





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 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [
										    '''[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:				Any           =   {
										    '''do_resize''': True,
										    '''size''': {'''height''': 224, '''width''': 224},
										    '''do_center_crop''': True,
										    '''crop_size''': {'''height''': 18, '''width''': 18},
										    '''do_normalize''': True,
										    '''image_mean''': [0.48145466, 0.4578275, 0.40821073],
										    '''image_std''': [0.26862954, 0.26130258, 0.27577711],
										    '''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 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										shutil.rmtree(self.tmpdirname					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   [np.random.randint(255			, size=(3, 30, 400)			, dtype=np.uinta					)]

										__SCREAMING_SNAKE_CASE:				str           =   [Image.fromarray(np.moveaxis(_lowerCAmelCase			, 0			, -1					)					) for x in image_inputs]

										return image_inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   self.get_tokenizer()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_rust_tokenizer()
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_image_processor()

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

										__SCREAMING_SNAKE_CASE:				str           =   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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   ChineseCLIPProcessor(tokenizer=self.get_tokenizer()			, image_processor=self.get_image_processor()					)
										processor.save_pretrained(self.tmpdirname					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_tokenizer(cls_token='''(CLS)'''			, sep_token='''(SEP)'''					)
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_image_processor(do_normalize=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   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 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ChineseCLIPProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   image_processor(_lowerCAmelCase			, return_tensors='''np'''					)
										__SCREAMING_SNAKE_CASE:				Dict           =   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 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_image_processor()
										__SCREAMING_SNAKE_CASE:				str           =   self.get_tokenizer()

										__SCREAMING_SNAKE_CASE:				List[Any]           =   ChineseCLIPProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				List[Any]           =   processor(text=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   tokenizer(_lowerCAmelCase					)

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				int           =   ChineseCLIPProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				List[str]           =   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 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				Optional[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:				Any           =   processor.batch_decode(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer.batch_decode(_lowerCAmelCase					)

										self.assertListEqual(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




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

										__SCREAMING_SNAKE_CASE:				List[Any]           =   ChineseCLIPProcessor(tokenizer=_lowerCAmelCase			, image_processor=_lowerCAmelCase					)

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

										__SCREAMING_SNAKE_CASE:				Any           =   processor(text=_lowerCAmelCase			, images=_lowerCAmelCase					)

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










def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict					)      ->    List[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   [0 for i in range(r + 1					)]
						# nc0 = 1
						__SCREAMING_SNAKE_CASE:				Dict           =   1
						for i in range(1       ,			n + 1					):
												# to compute current row from previous row.
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   min(UpperCamelCase__       ,			UpperCamelCase__					)
												while j > 0:
																		c[j] += c[j - 1]
																		j -= 1
						return c[r]




print(binomial_coefficient(n=10, r=5))



