

import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list







class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    "<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z".split(" "						)
      SCREAMING_SNAKE_CASE__         =    dict(zip(A_  ,	range(len(A_						)						)						)						)

      SCREAMING_SNAKE_CASE__         =    {
          "pad_token": "<pad>",
          "unk_token": "<unk>",
          "bos_token": "<s>",
          "eos_token": "</s>",
      }
      SCREAMING_SNAKE_CASE__         =    {
          "feature_size": 1,
          "padding_value": 0.0,
          "sampling_rate": 1_6000,
          "return_attention_mask": False,
          "do_normalize": True,
      }

      SCREAMING_SNAKE_CASE__         =    tempfile.mkdtemp()
      SCREAMING_SNAKE_CASE__         =    os.path.join(self.tmpdirname  ,	VOCAB_FILES_NAMES["vocab_file"]						)
      SCREAMING_SNAKE_CASE__         =    os.path.join(self.tmpdirname  ,	A_						)
      with open(self.vocab_file  ,	"w"  ,	encoding="utf-8"						) as fp:
          fp.write(json.dumps(A_						) + "\n"						)

      with open(self.feature_extraction_file  ,	"w"  ,	encoding="utf-8"						) as fp:
          fp.write(json.dumps(A_						) + "\n"						)



  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	**A_     :					Optional[Any]						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    self.add_kwargs_tokens_map.copy()
      kwargs.update(A_						)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,	**A_						)



  def   _UpperCamelCase     (						self     :					Optional[int]  ,	**A_     :					Optional[int]						)       ->			List[str]:
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,	**A_						)



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Dict:
      shutil.rmtree(self.tmpdirname						)



  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()

      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor(tokenizer=A_  ,	feature_extractor=A_						)

      processor.save_pretrained(self.tmpdirname						)
      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor.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   _UpperCamelCase     (						self     :					str						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor(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__         =    WavaVecaProcessor.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   _UpperCamelCase     (						self     :					List[str]						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor(tokenizer=A_  ,	feature_extractor=A_						)

      SCREAMING_SNAKE_CASE__         =    floats_list((3, 1000)						)

      SCREAMING_SNAKE_CASE__         =    feature_extractor(A_  ,	return_tensors="np"						)
      SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor(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   _UpperCamelCase     (						self     :					str						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor(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   _UpperCamelCase     (						self     :					Dict						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__         =    self.get_tokenizer()

      SCREAMING_SNAKE_CASE__         =    WavaVecaProcessor(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"  ,	)





def 				__a							(       __A			,							__A			,							__A			,							__A						):
    return len_board * len_board_column * row + column





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A			,							):

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

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

    SCREAMING_SNAKE_CASE__         =    [(0, 1), (0, -1), (-1, 0), (1, 0)]
    SCREAMING_SNAKE_CASE__         =    len(__A						)
    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(__A			,							__A			,							__A			,							__A						)
        if key in visited_points_set:
            continue

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

        visited_points_set.remove(__A						)

    return False





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    (
        "The board should be a non empty matrix of single chars strings."
    )

    SCREAMING_SNAKE_CASE__         =    len(__A						)
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(__A						)

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

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

    # Validate word
    if not isinstance(__A			,							__A						) or len(__A						) == 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(__A						):
        for j in range(__A						):
            if exits_word(
                __A			,							__A			,							__A			,							__A			,							0			,							{get_point_key(__A			,							__A			,							__A			,							__A						)}						):
                return True

    return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()


