

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       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         lowercase__    :   Any      =       'ZinengTang/tvlt-base'
         lowercase__    :   Optional[Any]      =       tempfile.mkdtemp()





    def       snake_case_				(      self						,   **a):
         return TvltImageProcessor.from_pretrained(self.checkpoint						,   **a)





    def       snake_case_				(      self						,   **a):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint						,   **a)





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





    def       snake_case_				(      self):
         lowercase__    :   int      =       self.get_image_processor()
         lowercase__    :   Dict      =       self.get_feature_extractor()

         lowercase__    :   List[Any]      =       TvltProcessor(image_processor=a						,   feature_extractor=a)
         processor.save_pretrained(self.tmpdirname)
         lowercase__    :   Optional[int]      =       TvltProcessor.from_pretrained(self.tmpdirname)

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





    def       snake_case_				(      self):
         lowercase__    :   Any      =       self.get_image_processor()
         lowercase__    :   str      =       self.get_feature_extractor()

         lowercase__    :   Optional[int]      =       TvltProcessor(image_processor=a						,   feature_extractor=a)

         lowercase__    :   List[Any]      =       np.ones([1_2000])

         lowercase__    :   str      =       feature_extractor(a						,   return_tensors='np')
         lowercase__    :   Union[str, Any]      =       processor(audio=a						,   return_tensors='np')

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





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       self.get_image_processor()
         lowercase__    :   List[Any]      =       self.get_feature_extractor()

         lowercase__    :   List[Any]      =       TvltProcessor(image_processor=a						,   feature_extractor=a)

         lowercase__    :   Tuple      =       np.ones([3, 224, 224])

         lowercase__    :   Optional[int]      =       image_processor(a						,   return_tensors='np')
         lowercase__    :   Union[str, Any]      =       processor(images=a						,   return_tensors='np')

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





    def       snake_case_				(      self):
         lowercase__    :   Any      =       self.get_image_processor()
         lowercase__    :   List[str]      =       self.get_feature_extractor()

         lowercase__    :   str      =       TvltProcessor(image_processor=a						,   feature_extractor=a)

         lowercase__    :   List[Any]      =       np.ones([1_2000])
         lowercase__    :   Optional[int]      =       np.ones([3, 224, 224])

         lowercase__    :   Optional[Any]      =       processor(audio=a						,   images=a)

         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(a):
              processor()





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       self.get_image_processor()
         lowercase__    :   Tuple      =       self.get_feature_extractor()

         lowercase__    :   List[str]      =       TvltProcessor(image_processor=a						,   feature_extractor=a)

         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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    dict	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__      , lowercase__    :   List[str]      =       set(SCREAMING_SNAKE_CASE_			), [start]

     while stack:
          lowercase__    :   Union[str, Any]      =       stack.pop()
          explored.add(SCREAMING_SNAKE_CASE_			)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]			):
               if adj not in explored:
                    stack.append(SCREAMING_SNAKE_CASE_			)
     return explored


snake_case_			  = {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(depth_first_search(G, '''A'''))

