





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
    import torch

if is_datasets_available():
    from datasets import load_dataset

_UpperCAmelCase							:			Optional[Any]        =							random.Random()


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None					)     ->	List[str]:
    if rng is None:
        lowerCamelCase__       : List[str]   					=						global_rng

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

    return values

class   lowerCAmelCase				(   unittest.TestCase     ):
 def __init__( self    : Union[str, Any]     ,	UpperCAmelCase    : Tuple     ,	UpperCAmelCase    : Optional[Any]=7     ,	UpperCAmelCase    : List[str]=400     ,	UpperCAmelCase    : Dict=2000     ,	UpperCAmelCase    : Union[str, Any]=2048     ,	UpperCAmelCase    : int=128     ,	UpperCAmelCase    : List[Any]=1     ,	UpperCAmelCase    : List[Any]=512     ,	UpperCAmelCase    : Optional[Any]=30     ,	UpperCAmelCase    : Optional[int]=44100     ,	)     ->      List[str]:
     lowerCamelCase__       : Tuple   					=						parent
     lowerCamelCase__       : Tuple   					=						batch_size
     lowerCamelCase__       : Any   					=						min_seq_length
     lowerCamelCase__       : List[str]   					=						max_seq_length
     lowerCamelCase__       : List[Any]   					=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     lowerCamelCase__       : Optional[Any]   					=						spectrogram_length
     lowerCamelCase__       : str   					=						feature_size
     lowerCamelCase__       : Optional[Any]   					=						num_audio_channels
     lowerCamelCase__       : int   					=						hop_length
     lowerCamelCase__       : Optional[Any]   					=						chunk_length
     lowerCamelCase__       : Optional[int]   					=						sampling_rate
 def 	A_      ( self    : Optional[Any]      )     ->      List[Any]:
     return {
         "spectrogram_length": self.spectrogram_length,
         "feature_size": self.feature_size,
         "num_audio_channels": self.num_audio_channels,
         "hop_length": self.hop_length,
         "chunk_length": self.chunk_length,
         "sampling_rate": self.sampling_rate,
     }







 def 	A_      ( self    : List[str]     ,	UpperCAmelCase    : Union[str, Any]=False     ,	UpperCAmelCase    : Dict=False      )     ->      int:
     def _flatten(UpperCAmelCase    : List[Any]      ):
         return list(itertools.chain(*UpperCAmelCase      )      )

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


@require_torch
@require_torchaudio
class   lowerCAmelCase				(   __UpperCamelCase,    unittest.TestCase     ):
 UpperCAmelCase__						=						TvltFeatureExtractor
 def 	A_      ( self    : Union[str, Any]      )     ->      Optional[int]:
     lowerCamelCase__       : Tuple   					=						TvltFeatureExtractionTester(self      )
 def 	A_      ( self    : List[Any]      )     ->      int:
     lowerCamelCase__       : Tuple   					=						self.feature_extraction_class(**self.feat_extract_dict      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'spectrogram_length'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'feature_size'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'num_audio_channels'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'hop_length'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'chunk_length'      )      )
     self.assertTrue(hasattr(UpperCAmelCase     ,	'sampling_rate'      )      )
 def 	A_      ( self    : str      )     ->      Optional[Any]:
     lowerCamelCase__       : Dict   					=						self.feature_extraction_class(**self.feat_extract_dict      )

     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__       : str   					=						feat_extract_first.save_pretrained(UpperCAmelCase      )[0]
         check_json_file_has_correct_format(UpperCAmelCase      )
         lowerCamelCase__       : Union[str, Any]   					=						self.feature_extraction_class.from_pretrained(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						feat_extract_first.to_dict()
     lowerCamelCase__       : int   					=						feat_extract_second.to_dict()
     lowerCamelCase__       : Union[str, Any]   					=						dict_first.pop('mel_filters'      )
     lowerCamelCase__       : Tuple   					=						dict_second.pop('mel_filters'      )
     self.assertTrue(np.allclose(UpperCAmelCase     ,	UpperCAmelCase      )      )
     self.assertEqual(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Optional[Any]      )     ->      Any:
     lowerCamelCase__       : Dict   					=						self.feature_extraction_class(**self.feat_extract_dict      )

     with tempfile.TemporaryDirectory() as tmpdirname:
         lowerCamelCase__       : Tuple   					=						os.path.join(UpperCAmelCase     ,	'feat_extract.json'      )
         feat_extract_first.to_json_file(UpperCAmelCase      )
         lowerCamelCase__       : List[Any]   					=						self.feature_extraction_class.from_json_file(UpperCAmelCase      )

     lowerCamelCase__       : Dict   					=						feat_extract_first.to_dict()
     lowerCamelCase__       : Any   					=						feat_extract_second.to_dict()
     lowerCamelCase__       : List[str]   					=						dict_first.pop('mel_filters'      )
     lowerCamelCase__       : int   					=						dict_second.pop('mel_filters'      )
     self.assertTrue(np.allclose(UpperCAmelCase     ,	UpperCAmelCase      )      )
     self.assertEqual(UpperCAmelCase     ,	UpperCAmelCase      )
 def 	A_      ( self    : Dict      )     ->      Optional[int]:
     # Initialize feature_extractor
     lowerCamelCase__       : Any   					=						self.feature_extraction_class(**self.feat_extract_dict      )

