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 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												__UpperCAmelCase						:			Optional[int]         =   "<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(" "     )
												__UpperCAmelCase						:			Optional[int]         =   dict(zip(__lowerCamelCase	,  range(len(__lowerCamelCase     )     )     )     )

												__UpperCAmelCase						:			Any         =   {
												    "pad_token": "<pad>",
												    "unk_token": "<unk>",
												    "bos_token": "<s>",
												    "eos_token": "</s>",
												}
												__UpperCAmelCase						:			Dict         =   {
												    "feature_size": 1,
												    "padding_value": 0.0,
												    "sampling_rate": 1_60_00,
												    "return_attention_mask": False,
												    "do_normalize": True,
												}

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

												with open(self.feature_extraction_file	,  "w"	,  encoding="utf-8"     ) as fp:
																			fp.write(json.dumps(__lowerCamelCase     ) + "\n"     )
					def    _lowerCamelCase (  self:				Optional[int]	,  **__lowerCamelCase:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			int         =   self.add_kwargs_tokens_map.copy()
												kwargs.update(__lowerCamelCase     )
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				str	,  **__lowerCamelCase:				Optional[int]     )    ->				str:
												return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **__lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												shutil.rmtree(self.tmpdirname     )
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			Optional[Any]         =   self.get_tokenizer()
												__UpperCAmelCase						:			int         =   self.get_feature_extractor()

												__UpperCAmelCase						:			Optional[Any]         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												processor.save_pretrained(self.tmpdirname     )
												__UpperCAmelCase						:			str         =   WavaVecaProcessor.from_pretrained(self.tmpdirname     )

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

												self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor.to_json_string()     )
												self.assertIsInstance(processor.feature_extractor	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				Dict     )    ->				str:
												__UpperCAmelCase						:			List[str]         =   WavaVecaProcessor(tokenizer=self.get_tokenizer()	,  feature_extractor=self.get_feature_extractor()     )
												processor.save_pretrained(self.tmpdirname     )

												__UpperCAmelCase						:			Union[str, Any]         =   self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"     )
												__UpperCAmelCase						:			Any         =   self.get_feature_extractor(do_normalize=__lowerCamelCase	,  padding_value=1.0     )

												__UpperCAmelCase						:			Optional[Any]         =   WavaVecaProcessor.from_pretrained(
												    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=__lowerCamelCase	,  padding_value=1.0     )

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

												self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor_add_kwargs.to_json_string()     )
												self.assertIsInstance(processor.feature_extractor	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]     )    ->				Union[str, Any]:
												__UpperCAmelCase						:			Any         =   self.get_feature_extractor()
												__UpperCAmelCase						:			str         =   self.get_tokenizer()

												__UpperCAmelCase						:			List[Any]         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												__UpperCAmelCase						:			Union[str, Any]         =   floats_list((3, 10_00)     )

												__UpperCAmelCase						:			Dict         =   feature_extractor(__lowerCamelCase	,  return_tensors="np"     )
												__UpperCAmelCase						:			str         =   processor(__lowerCamelCase	,  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    _lowerCamelCase (  self:				Optional[Any]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   self.get_feature_extractor()
												__UpperCAmelCase						:			Tuple         =   self.get_tokenizer()

												__UpperCAmelCase						:			Any         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												__UpperCAmelCase						:			List[Any]         =   "This is a test string"

												__UpperCAmelCase						:			List[Any]         =   processor(text=__lowerCamelCase     )

												__UpperCAmelCase						:			int         =   tokenizer(__lowerCamelCase     )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]     )
					def    _lowerCamelCase (  self:				str     )    ->				Any:
												__UpperCAmelCase						:			Optional[Any]         =   self.get_feature_extractor()
												__UpperCAmelCase						:			Dict         =   self.get_tokenizer()

												__UpperCAmelCase						:			int         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												__UpperCAmelCase						:			Optional[Any]         =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												__UpperCAmelCase						:			int         =   processor.batch_decode(__lowerCamelCase     )
												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.batch_decode(__lowerCamelCase     )

												self.assertListEqual(__lowerCamelCase	,  __lowerCamelCase     )





					def    _lowerCamelCase (  self:				str     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[str]         =   self.get_feature_extractor()
												__UpperCAmelCase						:			Union[str, Any]         =   self.get_tokenizer()

												__UpperCAmelCase						:			Tuple         =   WavaVecaProcessor(tokenizer=__lowerCamelCase	,  feature_extractor=__lowerCamelCase     )

												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 					_snake_case						:
					def __init__(  self:				List[Any]	,  __lowerCamelCase:				int = 0     )    ->				None:
												__UpperCAmelCase						:			Dict         =   value
												__UpperCAmelCase						:			Node | None         =   None
												__UpperCAmelCase						:			Node | None         =   None




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Node | None:
							if treea is None:
														return treea
							if treea is None:
														return treea

							__UpperCAmelCase						:			Tuple         =   treea.value + treea.value
							__UpperCAmelCase						:			Optional[Any]         =   merge_two_binary_trees(treea.left,    treea.left							)
							__UpperCAmelCase						:			List[str]         =   merge_two_binary_trees(treea.right,    treea.right							)
							return treea




def   _UpperCamelCase   (			snake_case__							)						->   None:
							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)




