



"""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				):



  def 		lowerCamelCase__				(  self       :    Union[str, Any]						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=tempfile.mkdtemp()

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=[
         """[UNK]""",
         """[CLS]""",
         """[SEP]""",
         """[PAD]""",
         """[MASK]""",
         """的""",
         """价""",
         """格""",
         """是""",
         """15""",
         """便""",
         """alex""",
         """##andra""",
         """，""",
         """。""",
         """-""",
         """t""",
         """shirt""",
     ]
     SCREAMING_SNAKE_CASE_:       List[Any]   	=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_:       Dict   	={
         """do_resize""": True,
         """size""": {"""height""": 224, """width""": 224},
         """do_center_crop""": True,
         """crop_size""": {"""height""": 18, """width""": 18},
         """do_normalize""": True,
         """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
         """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
         """do_convert_rgb""": True,
     }
     SCREAMING_SNAKE_CASE_:       Tuple   	=os.path.join(self.tmpdirname				,						lowerCamelCase_						)
     with open(self.image_processor_file				,						"""w"""				,						encoding="""utf-8"""						) as fp:
        json.dump(lowerCamelCase_				,						lowerCamelCase_						)



  def 		lowerCamelCase__				(  self       :    Dict				,						**lowerCAmelCase       :    int						)   ->    Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    Union[str, Any]				,						**lowerCAmelCase       :    Optional[int]						)   ->    Optional[Any]:


     '''simple docstring'''




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



  def 		lowerCamelCase__				(  self       :    Union[str, Any]				,						**lowerCAmelCase       :    List[Any]						)   ->    List[str]:


     '''simple docstring'''




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



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


     '''simple docstring'''




     shutil.rmtree(self.tmpdirname						)



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


     '''simple docstring'''




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

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

     return image_inputs



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=self.get_tokenizer()
     SCREAMING_SNAKE_CASE_:       str   	=self.get_rust_tokenizer()
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.get_image_processor()

     SCREAMING_SNAKE_CASE_:       List[str]   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)
     processor_slow.save_pretrained(self.tmpdirname						)
     SCREAMING_SNAKE_CASE_:       int   	=ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,						use_fast=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       List[Any]   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)
     processor_fast.save_pretrained(self.tmpdirname						)
     SCREAMING_SNAKE_CASE_:       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				,						lowerCamelCase_						)
     self.assertIsInstance(processor_fast.tokenizer				,						lowerCamelCase_						)

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



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,						image_processor=self.get_image_processor()						)
     processor.save_pretrained(self.tmpdirname						)

     SCREAMING_SNAKE_CASE_:       str   	=self.get_tokenizer(cls_token="""(CLS)"""				,						sep_token="""(SEP)"""						)
     SCREAMING_SNAKE_CASE_:       List[str]   	=self.get_image_processor(do_normalize=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=ChineseCLIPProcessor.from_pretrained(
         self.tmpdirname				,						cls_token="""(CLS)"""				,						sep_token="""(SEP)"""				,						do_normalize=lowerCamelCase_						)

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

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



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=self.get_image_processor()
     SCREAMING_SNAKE_CASE_:       List[Any]   	=self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:       List[str]   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       Tuple   	=self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=image_processor(lowerCamelCase_				,						return_tensors="""np"""						)
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=processor(images=lowerCamelCase_				,						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       :    Union[str, Any]						)   ->    List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[str]   	=self.get_image_processor()
     SCREAMING_SNAKE_CASE_:       str   	=self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:       int   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       Tuple   	="""Alexandra，T-shirt的价格是15便士。"""

     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=processor(text=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       str   	=tokenizer(lowerCamelCase_						)

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



  def 		lowerCamelCase__				(  self       :    Optional[Any]						)   ->    Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       int   	=self.get_image_processor()
     SCREAMING_SNAKE_CASE_:       Tuple   	=self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:       Tuple   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       Tuple   	="""Alexandra，T-shirt的价格是15便士。"""
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE_:       Dict   	=processor(text=lowerCamelCase_				,						images=lowerCamelCase_						)

     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(lowerCamelCase_						):
        processor()



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=self.get_image_processor()
     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:       int   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       List[str]   	=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     SCREAMING_SNAKE_CASE_:       Tuple   	=processor.batch_decode(lowerCamelCase_						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=tokenizer.batch_decode(lowerCamelCase_						)

     self.assertListEqual(lowerCamelCase_				,						lowerCamelCase_						)





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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	=self.get_image_processor()
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=self.get_tokenizer()

     SCREAMING_SNAKE_CASE_:       List[str]   	=ChineseCLIPProcessor(tokenizer=lowerCamelCase_				,						image_processor=lowerCamelCase_						)

     SCREAMING_SNAKE_CASE_:       Tuple   	="""Alexandra，T-shirt的价格是15便士。"""
     SCREAMING_SNAKE_CASE_:       int   	=self.prepare_image_inputs()

     SCREAMING_SNAKE_CASE_:       Optional[int]   	=processor(text=lowerCamelCase_				,						images=lowerCamelCase_						)

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








def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					if number > 0:
										raise ValueError("input must be a negative integer"       )
					a					     =   len(bin(A       )[3:]       )
					a					     =   bin(abs(A       ) - (1 << binary_number_length)       )[3:]
					a					     =   (
					    (
					        "1"
					        + "0" * (binary_number_length - len(A       ))
					        + twos_complement_number
					    )
					    if number < 0
					    else "0"
					)
					return "0b" + twos_complement_number


if __name__ == "__main__":
				import doctest

				doctest.testmod()
