import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
		from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

		from .test_feature_extraction_speechta import floats_list


__a  :					Tuple          =  get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_torch
class        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






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

							'''simple docstring'''


							__lowercase			      =							tempfile.mkdtemp()

							__lowercase			      =							SpeechTaTokenizer(lowerCAmelCase__						)
							tokenizer.save_pretrained(self.tmpdirname						)

							__lowercase			      =							{
							    '''feature_size''': 1,
							    '''padding_value''': 0.0,
							    '''sampling_rate''': 1_60_00,
							    '''do_normalize''': False,
							    '''num_mel_bins''': 80,
							    '''hop_length''': 16,
							    '''win_length''': 64,
							    '''win_function''': '''hann_window''',
							    '''fmin''': 80,
							    '''fmax''': 76_00,
							    '''mel_floor''': 1E-10,
							    '''reduction_factor''': 2,
							    '''return_attention_mask''': True,
							}

							__lowercase			      =							os.path.join(self.tmpdirname				,       lowerCAmelCase__						)
							with open(self.feature_extraction_file				,       '''w'''				,       encoding='''utf-8'''						) as fp:
											fp.write(json.dumps(lowerCAmelCase__						) + '''\n'''						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       **lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,       **lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       **lowerCAmelCase__						)					-> Any:

							'''simple docstring'''


							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,       **lowerCAmelCase__						)





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

							'''simple docstring'''


							shutil.rmtree(self.tmpdirname						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							__lowercase			      =							self.get_tokenizer()
							__lowercase			      =							self.get_feature_extractor()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							processor.save_pretrained(self.tmpdirname						)
							__lowercase			      =							SpeechTaProcessor.from_pretrained(self.tmpdirname						)

							self.assertEqual(processor.tokenizer.get_vocab()				,       tokenizer.get_vocab()						)
							self.assertIsInstance(processor.tokenizer				,       lowerCAmelCase__						)

							self.assertEqual(processor.feature_extractor.to_json_string()				,       feature_extractor.to_json_string()						)
							self.assertIsInstance(processor.feature_extractor				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							SpeechTaProcessor(tokenizer=self.get_tokenizer()				,       feature_extractor=self.get_feature_extractor()						)
							processor.save_pretrained(self.tmpdirname						)

							__lowercase			      =							self.get_tokenizer(bos_token='''(BOS)'''				,       eos_token='''(EOS)'''						)
							__lowercase			      =							self.get_feature_extractor(do_normalize=lowerCAmelCase__				,       padding_value=1.0						)

							__lowercase			      =							SpeechTaProcessor.from_pretrained(
							    self.tmpdirname				,       bos_token='''(BOS)'''				,       eos_token='''(EOS)'''				,       do_normalize=lowerCAmelCase__				,       padding_value=1.0						)

							self.assertEqual(processor.tokenizer.get_vocab()				,       tokenizer_add_kwargs.get_vocab()						)
							self.assertIsInstance(processor.tokenizer				,       lowerCAmelCase__						)

							self.assertEqual(processor.feature_extractor.to_json_string()				,       feature_extractor_add_kwargs.to_json_string()						)
							self.assertIsInstance(processor.feature_extractor				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							self.get_feature_extractor()
							__lowercase			      =							self.get_tokenizer()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							__lowercase			      =							floats_list((3, 10_00)						)

							__lowercase			      =							feature_extractor(audio=lowerCAmelCase__				,       return_tensors='''np'''						)
							__lowercase			      =							processor(audio=lowerCAmelCase__				,       return_tensors='''np'''						)

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()				,       input_processor[key].sum()				,       delta=1E-2						)





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

							'''simple docstring'''


							__lowercase			      =							self.get_feature_extractor()
							__lowercase			      =							self.get_tokenizer()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							__lowercase			      =							floats_list((3, 10_00)						)

							__lowercase			      =							feature_extractor(audio_target=lowerCAmelCase__				,       return_tensors='''np'''						)
							__lowercase			      =							processor(audio_target=lowerCAmelCase__				,       return_tensors='''np'''						)

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()				,       input_processor[key].sum()				,       delta=1E-2						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.get_feature_extractor()
							__lowercase			      =							self.get_tokenizer()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							__lowercase			      =							'''This is a test string'''

							__lowercase			      =							processor(text=lowerCAmelCase__						)
							__lowercase			      =							tokenizer(lowerCAmelCase__						)

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				,       encoded_processor[key]						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							self.get_feature_extractor()
							__lowercase			      =							self.get_tokenizer()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							__lowercase			      =							'''This is a test string'''

							__lowercase			      =							processor(text_target=lowerCAmelCase__						)
							__lowercase			      =							tokenizer(lowerCAmelCase__						)

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				,       encoded_processor[key]						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							self.get_feature_extractor()
							__lowercase			      =							self.get_tokenizer()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							__lowercase			      =							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							__lowercase			      =							processor.batch_decode(lowerCAmelCase__						)
							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__						)

							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							self.get_feature_extractor()
							__lowercase			      =							self.get_tokenizer()

							__lowercase			      =							SpeechTaProcessor(tokenizer=lowerCAmelCase__				,       feature_extractor=lowerCAmelCase__						)

							self.assertListEqual(
							    processor.model_input_names				,       feature_extractor.model_input_names				,       msg='''`processor` and `feature_extractor` model input names do not match'''				,       )
import timeit




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):  # Initializes a matrix identical to the one provided.
											__lowercase			      =							arg
											__lowercase			      =							len(lowerCAmelCase__						)
							else:  # Initializes a square matrix of the given size and set values to zero.
											__lowercase			      =							arg
											__lowercase			      =							[[0 for _ in range(self.n						)] for _ in range(self.n						)]





			def __mul__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							Matrix(self.n						)
							for i in range(self.n						):
											for j in range(self.n						):
															for k in range(self.n						):
																			matrix.t[i][j] += self.t[i][k] * b.t[k][j]
							return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							Matrix([[1, 0], [0, 1]]							)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa
				__lowercase			      =							Matrix([[1, 1], [1, 0]]							)
				__lowercase			      =							modular_exponentiation(lowercase							,      n - 2							)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa

				__lowercase			      =							fa
				__lowercase			      =							fa
				n -= 2

				while n > 0:
								__lowercase      ,					__lowercase			      =							fn_a + fn_a, fn_a
								n -= 1

				return fn_a





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
				__lowercase			      =							'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print('''With matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import simple_fibonacci
'''
				__lowercase			      =							'''simple_fibonacci(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print(
				    '''Without matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
		main()