

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    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    def    snake_case_       (   self):
         lowercase__					:     Union[str, Any]   = '<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(' ')
         lowercase__					:     Dict   = dict(zip(a		,     range(len(a))))

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

         lowercase__					:     Any   = tempfile.mkdtemp()
         lowercase__					:     Tuple   = os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['vocab_file'])
         lowercase__					:     Any   = 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    snake_case_       (   self		,     **a):
         lowercase__					:     Any   = self.add_kwargs_tokens_map.copy()
         kwargs.update(a)
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **a)





    def    snake_case_       (   self		,     **a):
         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,     **a)





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





    def    snake_case_       (   self):
         lowercase__					:     Dict   = self.get_tokenizer()
         lowercase__					:     Optional[int]   = self.get_feature_extractor()

         lowercase__					:     List[str]   = WavaVecaProcessor(tokenizer=a		,     feature_extractor=a)

         processor.save_pretrained(self.tmpdirname)
         lowercase__					:     Optional[int]   = 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    snake_case_       (   self):
         lowercase__					:     Optional[Any]   = WavaVecaProcessor(tokenizer=self.get_tokenizer()		,     feature_extractor=self.get_feature_extractor())
         processor.save_pretrained(self.tmpdirname)

         lowercase__					:     Any   = self.get_tokenizer(bos_token='(BOS)'		,     eos_token='(EOS)')
         lowercase__					:     str   = self.get_feature_extractor(do_normalize=a		,     padding_value=1.0)

         lowercase__					:     Union[str, Any]   = 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    snake_case_       (   self):
         lowercase__					:     int   = self.get_feature_extractor()
         lowercase__					:     Tuple   = self.get_tokenizer()

         lowercase__					:     Tuple   = WavaVecaProcessor(tokenizer=a		,     feature_extractor=a)

         lowercase__					:     Union[str, Any]   = floats_list((3, 1000))

         lowercase__					:     Optional[Any]   = feature_extractor(a		,     return_tensors='np')
         lowercase__					:     Optional[int]   = 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):
         lowercase__					:     Union[str, Any]   = self.get_feature_extractor()
         lowercase__					:     str   = self.get_tokenizer()

         lowercase__					:     Optional[int]   = WavaVecaProcessor(tokenizer=a		,     feature_extractor=a)

         lowercase__					:     int   = 'This is a test string'

         lowercase__					:     List[Any]   = processor(text=a)

         lowercase__					:     Tuple   = tokenizer(a)

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





    def    snake_case_       (   self):
         lowercase__					:     Any   = self.get_feature_extractor()
         lowercase__					:     int   = self.get_tokenizer()

         lowercase__					:     int   = WavaVecaProcessor(tokenizer=a		,     feature_extractor=a)

         lowercase__					:     List[Any]   = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowercase__					:     Optional[Any]   = processor.batch_decode(a)
         lowercase__					:     List[str]   = tokenizer.batch_decode(a)

         self.assertListEqual(a		,     a)





    def    snake_case_       (   self):
         lowercase__					:     Optional[Any]   = self.get_feature_extractor()
         lowercase__					:     Optional[int]   = self.get_tokenizer()

         lowercase__					:     int   = 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'		,     )




from __future__ import annotations


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a = 0):
         lowercase__					:     int   = value
         lowercase__					:     Node | None   = None
         lowercase__					:     Node | None   = None



def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):



     '''simple docstring'''





     if treea is None:
          return treea
     if treea is None:
          return treea

     lowercase__					:     Optional[Any]   = treea.value + treea.value
     lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
     lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
     return treea

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):



     '''simple docstring'''





     if root:
          print(root.value    )
          print_preorder(root.left    )
          print_preorder(root.right    )


if __name__ == "__main__":
       snake_case_    						=				Node(1)
       snake_case_    						=				Node(2)
       snake_case_    						=				Node(3)
       snake_case_    						=				Node(4)

       snake_case_    						=				Node(2)
       snake_case_    						=				Node(4)
       snake_case_    						=				Node(6)
       snake_case_    						=				Node(9)
       snake_case_    						=				Node(5)

       print('''Tree1 is: ''')
       print_preorder(treea)
       print('''Tree2 is: ''')
       print_preorder(treea)
       snake_case_    						=				merge_two_binary_trees(treea, treea)
       print('''Merged Tree is: ''')
       print_preorder(merged_tree)