     # create three inputs of length 800, 1000, and 1200
     lowerCamelCase__       : Tuple   					=						[floats_list((1, x)      )[0] for x in range(800     ,	1400     ,	200      )]
     lowerCamelCase__       : Optional[Any]   					=						[np.asarray(UpperCAmelCase      ) for speech_input in speech_inputs]

     # Test not batched input
     lowerCamelCase__       : Optional[int]   					=						feature_extractor(np_speech_inputs[0]     ,	return_tensors='np'     ,	sampling_rate=44100      ).audio_values

     self.assertTrue(encoded_audios.ndim == 4      )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

     # Test batched
     lowerCamelCase__       : Any   					=						feature_extractor(UpperCAmelCase     ,	return_tensors='np'     ,	sampling_rate=44100      ).audio_values

     self.assertTrue(encoded_audios.ndim == 4      )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

     # Test audio masking
     lowerCamelCase__       : Optional[Any]   					=						feature_extractor(
         UpperCAmelCase     ,	return_tensors='np'     ,	sampling_rate=44100     ,	mask_audio=UpperCAmelCase      ).audio_values

     self.assertTrue(encoded_audios.ndim == 4      )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )

     # Test 2-D numpy arrays are batched.
     lowerCamelCase__       : Any   					=						[floats_list((1, x)      )[0] for x in (800, 800, 800)]
     lowerCamelCase__       : str   					=						np.asarray(UpperCAmelCase      )
     lowerCamelCase__       : int   					=						feature_extractor(UpperCAmelCase     ,	return_tensors='np'     ,	sampling_rate=44100      ).audio_values
     self.assertTrue(encoded_audios.ndim == 4      )
     self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size      )
     self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length      )
     self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels      )
 def 	A_      ( self    : Dict     ,	UpperCAmelCase    : int      )     ->      Any:
     lowerCamelCase__       : Optional[int]   					=						load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,	'clean'     ,	split='validation'      )
     # automatic decoding with librispeech
     lowerCamelCase__       : List[Any]   					=						ds.sort('id'      ).select(range(UpperCAmelCase      )      )[:num_samples]['audio']

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







 def 	A_      ( self    : List[str]      )     ->      List[str]:
     lowerCamelCase__       : Dict   					=						self._load_datasamples(1      )
     lowerCamelCase__       : int   					=						TvltFeatureExtractor()
     lowerCamelCase__       : str   					=						feature_extractor(UpperCAmelCase     ,	return_tensors='pt'      ).audio_values

     self.assertEquals(audio_values.shape     ,	(1, 1, 192, 128)      )

     lowerCamelCase__       : List[Any]   					=						torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]      )
     self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,	UpperCAmelCase     ,	atol=1e-4      )      )












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : Dict   					=						''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase					)     ->	dict[str, str]:
    lowerCamelCase__       : List[str]   					=						[chr(i + 65					) for i in range(26					)]
    # Remove duplicate characters from key
    lowerCamelCase__       : Any   					=						remove_duplicates(key.upper()					)
    lowerCamelCase__       : List[Any]   					=						len(_UpperCAmelCase					)
    # First fill cipher with key characters
    lowerCamelCase__       : Optional[int]   					=						{alphabet[i]: char for i, char in enumerate(_UpperCAmelCase					)}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase					)		,      26					):
        lowerCamelCase__       : Any   					=						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            lowerCamelCase__       : Union[str, Any]   					=						alphabet[i - offset]
        lowerCamelCase__       : int   					=						char
    return cipher_alphabet


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    return "".join(cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	str:
    lowerCamelCase__       : List[str]   					=						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase		,      _UpperCAmelCase					) for ch in message.upper()					)


def 			SCREAMING_SNAKE_CASE (			)     ->	None:
    lowerCamelCase__       : str   					=						input('Enter message to encode or decode: '					).strip()
    lowerCamelCase__       : Union[str, Any]   					=						input('Enter keyword: '					).strip()
    lowerCamelCase__       : Dict   					=						input('Encipher or decipher? E/D:'					).strip()[0].lower()
    try:
        lowerCamelCase__       : Dict   					=						{'e': encipher, 'd': decipher}[option]
    except KeyError:
        raise KeyError('invalid input option'					)
    lowerCamelCase__       : int   					=						create_cipher_map(_UpperCAmelCase					)
    print(func(_UpperCAmelCase		,      _UpperCAmelCase					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





