
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 							lowerCamelCase       (		unittest.TestCase					):



  """simple docstring"""





  def 					lowercase_						(					self					):
      A_								=					"<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(" "					)
      A_								=					dict(zip(__UpperCAmelCase						,    range(len(__UpperCAmelCase					)					)					)					)

      A_								=					{
          "pad_token": "<pad>",
          "unk_token": "<unk>",
          "bos_token": "<s>",
          "eos_token": "</s>",
      }
      A_								=					{
          "feature_size": 1,
          "padding_value": 0.0,
          "sampling_rate": 16000,
          "return_attention_mask": False,
          "do_normalize": True,
      }

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

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

  def 					lowercase_						(					self						,    **__UpperCamelCase					):
      A_								=					self.add_kwargs_tokens_map.copy()
      kwargs.update(__UpperCAmelCase					)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,    **__UpperCAmelCase					)

  def 					lowercase_						(					self						,    **__UpperCamelCase					):
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,    **__UpperCAmelCase					)

  def 					lowercase_						(					self					):
      shutil.rmtree(self.tmpdirname					)

  def 					lowercase_						(					self					):
      A_								=					self.get_tokenizer()
      A_								=					self.get_feature_extractor()

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

      processor.save_pretrained(self.tmpdirname					)
      A_								=					WavaVecaProcessor.from_pretrained(self.tmpdirname					)

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

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

  def 					lowercase_						(					self					):
      A_								=					WavaVecaProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()					)
      processor.save_pretrained(self.tmpdirname					)

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

      A_								=					WavaVecaProcessor.from_pretrained(
          self.tmpdirname						,    bos_token="(BOS)"						,    eos_token="(EOS)"						,    do_normalize=__UpperCAmelCase						,    padding_value=1.0					)

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

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

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

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

      A_								=					floats_list((3, 1000)					)

      A_								=					feature_extractor(__UpperCAmelCase						,    return_tensors="np"					)
      A_								=					processor(__UpperCAmelCase						,    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 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

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

      A_								=					"This is a test string"

      A_								=					processor(text=__UpperCAmelCase					)

      A_								=					tokenizer(__UpperCAmelCase					)

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

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

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

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

      A_								=					processor.batch_decode(__UpperCAmelCase					)
      A_								=					tokenizer.batch_decode(__UpperCAmelCase					)

      self.assertListEqual(__UpperCAmelCase						,    __UpperCAmelCase					)

  def 					lowercase_						(					self					):
      A_								=					self.get_feature_extractor()
      A_								=					self.get_tokenizer()

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

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












__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


			global gear_one_pos
			global gear_two_pos
			global gear_three_pos
			UpperCamelCase_     				= gear_one[0]
			gear_one.append(__snake_case					)
			del gear_one[0]
			gear_one_pos += 1
			if gear_one_pos % int(len(__snake_case					)					) == 0:
						UpperCamelCase_     				= gear_two[0]
						gear_two.append(__snake_case					)
						del gear_two[0]
						gear_two_pos += 1
						if gear_two_pos % int(len(__snake_case					)					) == 0:
									UpperCamelCase_     				= gear_three[0]
									gear_three.append(__snake_case					)
									del gear_three[0]
									gear_three_pos += 1




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= alphabets.index(__snake_case					)
			UpperCamelCase_     				= gear_one[target]
			UpperCamelCase_     				= gear_two[target]
			UpperCamelCase_     				= gear_three[target]
			UpperCamelCase_     				= reflector[target]
			UpperCamelCase_     				= gear_three.index(__snake_case					)
			UpperCamelCase_     				= gear_two.index(__snake_case					)
			UpperCamelCase_     				= gear_one.index(__snake_case					)
			code.append(alphabets[target]					)
			rotator()


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[Any]							     =		int(input("""Please set token:(must be only digits)\n"""))
												break
								except Exception as error:
												print(error)
				for _ in range(token):
								rotator()
				for j in decode:
								engine(j)
				print("""\n""" + """""".join(code))
				print(
				    f"""\nYour Token is {token} please write it down.\nIf you want to decode """
				    """this message again you should input same digits as token!"""
				)
