




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 						A(		unittest.TestCase     ):



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_	  =					tempfile.mkdtemp()

       lowerCamelCase_	  =					[
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           '的',
           '价',
           '格',
           '是',
           '15',
           '便',
           'alex',
           '##andra',
           '，',
           '。',
           '-',
           't',
           'shirt',
       ]
       lowerCamelCase_	  =					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]						)						)

       lowerCamelCase_	  =					{
           'do_resize': True,
           'size': {'height': 224, 'width': 224},
           'do_center_crop': True,
           'crop_size': {'height': 18, 'width': 18},
           'do_normalize': True,
           'image_mean': [0.48145466, 0.4578275, 0.40821073],
           'image_std': [0.26862954, 0.26130258, 0.27577711],
           'do_convert_rgb': True,
       }
       lowerCamelCase_	  =					os.path.join(self.tmpdirname		,							A_						)
       with open(self.image_processor_file		,							'w'		,							encoding='utf-8'						) as fp:
             json.dump(A_		,							A_						)

 def        a__				(       self      :						Dict		,							**A_      :						List[str]						)     ->			Any:

       """simple docstring"""






       return BertTokenizer.from_pretrained(self.tmpdirname		,							**A_						)

 def        a__				(       self      :						Union[str, Any]		,							**A_      :						Optional[Any]						)     ->			List[str]:

       """simple docstring"""






       return BertTokenizerFast.from_pretrained(self.tmpdirname		,							**A_						)

 def        a__				(       self      :						Optional[int]		,							**A_      :						Optional[Any]						)     ->			List[Any]:

       """simple docstring"""






       return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,							**A_						)

 def        a__				(       self      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       shutil.rmtree(self.tmpdirname						)

 def        a__				(       self      :						str						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					[np.random.randint(255		,							size=(3, 30, 400)		,							dtype=np.uinta						)]

       lowerCamelCase_	  =					[Image.fromarray(np.moveaxis(A_		,							0		,							-1						)						) for x in image_inputs]

       return image_inputs

 def        a__				(       self      :						List[Any]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_tokenizer()
       lowerCamelCase_	  =					self.get_rust_tokenizer()
       lowerCamelCase_	  =					self.get_image_processor()

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)
       processor_slow.save_pretrained(self.tmpdirname						)
       lowerCamelCase_	  =					ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,							use_fast=A_						)

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)
       processor_fast.save_pretrained(self.tmpdirname						)
       lowerCamelCase_	  =					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        a__				(       self      :						Optional[Any]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,							image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       lowerCamelCase_	  =					self.get_tokenizer(cls_token='(CLS)'		,							sep_token='(SEP)'						)
       lowerCamelCase_	  =					self.get_image_processor(do_normalize=A_						)

       lowerCamelCase_	  =					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        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_image_processor()
       lowerCamelCase_	  =					self.get_tokenizer()

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)

       lowerCamelCase_	  =					self.prepare_image_inputs()

       lowerCamelCase_	  =					image_processor(A_		,							return_tensors='np'						)
       lowerCamelCase_	  =					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        a__				(       self      :						List[str]						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_image_processor()
       lowerCamelCase_	  =					self.get_tokenizer()

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)

       lowerCamelCase_	  =					'Alexandra，T-shirt的价格是15便士。'

       lowerCamelCase_	  =					processor(text=A_						)

       lowerCamelCase_	  =					tokenizer(A_						)

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

 def        a__				(       self      :						str						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_image_processor()
       lowerCamelCase_	  =					self.get_tokenizer()

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)

       lowerCamelCase_	  =					'Alexandra，T-shirt的价格是15便士。'
       lowerCamelCase_	  =					self.prepare_image_inputs()

       lowerCamelCase_	  =					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        a__				(       self      :						int						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_image_processor()
       lowerCamelCase_	  =					self.get_tokenizer()

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)

       lowerCamelCase_	  =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       lowerCamelCase_	  =					processor.batch_decode(A_						)
       lowerCamelCase_	  =					tokenizer.batch_decode(A_						)

       self.assertListEqual(A_		,							A_						)





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

       """simple docstring"""






       lowerCamelCase_	  =					self.get_image_processor()
       lowerCamelCase_	  =					self.get_tokenizer()

       lowerCamelCase_	  =					ChineseCLIPProcessor(tokenizer=A_		,							image_processor=A_						)

       lowerCamelCase_	  =					'Alexandra，T-shirt的价格是15便士。'
       lowerCamelCase_	  =					self.prepare_image_inputs()

       lowerCamelCase_	  =					processor(text=A_		,							images=A_						)

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









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


if __name__ == "__main__":
 print(solution())



