import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
    from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCAmelCase__							= get_tests_dir('fixtures/test_sentencepiece.model')
@require_torch
@require_torchaudio
@require_sentencepiece
class 				a_ (						unittest.TestCase						):






 '''simple docstring'''






 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					tempfile.mkdtemp()

        lowerCAmelCase__     	=					['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
        lowerCAmelCase__     	=					dict(zip(lowercase__			,    range(len(lowercase__))))
        lowerCAmelCase__     	=					Path(self.tmpdirname)
        save_json(lowercase__			,    save_dir / VOCAB_FILES_NAMES['vocab_file'])
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
               copyfile(lowercase__			,    save_dir / VOCAB_FILES_NAMES['spm_file'])

        lowerCAmelCase__     	=					SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
        tokenizer.save_pretrained(self.tmpdirname)

        lowerCAmelCase__     	=					{
            'feature_size': 24,
            'num_mel_bins': 24,
            'padding_value': 0.0,
            'sampling_rate': 16_000,
            'return_attention_mask': False,
            'do_normalize': True,
        }
        save_json(lowercase__			,    save_dir / FEATURE_EXTRACTOR_NAME)




 def 		__snake_case						(			self					:  Any			,    **lowercase__					:  Union[str, Any]):







        '''simple docstring'''



        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname			,    **lowercase__)




 def 		__snake_case						(			self					:  Tuple			,    **lowercase__					:  Tuple):







        '''simple docstring'''



        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname			,    **lowercase__)




 def 		__snake_case						(			self					:  int):







        '''simple docstring'''



        shutil.rmtree(self.tmpdirname)




 def 		__snake_case						(			self					:  Tuple):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.get_tokenizer()
        lowerCAmelCase__     	=					self.get_feature_extractor()

        lowerCAmelCase__     	=					SpeechaTextProcessor(tokenizer=lowercase__			,    feature_extractor=lowercase__)

        processor.save_pretrained(self.tmpdirname)
        lowerCAmelCase__     	=					SpeechaTextProcessor.from_pretrained(self.tmpdirname)

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

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




 def 		__snake_case						(			self					:  str):







        '''simple docstring'''



        lowerCAmelCase__     	=					SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor())
        processor.save_pretrained(self.tmpdirname)

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

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

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

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




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.get_feature_extractor()
        lowerCAmelCase__     	=					self.get_tokenizer()

        lowerCAmelCase__     	=					SpeechaTextProcessor(tokenizer=lowercase__			,    feature_extractor=lowercase__)

        lowerCAmelCase__     	=					floats_list((3, 1_000))

        lowerCAmelCase__     	=					feature_extractor(lowercase__			,    return_tensors='np')
        lowerCAmelCase__     	=					processor(lowercase__			,    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 		__snake_case						(			self					:  Any):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.get_feature_extractor()
        lowerCAmelCase__     	=					self.get_tokenizer()

        lowerCAmelCase__     	=					SpeechaTextProcessor(tokenizer=lowercase__			,    feature_extractor=lowercase__)

        lowerCAmelCase__     	=					'This is a test string'

        lowerCAmelCase__     	=					processor(text=lowercase__)

        lowerCAmelCase__     	=					tokenizer(lowercase__)

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




 def 		__snake_case						(			self					:  List[str]):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.get_feature_extractor()
        lowerCAmelCase__     	=					self.get_tokenizer()

        lowerCAmelCase__     	=					SpeechaTextProcessor(tokenizer=lowercase__			,    feature_extractor=lowercase__)

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

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

        self.assertListEqual(lowercase__			,    lowercase__)




 def 		__snake_case						(			self					:  int):







        '''simple docstring'''



        lowerCAmelCase__     	=					self.get_feature_extractor()
        lowerCAmelCase__     	=					self.get_tokenizer()

        lowerCAmelCase__     	=					SpeechaTextProcessor(tokenizer=lowercase__			,    feature_extractor=lowercase__)

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


'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__      ,    SCREAMING_SNAKE_CASE__ = 100      ,    )   ->				float:


		'''simple docstring'''


		snake_case   :    int			       =		x_start
		snake_case   :    Tuple			       =		fnc(SCREAMING_SNAKE_CASE__       )
		snake_case   :    Dict			       =		0.0

		for _ in range(SCREAMING_SNAKE_CASE__       ):
				# Approximates small segments of curve as linear and solve
				# for trapezoidal area
				snake_case   :    str			       =		(x_end - x_start) / steps + xa
				snake_case   :    str			       =		fnc(SCREAMING_SNAKE_CASE__       )
				area += abs(fxa + fxa       ) * (xa - xa) / 2

				# Increment step
				snake_case   :    Any			       =		xa
				snake_case   :    List[str]			       =		fxa
		return area


if __name__ == "__main__":




						def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				str:


								'''simple docstring'''


								return x**3

						print("f(x) = x^3")
						print("The area between the curve, x = -10, x = 10 and the x axis is:")
						lowercase__		    =	1_0
						while i <= 1_0_0_0_0_0:
												lowercase__		    =	trapezoidal_area(f, -5, 5, i)
												print(f"with {i} steps: {area}")
												i *= 1_0


