Union[str, Any]     		=      feature_extractor(input_audio[0]    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (1, 1, 9_36_80)				)

							# no pad if no chunk_length_s
							__magic_name__    :     Optional[Any]     		=      None
							with self.assertRaisesRegex(
							    lowerCAmelCase__    ,     """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,     ):
													__magic_name__    :     int     		=      feature_extractor(lowerCAmelCase__    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values

							__magic_name__    :     str     		=      feature_extractor(input_audio[0]    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (1, 1, 9_36_80)				)

							# no pad if no overlap
							__magic_name__    :     Tuple     		=      2
							__magic_name__    :     Optional[Any]     		=      None
							with self.assertRaisesRegex(
							    lowerCAmelCase__    ,     """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""    ,     ):
													__magic_name__    :     List[str]     		=      feature_extractor(lowerCAmelCase__    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values

							__magic_name__    :     Union[str, Any]     		=      feature_extractor(input_audio[0]    ,     padding=lowerCAmelCase__    ,     return_tensors="""pt"""				).input_values
							self.assertEquals(truncated_outputs.shape    ,     (1, 1, 9_36_80)				)








from typing import Literal

__magic_name__:  List[Any]  	=			"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     int     		=      LETTERS if mode == """decrypt""" else key
						__magic_name__    :     Optional[Any]     		=      key if mode == """decrypt""" else LETTERS
						__magic_name__    :     Optional[Any]     		=      """"""
						# loop through each symbol in the message
						for symbol in message:
												if symbol.upper() in chars_a:
																		# encrypt/decrypt the symbol
																		__magic_name__    :     Optional[Any]     		=      chars_a.find(symbol.upper()				)
																		if symbol.isupper():
																								translated += chars_b[sym_index].upper()
																		else:
																								translated += chars_b[sym_index].lower()
												else:
																		# symbol is not in LETTERS, just add it
																		translated += symbol
						return translated

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""encrypt"""				)

def 					UpperCamelCase		( _A,					_A				):






						"""simple docstring"""







						return translate_message(_A,					_A,					"""decrypt"""				)

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     Optional[Any]     		=      """Hello World"""
						__magic_name__    :     str     		=      """QWERTYUIOPASDFGHJKLZXCVBNM"""
						__magic_name__    :     List[str]     		=      """decrypt"""  # set to 'encrypt' or 'decrypt'

						if mode == "encrypt":
												__magic_name__    :     Dict     		=      encrypt_message(_A,					_A				)
						elif mode == "decrypt":
												__magic_name__    :     Any     		=      decrypt_message(_A,					_A				)
						print(f'Using the key {key}, the {mode}ed message is: {translated}'				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()
