import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor





@require_torch
class      A							(				unittest.TestCase     ):


   def 			lowercase_      (self     :						List[Any]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  "ZinengTang/tvlt-base"
         UpperCAmelCase__								=  tempfile.mkdtemp()


   def 			lowercase_      (self     :						Optional[Any]       ,  **__UpperCAmelCase     :						Optional[Any]		)						->				Tuple:
         """simple docstring"""



         return TvltImageProcessor.from_pretrained(self.checkpoint       ,  **_UpperCamelCase		)


   def 			lowercase_      (self     :						Union[str, Any]       ,  **__UpperCAmelCase     :						Optional[int]		)						->				Any:
         """simple docstring"""



         return TvltFeatureExtractor.from_pretrained(self.checkpoint       ,  **_UpperCamelCase		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Union[str, Any]:
         """simple docstring"""



         shutil.rmtree(self.tmpdirname		)


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_feature_extractor()

         UpperCAmelCase__								=  TvltProcessor(image_processor=_UpperCamelCase       ,  feature_extractor=_UpperCamelCase		)
         processor.save_pretrained(self.tmpdirname		)
         UpperCAmelCase__								=  TvltProcessor.from_pretrained(self.tmpdirname		)

         self.assertIsInstance(processor.feature_extractor       ,  _UpperCamelCase		)
         self.assertIsInstance(processor.image_processor       ,  _UpperCamelCase		)


   def 			lowercase_      (self     :						List[Any]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_feature_extractor()

         UpperCAmelCase__								=  TvltProcessor(image_processor=_UpperCamelCase       ,  feature_extractor=_UpperCamelCase		)

         UpperCAmelCase__								=  np.ones([1_2_0_0_0]		)

         UpperCAmelCase__								=  feature_extractor(_UpperCamelCase       ,  return_tensors="np"		)
         UpperCAmelCase__								=  processor(audio=_UpperCamelCase       ,  return_tensors="np"		)

         for key in audio_dict.keys():
               self.assertAlmostEqual(audio_dict[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2		)


   def 			lowercase_      (self     :						str		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_feature_extractor()

         UpperCAmelCase__								=  TvltProcessor(image_processor=_UpperCamelCase       ,  feature_extractor=_UpperCamelCase		)

         UpperCAmelCase__								=  np.ones([3, 2_2_4, 2_2_4]		)

         UpperCAmelCase__								=  image_processor(_UpperCamelCase       ,  return_tensors="np"		)
         UpperCAmelCase__								=  processor(images=_UpperCamelCase       ,  return_tensors="np"		)

         for key in image_dict.keys():
               self.assertAlmostEqual(image_dict[key].sum()       ,  input_processor[key].sum()       ,  delta=1E-2		)


   def 			lowercase_      (self     :						int		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_feature_extractor()

         UpperCAmelCase__								=  TvltProcessor(image_processor=_UpperCamelCase       ,  feature_extractor=_UpperCamelCase		)

         UpperCAmelCase__								=  np.ones([1_2_0_0_0]		)
         UpperCAmelCase__								=  np.ones([3, 2_2_4, 2_2_4]		)

         UpperCAmelCase__								=  processor(audio=_UpperCamelCase       ,  images=_UpperCamelCase		)

         self.assertListEqual(list(inputs.keys()		)       ,  ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

         # test if it raises when no input is passed
         with pytest.raises(_UpperCamelCase		):
               processor()





   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.get_image_processor()
         UpperCAmelCase__								=  self.get_feature_extractor()

         UpperCAmelCase__								=  TvltProcessor(image_processor=_UpperCamelCase       ,  feature_extractor=_UpperCamelCase		)

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










from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





