






"""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 	__UpperCAmelCase		(				unittest.TestCase	):


	'''simple docstring'''





	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=tempfile.mkdtemp()

		_SCREAMING_SNAKE_CASE						=[
		    '''[UNK]''',
		    '''[CLS]''',
		    '''[SEP]''',
		    '''[PAD]''',
		    '''[MASK]''',
		    '''的''',
		    '''价''',
		    '''格''',
		    '''是''',
		    '''15''',
		    '''便''',
		    '''alex''',
		    '''##andra''',
		    '''，''',
		    '''。''',
		    '''-''',
		    '''t''',
		    '''shirt''',
		]
		_SCREAMING_SNAKE_CASE						=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						={
		    '''do_resize''': True,
		    '''size''': {'''height''': 2_2_4, '''width''': 2_2_4},
		    '''do_center_crop''': True,
		    '''crop_size''': {'''height''': 1_8, '''width''': 1_8},
		    '''do_normalize''': True,
		    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
		    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
		    '''do_convert_rgb''': True,
		}
		_SCREAMING_SNAKE_CASE						=os.path.join(self.tmpdirname      ,      _A					)
		with open(self.image_processor_file      ,      '''w'''      ,      encoding='''utf-8'''					) as fp:
			json.dump(_A      ,      _A					)


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

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


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

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


	def 				UpperCamelCase_  (     self      ,      **_A					):



		'''simple docstring'''

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		shutil.rmtree(self.tmpdirname					)


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=[np.random.randint(2_5_5      ,      size=(3, 3_0, 4_0_0)      ,      dtype=np.uinta					)]

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

		return image_inputs


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

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

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)
		processor_slow.save_pretrained(self.tmpdirname					)
		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,      use_fast=_A					)

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)
		processor_fast.save_pretrained(self.tmpdirname					)
		_SCREAMING_SNAKE_CASE						=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 				UpperCamelCase_  (     self					):



		'''simple docstring'''

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

		_SCREAMING_SNAKE_CASE						=self.get_tokenizer(cls_token='''(CLS)'''      ,      sep_token='''(SEP)'''					)
		_SCREAMING_SNAKE_CASE						=self.get_image_processor(do_normalize=_A					)

		_SCREAMING_SNAKE_CASE						=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 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)

		_SCREAMING_SNAKE_CASE						=self.prepare_image_inputs()

		_SCREAMING_SNAKE_CASE						=image_processor(_A      ,      return_tensors='''np'''					)
		_SCREAMING_SNAKE_CASE						=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 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)

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

		_SCREAMING_SNAKE_CASE						=processor(text=_A					)

		_SCREAMING_SNAKE_CASE						=tokenizer(_A					)

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


	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)

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

		_SCREAMING_SNAKE_CASE						=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 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)

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

		_SCREAMING_SNAKE_CASE						=processor.batch_decode(_A					)
		_SCREAMING_SNAKE_CASE						=tokenizer.batch_decode(_A					)

		self.assertListEqual(_A      ,      _A					)







	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.get_image_processor()
		_SCREAMING_SNAKE_CASE						=self.get_tokenizer()

		_SCREAMING_SNAKE_CASE						=ChineseCLIPProcessor(tokenizer=_A      ,      image_processor=_A					)

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

		_SCREAMING_SNAKE_CASE						=processor(text=_A      ,      images=_A					)

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







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 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()

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

        _lowercase      			={
            '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,
        }
        _lowercase      			=os.path.join(self.tmpdirname				,  lowerCAmelCase   )
        with open(self.image_processor_file				,  'w'				,  encoding='utf-8'   ) as fp:
              json.dump(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






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




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






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




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






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




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






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

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

        return image_inputs




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






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

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_slow.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,  use_fast=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_fast.save_pretrained(self.tmpdirname   )
        _lowercase      			=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     A__	(							self   )    ->					str:
        '''simple docstring'''






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

        _lowercase      			=self.get_tokenizer(cls_token='(CLS)'				,  sep_token='(SEP)'   )
        _lowercase      			=self.get_image_processor(do_normalize=lowerCAmelCase   )

        _lowercase      			=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     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=image_processor(lowerCAmelCase				,  return_tensors='np'   )
        _lowercase      			=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     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

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

        _lowercase      			=processor(text=lowerCAmelCase   )

        _lowercase      			=tokenizer(lowerCAmelCase   )

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




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

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

        _lowercase      			=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     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

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

        _lowercase      			=processor.batch_decode(lowerCAmelCase   )
        _lowercase      			=tokenizer.batch_decode(lowerCAmelCase   )

        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

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

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

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


