



'''simple docstring'''






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


A_				:					Optional[int]							=      get_tests_dir("""fixtures/test_sentencepiece.model""")



@require_torch
@require_torchaudio
@require_sentencepiece
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		str     	=					tempfile.mkdtemp()

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

        _UpperCAmelCase    :		Dict     	=					SpeechaTextTokenizer.from_pretrained(self.tmpdirname				)
        tokenizer.save_pretrained(self.tmpdirname				)

        _UpperCAmelCase    :		List[Any]     	=					{
            """feature_size""": 24,
            """num_mel_bins""": 24,
            """padding_value""": 0.0,
            """sampling_rate""": 16_000,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }
        save_json(a_    ,save_dir / FEATURE_EXTRACTOR_NAME				)







 def _snake_case					(				self    ,**a_				)						->  str:
        return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,**a_				)







 def _snake_case					(				self    ,**a_				)						->  Union[str, Any]:
        return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,**a_				)







 def _snake_case					(				self				)						->  Union[str, Any]:
        shutil.rmtree(self.tmpdirname				)







 def _snake_case					(				self				)						->  str:
        _UpperCAmelCase    :		Optional[int]     	=					self.get_tokenizer()
        _UpperCAmelCase    :		Tuple     	=					self.get_feature_extractor()

        _UpperCAmelCase    :		Dict     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        processor.save_pretrained(self.tmpdirname				)
        _UpperCAmelCase    :		Tuple     	=					SpeechaTextProcessor.from_pretrained(self.tmpdirname				)

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

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







 def _snake_case					(				self				)						->  Union[str, Any]:
        _UpperCAmelCase    :		List[str]     	=					SpeechaTextProcessor(
            tokenizer=self.get_tokenizer()    ,feature_extractor=self.get_feature_extractor()				)
        processor.save_pretrained(self.tmpdirname				)

        _UpperCAmelCase    :		Optional[int]     	=					self.get_tokenizer(bos_token="""(BOS)"""    ,eos_token="""(EOS)"""				)
        _UpperCAmelCase    :		Tuple     	=					self.get_feature_extractor(do_normalize=a_    ,padding_value=1.0				)

        _UpperCAmelCase    :		List[str]     	=					SpeechaTextProcessor.from_pretrained(
            self.tmpdirname    ,bos_token="""(BOS)"""    ,eos_token="""(EOS)"""    ,do_normalize=a_    ,padding_value=1.0				)

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

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







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Union[str, Any]     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		Optional[Any]     	=					self.get_tokenizer()

        _UpperCAmelCase    :		Optional[int]     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					floats_list((3, 1_000)				)

        _UpperCAmelCase    :		Any     	=					feature_extractor(a_    ,return_tensors="""np"""				)
        _UpperCAmelCase    :		Dict     	=					processor(a_    ,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				)						->  str:
        _UpperCAmelCase    :		Dict     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		str     	=					self.get_tokenizer()

        _UpperCAmelCase    :		int     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        _UpperCAmelCase    :		Dict     	=					"""This is a test string"""

        _UpperCAmelCase    :		Tuple     	=					processor(text=a_				)

        _UpperCAmelCase    :		List[Any]     	=					tokenizer(a_				)

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







 def _snake_case					(				self				)						->  Optional[Any]:
        _UpperCAmelCase    :		Tuple     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		Dict     	=					self.get_tokenizer()

        _UpperCAmelCase    :		int     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

        _UpperCAmelCase    :		Union[str, Any]     	=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase    :		str     	=					processor.batch_decode(a_				)
        _UpperCAmelCase    :		str     	=					tokenizer.batch_decode(a_				)

        self.assertListEqual(a_    ,a_				)







 def _snake_case					(				self				)						->  List[Any]:
        _UpperCAmelCase    :		List[Any]     	=					self.get_feature_extractor()
        _UpperCAmelCase    :		Any     	=					self.get_tokenizer()

        _UpperCAmelCase    :		int     	=					SpeechaTextProcessor(tokenizer=a_    ,feature_extractor=a_				)

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





'''simple docstring'''







import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

_snake_case					: List[str]     			=					[[12, 10], [3, 9]]
_snake_case					: Any     			=					[[3, 4], [7, 4]]
_snake_case					: List[str]     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
_snake_case					: Any     			=					[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
_snake_case					: Optional[Any]     			=					[1]
_snake_case					: str     			=					[2]

_snake_case					: Optional[int]     			=					logging.getLogger()
_snake_case					: Optional[Any]     			=					logging.DEBUG
_snake_case					: Tuple     			=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} returned integer'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_addition.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) + np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_addition.__name__} with different matrix dims'	)
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Optional[Any]						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} returned integer'	)
																		matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_subtraction.__name__} with same matrix dims'	)
												_SCREAMING_SNAKE_CASE					=			(np.array(_SCREAMING_SNAKE_CASE	) - np.array(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_subtraction.__name__} with different matrix dims'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ("mat1", "mat2")						,      [(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: List[str]	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2) or (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												logger.info(F'\n\t{test_multiplication.__name__} returned integer'	)
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		matop.add(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						elif (np.array(_SCREAMING_SNAKE_CASE	)).shape == (np.array(_SCREAMING_SNAKE_CASE	)).shape:
												logger.info(F'\n\t{test_multiplication.__name__} meets dim requirements'	)
												_SCREAMING_SNAKE_CASE					=			(np.matmul(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.multiply(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
												assert theo == act
						else:
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(
																		    F'\n\t{test_multiplication.__name__} does not meet dim requirements'	)
																		assert matop.subtract(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(3.5 * np.array(_SCREAMING_SNAKE_CASE	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.scalar_multiply(_SCREAMING_SNAKE_CASE						,      3.5	)
						assert theo == act



@pytest.mark.mat_ops
def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			(np.identity(5	)).tolist()
						_SCREAMING_SNAKE_CASE					=			matop.identity(5	)
						assert theo == act




@pytest.mark.mat_ops
@pytest.mark.parametrize("mat"						,      [mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Tuple	):
						if (np.array(_SCREAMING_SNAKE_CASE	)).shape < (2, 2):
												with pytest.raises(_SCREAMING_SNAKE_CASE	):
																		logger.info(F'\n\t{test_transpose.__name__} returned integer'	)
																		matop.transpose(_SCREAMING_SNAKE_CASE	)
						else:
												_SCREAMING_SNAKE_CASE					=			(np.transpose(_SCREAMING_SNAKE_CASE	)).tolist()
												_SCREAMING_SNAKE_CASE					=			matop.transpose(_SCREAMING_SNAKE_CASE						,      return_map=_SCREAMING_SNAKE_CASE	)
												assert theo == act