
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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    tempfile.mkdtemp()

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

								__a										=    {
								    '''do_resize''': True,
								    '''size''': {'''height''': 224, '''width''': 224},
								    '''do_center_crop''': True,
								    '''crop_size''': {'''height''': 18, '''width''': 18},
								    '''do_normalize''': True,
								    '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
								    '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
								    '''do_convert_rgb''': True,
								}
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      List[str]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								shutil.rmtree(self.tmpdirname				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




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

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

								return image_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)
								processor_slow.save_pretrained(self.tmpdirname				)
								__a										=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,     use_fast=_snake_case				)

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)
								processor_fast.save_pretrained(self.tmpdirname				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




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

								__a										=    self.get_tokenizer(cls_token='''(CLS)'''		,     sep_token='''(SEP)'''				)
								__a										=    self.get_image_processor(do_normalize=_snake_case				)

								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

								__a										=    self.prepare_image_inputs()

								__a										=    image_processor(_snake_case		,     return_tensors='''np'''				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor(text=_snake_case				)

								__a										=    tokenizer(_snake_case				)

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor.batch_decode(_snake_case				)
								__a										=    tokenizer.batch_decode(_snake_case				)

								self.assertListEqual(_snake_case		,     _snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.get_image_processor()
								__a										=    self.get_tokenizer()

								__a										=    ChineseCLIPProcessor(tokenizer=_snake_case		,     image_processor=_snake_case				)

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

								__a										=    processor(text=_snake_case		,     images=_snake_case				)

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

def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  list:
				__a										=    len(a__  )
				__a										=    [[0] * n for i in range(a__  )]
				for i in range(a__  ):
								__a										=    y_points[i]

				for i in range(2						,    a__  ):
								for j in range(a__						,    a__  ):
												__a										=    (
												    (xa - x_points[j - i + 1]) * q[j][i - 1]
												    - (xa - x_points[j]) * q[j - 1][i - 1]
												) / (x_points[j] - x_points[j - i + 1])

				return [q[n - 1][n - 1], q]


if __name__ == "__main__":
					import doctest

					doctest.testmod()