





'''simple docstring'''

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


lowercase_         =			get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")


@require_torch
class  a_					(    unittest.TestCase	):

    '''simple docstring'''


    def       snake_case_(		self	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

        _SCREAMING_SNAKE_CASE     =      SpeechTaTokenizer(A	)
        tokenizer.save_pretrained(self.tmpdirname	)

        _SCREAMING_SNAKE_CASE     =      {
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 1_6000,
            """do_normalize""": False,
            """num_mel_bins""": 80,
            """hop_length""": 16,
            """win_length""": 64,
            """win_function""": """hann_window""",
            """fmin""": 80,
            """fmax""": 7600,
            """mel_floor""": 1e-10,
            """reduction_factor""": 2,
            """return_attention_mask""": True,
        }

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


    def       snake_case_(		self			,		**A	)		->       Union[str, Any]:
        return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,		**A	)


    def       snake_case_(		self			,		**A	)		->       Optional[Any]:
        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,		**A	)


    def       snake_case_(		self	)		->       Tuple:
        shutil.rmtree(self.tmpdirname	)


    def       snake_case_(		self	)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=A			,		feature_extractor=A	)

        processor.save_pretrained(self.tmpdirname	)
        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor.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]:
        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=self.get_tokenizer()			,		feature_extractor=self.get_feature_extractor()	)
        processor.save_pretrained(self.tmpdirname	)

        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer(bos_token="""(BOS)"""			,		eos_token="""(EOS)"""	)
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor(do_normalize=A			,		padding_value=1.0	)

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor.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	)		->       Dict:
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=A			,		feature_extractor=A	)

        _SCREAMING_SNAKE_CASE     =      floats_list((3, 1000)	)

        _SCREAMING_SNAKE_CASE     =      feature_extractor(audio=A			,		return_tensors="""np"""	)
        _SCREAMING_SNAKE_CASE     =      processor(audio=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	)		->       int:
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=A			,		feature_extractor=A	)

        _SCREAMING_SNAKE_CASE     =      floats_list((3, 1000)	)

        _SCREAMING_SNAKE_CASE     =      feature_extractor(audio_target=A			,		return_tensors="""np"""	)
        _SCREAMING_SNAKE_CASE     =      processor(audio_target=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	)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=A			,		feature_extractor=A	)

        _SCREAMING_SNAKE_CASE     =      """This is a test string"""

        _SCREAMING_SNAKE_CASE     =      processor(text=A	)
        _SCREAMING_SNAKE_CASE     =      tokenizer(A	)

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


    def       snake_case_(		self	)		->       Union[str, Any]:
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=A			,		feature_extractor=A	)

        _SCREAMING_SNAKE_CASE     =      """This is a test string"""

        _SCREAMING_SNAKE_CASE     =      processor(text_target=A	)
        _SCREAMING_SNAKE_CASE     =      tokenizer(A	)

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


    def       snake_case_(		self	)		->       Optional[Any]:
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(tokenizer=A			,		feature_extractor=A	)

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

        _SCREAMING_SNAKE_CASE     =      processor.batch_decode(A	)
        _SCREAMING_SNAKE_CASE     =      tokenizer.batch_decode(A	)

        self.assertListEqual(A			,		A	)





    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.get_feature_extractor()
        _SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

        _SCREAMING_SNAKE_CASE     =      SpeechTaProcessor(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'''

def lowerCamelCase						(					__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int   )						->int:
    return len_board * len_board_column * row + column





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   int ,	__lowerCamelCase			:   set[int] ,	)						->bool:

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__lowerCamelCase   ) - 1:
        return True

    _SCREAMING_SNAKE_CASE     =      [(0, 1), (0, -1), (-1, 0), (1, 0)]
    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for direction in traverts_directions:
        _SCREAMING_SNAKE_CASE     =      row + direction[0]
        _SCREAMING_SNAKE_CASE     =      column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        _SCREAMING_SNAKE_CASE     =      get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )
        if key in visited_points_set:
            continue

        visited_points_set.add(__lowerCamelCase   )
        if exits_word(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	word_index + 1 ,	__lowerCamelCase   ):
            return True

        visited_points_set.remove(__lowerCamelCase   )

    return False





def lowerCamelCase						(					__lowerCamelCase			:   list[list[str]] ,	__lowerCamelCase			:   str   )						->bool:
    _SCREAMING_SNAKE_CASE     =      (
        """The board should be a non empty matrix of single chars strings."""
    )

    _SCREAMING_SNAKE_CASE     =      len(__lowerCamelCase   )
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(__lowerCamelCase   )

    for row in board:
        if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
            raise ValueError(__lowerCamelCase   )

        for item in row:
            if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) != 1:
                raise ValueError(__lowerCamelCase   )

    # Validate word
    if not isinstance(__lowerCamelCase ,	__lowerCamelCase   ) or len(__lowerCamelCase   ) == 0:
        raise ValueError(
            """The word parameter should be a string of length greater than 0."""   )

    _SCREAMING_SNAKE_CASE     =      len(board[0]   )
    for i in range(__lowerCamelCase   ):
        for j in range(__lowerCamelCase   ):
            if exits_word(
                __lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	0 ,	{get_point_key(__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase ,	__lowerCamelCase   )}   ):
                return True

    return False


if __name__ == "__main__":
      import doctest

      doctest.testmod()



