import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class       snake_case					(      unittest.TestCase						):




  def 	__lowercase(      self     :	int				)->       List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE__				:				List[str]			=		[
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           '的',
           '价',
           '格',
           '是',
           '15',
           '便',
           'alex',
           '##andra',
           '，',
           '。',
           '-',
           't',
           'shirt',
       ]
       SCREAMING_SNAKE_CASE__				:				int			=		os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES['vocab_file']				)
       with open(self.vocab_file     , 'w'     , encoding='utf-8'				) as vocab_writer:
            vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		{
           'do_resize': True,
           'size': {'height': 224, 'width': 224},
           'do_center_crop': True,
           'crop_size': {'height': 18, 'width': 18},
           'do_normalize': True,
           'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
           'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
           'do_convert_rgb': True,
       }
       SCREAMING_SNAKE_CASE__				:				List[str]			=		os.path.join(self.tmpdirname     , a_				)
       with open(self.image_processor_file     , 'w'     , encoding='utf-8'				) as fp:
            json.dump(a_     , a_				)




  def 	__lowercase(      self     :	Dict     , **a_     :	int				)->       Optional[int]:




       """simple docstring"""
       return BertTokenizer.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	List[str]     , **a_     :	int				)->       int:




       """simple docstring"""
       return BertTokenizerFast.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	str     , **a_     :	Any				)->       Optional[Any]:




       """simple docstring"""
       return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     , **a_				)




  def 	__lowercase(      self     :	Union[str, Any]				)->       List[Any]:




       """simple docstring"""
       shutil.rmtree(self.tmpdirname				)




  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		[np.random.randint(255     , size=(3, 30, 400)     , dtype=np.uinta				)]

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		[Image.fromarray(np.moveaxis(a_     , 0     , -1				)				) for x in image_inputs]

       return image_inputs




  def 	__lowercase(      self     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.get_tokenizer()
       SCREAMING_SNAKE_CASE__				:				str			=		self.get_rust_tokenizer()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.get_image_processor()

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)
       processor_slow.save_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		ChineseCLIPProcessor.from_pretrained(self.tmpdirname     , use_fast=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)
       processor_fast.save_pretrained(self.tmpdirname				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

       self.assertEqual(processor_slow.tokenizer.get_vocab()     , tokenizer_slow.get_vocab()				)
       self.assertEqual(processor_fast.tokenizer.get_vocab()     , tokenizer_fast.get_vocab()				)
       self.assertEqual(tokenizer_slow.get_vocab()     , tokenizer_fast.get_vocab()				)
       self.assertIsInstance(processor_slow.tokenizer     , a_				)
       self.assertIsInstance(processor_fast.tokenizer     , a_				)

       self.assertEqual(processor_slow.image_processor.to_json_string()     , image_processor.to_json_string()				)
       self.assertEqual(processor_fast.image_processor.to_json_string()     , image_processor.to_json_string()				)
       self.assertIsInstance(processor_slow.image_processor     , a_				)
       self.assertIsInstance(processor_fast.image_processor     , a_				)




  def 	__lowercase(      self     :	str				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     , image_processor=self.get_image_processor()				)
       processor.save_pretrained(self.tmpdirname				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_tokenizer(cls_token='(CLS)'     , sep_token='(SEP)'				)
       SCREAMING_SNAKE_CASE__				:				str			=		self.get_image_processor(do_normalize=a_				)

       SCREAMING_SNAKE_CASE__				:				int			=		ChineseCLIPProcessor.from_pretrained(
           self.tmpdirname     , cls_token='(CLS)'     , sep_token='(SEP)'     , do_normalize=a_				)

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

       self.assertEqual(processor.image_processor.to_json_string()     , image_processor_add_kwargs.to_json_string()				)
       self.assertIsInstance(processor.image_processor     , a_				)




  def 	__lowercase(      self     :	List[str]				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.get_image_processor()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__				:				Dict			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		image_processor(a_     , return_tensors='np'				)
       SCREAMING_SNAKE_CASE__				:				Any			=		processor(images=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 	__lowercase(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.get_image_processor()
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		'Alexandra，T-shirt的价格是15便士。'

       SCREAMING_SNAKE_CASE__				:				int			=		processor(text=a_				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		tokenizer(a_				)

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




  def 	__lowercase(      self     :	Dict				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.get_image_processor()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		'Alexandra，T-shirt的价格是15便士。'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		processor(text=a_     , images=a_				)

       self.assertListEqual(list(inputs.keys()				)     , ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']				)

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




  def 	__lowercase(      self     :	Tuple				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.get_image_processor()
       SCREAMING_SNAKE_CASE__				:				str			=		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__				:				Any			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)

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

       SCREAMING_SNAKE_CASE__				:				Tuple			=		processor.batch_decode(a_				)
       SCREAMING_SNAKE_CASE__				:				str			=		tokenizer.batch_decode(a_				)

       self.assertListEqual(a_     , a_				)




  def 	__lowercase(      self     :	Optional[int]				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.get_image_processor()
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.get_tokenizer()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ChineseCLIPProcessor(tokenizer=a_     , image_processor=a_				)

       SCREAMING_SNAKE_CASE__				:				Any			=		'Alexandra，T-shirt的价格是15便士。'
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.prepare_image_inputs()

       SCREAMING_SNAKE_CASE__				:				int			=		processor(text=a_     , images=a_				)

       self.assertListEqual(list(inputs.keys()				)     , processor.model_input_names				)




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

     for i in range(len(lowercase__ ) ):
          if s[i] in open_brackets:
               stack.append(s[i] )

          elif s[i] in closed_brackets and (
              len(lowercase__ ) == 0 or (len(lowercase__ ) > 0 and open_to_closed[stack.pop()] != s[i])
          ):
               return False

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



