





import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


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

if is_torch_available():
		import torch




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Any=1.0			,				SCREAMING_SNAKE_CASE__      :  str=None			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]=None       )	->    int:

							'''simple docstring'''






							if rng is None:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						global_rng

							SCREAMING_SNAKE_CASE__       :      Any     =						[]
							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      ):







							"""simple docstring"""




							def __init__( self     :					Optional[int],			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Tuple=7,			_UpperCAmelCase     :					List[Any]=4_0_0,			_UpperCAmelCase     :					Dict=2_0_0_0,			_UpperCAmelCase     :					Dict=1,			_UpperCAmelCase     :					Dict=0.0,			_UpperCAmelCase     :					int=1_6_0_0_0,			_UpperCAmelCase     :					Optional[Any]=True,			_UpperCAmelCase     :					int=True,			)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						parent
														SCREAMING_SNAKE_CASE__       :      int     =						batch_size
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						min_seq_length
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						max_seq_length
														SCREAMING_SNAKE_CASE__       :      List[str]     =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						feature_size
														SCREAMING_SNAKE_CASE__       :      Tuple     =						padding_value
														SCREAMING_SNAKE_CASE__       :      Any     =						sampling_rate
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						return_attention_mask
														SCREAMING_SNAKE_CASE__       :      int     =						do_normalize






							def  A_     ( self     :					List[Any]						)				->			List[Any]:





														"""simple docstring"""
														return {
														    "feature_size": self.feature_size,
														    "padding_value": self.padding_value,
														    "sampling_rate": self.sampling_rate,
														    "return_attention_mask": self.return_attention_mask,
														    "do_normalize": self.do_normalize,
														}






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					str=False,			_UpperCAmelCase     :					Dict=False						)				->			Dict:





														"""simple docstring"""







														def _flatten(_UpperCAmelCase     :					str						):
																					return list(itertools.chain(*_lowerCamelCase						)						)

														if equal_length:
																					SCREAMING_SNAKE_CASE__       :      str     =						floats_list((self.batch_size, self.max_seq_length)						)
														else:
																					# make sure that inputs increase in size
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[
																					    _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:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[np.asarray(_lowerCamelCase						) for x in speech_inputs]

														return speech_inputs







@require_torch
@require_torchaudio
class 						lowerCamelCase       (a__							,     unittest.TestCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ASTFeatureExtractor






							def  A_     ( self     :					int						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						ASTFeatureExtractionTester(self						)






							def  A_     ( self     :					Union[str, Any]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														# create three inputs of length 800, 1000, and 1200
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[floats_list((1, x)						)[0] for x in range(8_0_0,			1_4_0_0,			2_0_0						)]
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[np.asarray(_lowerCamelCase						) for speech_input in speech_inputs]

														# Test not batched input
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						feat_extract(speech_inputs[0],			return_tensors="np"						).input_values
														SCREAMING_SNAKE_CASE__       :      int     =						feat_extract(np_speech_inputs[0],			return_tensors="np"						).input_values
														self.assertTrue(np.allclose(_lowerCamelCase,			_lowerCamelCase,			atol=1E-3						)						)

														# Test batched
														SCREAMING_SNAKE_CASE__       :      str     =						feat_extract(_lowerCamelCase,			padding=_lowerCamelCase,			return_tensors="np"						).input_values
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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						)						)

														# Test 2-D numpy arrays are batched.
														SCREAMING_SNAKE_CASE__       :      Dict     =						[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
														SCREAMING_SNAKE_CASE__       :      int     =						np.asarray(_lowerCamelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						feat_extract(_lowerCamelCase,			return_tensors="np"						).input_values
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						feat_extract(_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						)						)






							@require_torch
							def  A_     ( self     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														import torch

														SCREAMING_SNAKE_CASE__       :      Dict     =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						np.random.rand(1_0_0						).astype(np.floataa						)
														SCREAMING_SNAKE_CASE__       :      int     =						np_speech_inputs.tolist()

														for inputs in [py_speech_inputs, np_speech_inputs]:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						feature_extractor.pad([{"input_values": inputs}],			return_tensors="np"						)
																					self.assertTrue(np_processed.input_values.dtype == np.floataa						)
																					SCREAMING_SNAKE_CASE__       :      int     =						feature_extractor.pad([{"input_values": inputs}],			return_tensors="pt"						)
																					self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					Tuple						)				->			Optional[int]:





														"""simple docstring"""
														from datasets import load_dataset

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

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






							@require_torch
							def  A_     ( self     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(
														    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
														     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
														     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
														     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]						)
														# fmt: on

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self._load_datasamples(1						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						ASTFeatureExtractor()
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						feature_extractor(_lowerCamelCase,			return_tensors="pt"						).input_values
														self.assertEquals(input_values.shape,			(1, 1_0_2_4, 1_2_8)						)
														self.assertTrue(torch.allclose(input_values[0, 0, :3_0],			_lowerCamelCase,			atol=1E-4						)						)













from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						analyze_text(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						list(" " + ascii_lowercase       )
							# what is our total sum of probabilities.
							SCREAMING_SNAKE_CASE__       :      str     =						sum(single_char_strings.values()       )

							# one length string
							SCREAMING_SNAKE_CASE__       :      str     =						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:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						single_char_strings[ch]
																					SCREAMING_SNAKE_CASE__       :      Any     =						my_str / all_sum
																					my_fir_sum += prob * math.loga(SCREAMING_SNAKE_CASE__       )  # entropy formula.

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

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

    # 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 (  SCREAMING_SNAKE_CASE__      :  str       )	->    tuple[dict, dict]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Counter()  # type: ignore
							SCREAMING_SNAKE_CASE__       :      Dict     =						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(SCREAMING_SNAKE_CASE__       ) - 1       ):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings




def _a (  )	->    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()






