




'''simple docstring'''



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 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							int:
          __UpperCAmelCase			:				Dict													=    tempfile.mkdtemp()

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

          __UpperCAmelCase			:				List[str]													=    {
              '''do_resize''': True,
              '''size''': {'''height''': 224, '''width''': 224},
              '''do_center_crop''': True,
              '''crop_size''': {'''height''': 18, '''width''': 18},
              '''do_normalize''': True,
              '''image_mean''': [0.48_145_466, 0.4_578_275, 0.40_821_073],
              '''image_std''': [0.26_862_954, 0.26_130_258, 0.27_577_711],
              '''do_convert_rgb''': True,
          }
          __UpperCAmelCase			:				List[str]													=    os.path.join(self.tmpdirname							,				snake_case    )
          with open(self.image_processor_file							,				'''w'''							,				encoding='''utf-8'''    ) as fp:
               json.dump(snake_case							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	Any							,				**snake_case  :	List[Any]    )		->							str:
          return BertTokenizer.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				**snake_case  :	Optional[Any]    )		->							int:
          return BertTokenizerFast.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	str							,				**snake_case  :	List[str]    )		->							int:
          return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							Union[str, Any]:
          shutil.rmtree(self.tmpdirname    )

     def 				lowerCamelCase__				(       self  :	int    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[str]													=    [np.random.randint(255							,				size=(3, 30, 400)							,				dtype=np.uinta    )]

          __UpperCAmelCase			:				Union[str, Any]													=    [Image.fromarray(np.moveaxis(snake_case							,				0							,				-1    )    ) for x in image_inputs]

          return image_inputs

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				str													=    self.get_tokenizer()
          __UpperCAmelCase			:				Tuple													=    self.get_rust_tokenizer()
          __UpperCAmelCase			:				List[Any]													=    self.get_image_processor()

          __UpperCAmelCase			:				Optional[Any]													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )
          processor_slow.save_pretrained(self.tmpdirname    )
          __UpperCAmelCase			:				Dict													=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname							,				use_fast=snake_case    )

          __UpperCAmelCase			:				Tuple													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )
          processor_fast.save_pretrained(self.tmpdirname    )
          __UpperCAmelCase			:				List[Any]													=    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							,				snake_case    )
          self.assertIsInstance(processor_fast.tokenizer							,				snake_case    )

          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							,				snake_case    )
          self.assertIsInstance(processor_fast.image_processor							,				snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Dict													=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()							,				image_processor=self.get_image_processor()    )
          processor.save_pretrained(self.tmpdirname    )

          __UpperCAmelCase			:				Optional[int]													=    self.get_tokenizer(cls_token='''(CLS)'''							,				sep_token='''(SEP)'''    )
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_image_processor(do_normalize=snake_case    )

          __UpperCAmelCase			:				Dict													=    ChineseCLIPProcessor.from_pretrained(
              self.tmpdirname							,				cls_token='''(CLS)'''							,				sep_token='''(SEP)'''							,				do_normalize=snake_case    )

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

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

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[int]:
          __UpperCAmelCase			:				Optional[int]													=    self.get_image_processor()
          __UpperCAmelCase			:				List[Any]													=    self.get_tokenizer()

          __UpperCAmelCase			:				Any													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				Optional[Any]													=    self.prepare_image_inputs()

          __UpperCAmelCase			:				List[str]													=    image_processor(snake_case							,				return_tensors='''np'''    )
          __UpperCAmelCase			:				Dict													=    processor(images=snake_case							,				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  :	Dict    )		->							Union[str, Any]:
          __UpperCAmelCase			:				List[str]													=    self.get_image_processor()
          __UpperCAmelCase			:				str													=    self.get_tokenizer()

          __UpperCAmelCase			:				str													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				Optional[int]													=    '''Alexandra，T-shirt的价格是15便士。'''

          __UpperCAmelCase			:				Union[str, Any]													=    processor(text=snake_case    )

          __UpperCAmelCase			:				Dict													=    tokenizer(snake_case    )

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

     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Optional[int]:
          __UpperCAmelCase			:				Tuple													=    self.get_image_processor()
          __UpperCAmelCase			:				str													=    self.get_tokenizer()

          __UpperCAmelCase			:				str													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

          __UpperCAmelCase			:				int													=    '''Alexandra，T-shirt的价格是15便士。'''
          __UpperCAmelCase			:				Tuple													=    self.prepare_image_inputs()

          __UpperCAmelCase			:				Union[str, Any]													=    processor(text=snake_case							,				images=snake_case    )

          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(snake_case    ):
               processor()

     def 				lowerCamelCase__				(       self  :	Dict    )		->							str:
          __UpperCAmelCase			:				Tuple													=    self.get_image_processor()
          __UpperCAmelCase			:				Union[str, Any]													=    self.get_tokenizer()

          __UpperCAmelCase			:				List[Any]													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

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

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

          self.assertListEqual(snake_case							,				snake_case    )






     def 				lowerCamelCase__				(       self  :	Any    )		->							Optional[Any]:
          __UpperCAmelCase			:				str													=    self.get_image_processor()
          __UpperCAmelCase			:				Optional[Any]													=    self.get_tokenizer()

          __UpperCAmelCase			:				Optional[Any]													=    ChineseCLIPProcessor(tokenizer=snake_case							,				image_processor=snake_case    )

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

          __UpperCAmelCase			:				Any													=    processor(text=snake_case							,				images=snake_case    )

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



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			list						):





     '''simple docstring'''






     if not nums:
          raise ValueError('''List is empty'''						)
     return sum(_lowercase						) / len(_lowercase						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()