



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

_lowercase     							=						random.Random()

def      _A				(UpperCamelCase		:      str      ,			UpperCamelCase		:      Tuple=1.0      ,			UpperCamelCase		:      Optional[int]=None      ,			UpperCamelCase		:      List[Any]=None )  ->Optional[Any]:



			'''simple docstring'''







			if rng is None:
						lowerCamelCase__ :      Optional[Any]								=		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 				__A					( unittest.TestCase      ):





			def __init__(self							,    __magic_name__							,    __magic_name__=7							,    __magic_name__=400							,    __magic_name__=2000							,    __magic_name__=2048							,    __magic_name__=128							,    __magic_name__=1							,    __magic_name__=512							,    __magic_name__=30							,    __magic_name__=44100							,    ):
						lowerCamelCase__ :      Any								=		parent
						lowerCamelCase__ :      int								=		batch_size
						lowerCamelCase__ :      str								=		min_seq_length
						lowerCamelCase__ :      Optional[Any]								=		max_seq_length
						lowerCamelCase__ :      Tuple								=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						lowerCamelCase__ :      int								=		spectrogram_length
						lowerCamelCase__ :      Tuple								=		feature_size
						lowerCamelCase__ :      Any								=		num_audio_channels
						lowerCamelCase__ :      List[str]								=		hop_length
						lowerCamelCase__ :      Any								=		chunk_length
						lowerCamelCase__ :      List[str]								=		sampling_rate





			def        _snake_case    (self				):
						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        _snake_case    (self							,    __magic_name__=False							,    __magic_name__=False				):

						def _flatten(__magic_name__				):
									return list(itertools.chain(*__magic_name__				)				)

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







@require_torch
@require_torchaudio
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:str     =			TvltFeatureExtractor





			def        _snake_case    (self				):
						lowerCamelCase__ :      str								=		TvltFeatureExtractionTester(self				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.feature_extraction_class(**self.feat_extract_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """spectrogram_length"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """feature_size"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """num_audio_channels"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """hop_length"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """chunk_length"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """sampling_rate"""				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[int]								=		self.feature_extraction_class(**self.feat_extract_dict				)

						with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase__ :      Dict								=		feat_extract_first.save_pretrained(__magic_name__				)[0]
									check_json_file_has_correct_format(__magic_name__				)
									lowerCamelCase__ :      Optional[Any]								=		self.feature_extraction_class.from_pretrained(__magic_name__				)

						lowerCamelCase__ :      List[Any]								=		feat_extract_first.to_dict()
						lowerCamelCase__ :      List[Any]								=		feat_extract_second.to_dict()
						lowerCamelCase__ :      str								=		dict_first.pop("""mel_filters"""				)
						lowerCamelCase__ :      Tuple								=		dict_second.pop("""mel_filters"""				)
						self.assertTrue(np.allclose(__magic_name__							,    __magic_name__				)				)
						self.assertEqual(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.feature_extraction_class(**self.feat_extract_dict				)

						with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase__ :      str								=		os.path.join(__magic_name__							,    """feat_extract.json"""				)
									feat_extract_first.to_json_file(__magic_name__				)
									lowerCamelCase__ :      str								=		self.feature_extraction_class.from_json_file(__magic_name__				)

						lowerCamelCase__ :      Any								=		feat_extract_first.to_dict()
						lowerCamelCase__ :      Dict								=		feat_extract_second.to_dict()
						lowerCamelCase__ :      Optional[int]								=		dict_first.pop("""mel_filters"""				)
						lowerCamelCase__ :      Any								=		dict_second.pop("""mel_filters"""				)
						self.assertTrue(np.allclose(__magic_name__							,    __magic_name__				)				)
						self.assertEqual(__magic_name__							,    __magic_name__				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		self.feature_extraction_class(**self.feat_extract_dict				)

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

						# Test not batched input
						lowerCamelCase__ :      str								=		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__ :      Dict								=		feature_extractor(__magic_name__							,    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[int]								=		feature_extractor(
						    __magic_name__							,    return_tensors="""np"""							,    sampling_rate=44100							,    mask_audio=__magic_name__				).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__ :      Optional[Any]								=		np.asarray(__magic_name__				)
						lowerCamelCase__ :      List[str]								=		feature_extractor(__magic_name__							,    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        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Any								=		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""							,    """clean"""							,    split="""validation"""				)
						# automatic decoding with librispeech
						lowerCamelCase__ :      Dict								=		ds.sort("""id"""				).select(range(__magic_name__				)				)[:num_samples]["""audio"""]

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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		self._load_datasamples(1				)
						lowerCamelCase__ :      Optional[int]								=		TvltFeatureExtractor()
						lowerCamelCase__ :      Any								=		feature_extractor(__magic_name__							,    return_tensors="""pt"""				).audio_values

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

						lowerCamelCase__ :      str								=		torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
						self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]							,    __magic_name__							,    atol=1E-4				)				)








from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def      _A				(UpperCamelCase		:      str )  ->None:



			'''simple docstring'''







			lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		analyze_text(UpperCamelCase )
			lowerCamelCase__ :      Any								=		list(""" """ + ascii_lowercase )
			# what is our total sum of probabilities.
			lowerCamelCase__ :      str								=		sum(single_char_strings.values() )

			# one length string
			lowerCamelCase__ :      Union[str, Any]								=		0
			# for each alpha we go in our dict and if it is in it we calculate entropy
			for ch in my_alphas:
						if ch in single_char_strings:
									lowerCamelCase__ :      List[Any]								=		single_char_strings[ch]
									lowerCamelCase__ :      List[str]								=		my_str / all_sum
									my_fir_sum += prob * math.loga(UpperCamelCase )  # entropy formula.

    # print entropy
			print(f"{round(-1 * my_fir_sum ):.1f}" )

			# two len string
			lowerCamelCase__ :      str								=		sum(two_char_strings.values() )
			lowerCamelCase__ :      int								=		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCamelCase__ :      str								=		cha + cha
									if sequence in two_char_strings:
												lowerCamelCase__ :      str								=		two_char_strings[sequence]
												lowerCamelCase__ :      int								=		int(UpperCamelCase ) / all_sum
												my_sec_sum += prob * math.loga(UpperCamelCase )

    # print second entropy
			print(f"{round(-1 * my_sec_sum ):.1f}" )

			# print the difference between them
			print(f"{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}" )






def      _A				(UpperCamelCase		:      str )  ->tuple[dict, dict]:



			'''simple docstring'''







			lowerCamelCase__ :      Optional[int]								=		Counter()  # type: ignore
			lowerCamelCase__ :      List[Any]								=		Counter()  # type: ignore
			single_char_strings[text[-1]] += 1

			# first case when we have space at start.
			two_char_strings[" " + text[0]] += 1
			for i in range(0      ,			len(UpperCamelCase ) - 1 ):
						single_char_strings[text[i]] += 1
						two_char_strings[text[i : i + 2]] += 1
			return single_char_strings, two_char_strings






def      _A				()  ->List[str]:



			'''simple docstring'''







			import doctest

			doctest.testmod()
			# text = (
			#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
			#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
			#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
			#     "beloved evident through no service elderly is. Blind there if every no so "
			#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
			#     "of message cottage windows do besides against uncivil.  Delightful "
			#     "unreserved impossible few estimating men favourable see entreaties. She "
			#     "propriety immediate was improving. He or entrance humoured likewise "
			#     "moderate. Much nor game son say feel. Fat make met can must form into "
			#     "gate. Me we offending prevailed discovery. "
			# )

			# calculate_prob(text)


if __name__ == "__main__":
				main()



