



"""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  __lowerCAmelCase	(					self		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[Any]											=    tempfile.mkdtemp()

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

         _UpperCAmelCase							:  List[str]											=    {
             '''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''': [0.48_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							:  Dict											=    os.path.join(self.tmpdirname       ,	A		)
         with open(self.image_processor_file       ,	'''w'''       ,	encoding='''utf-8'''		) as fp:
             json.dump(A       ,	A		)





     def  __lowerCAmelCase	(					self       ,	**A		)							->					Any:
         return BertTokenizer.from_pretrained(self.tmpdirname       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	**A		)							->					int:
         return BertTokenizerFast.from_pretrained(self.tmpdirname       ,	**A		)





     def  __lowerCAmelCase	(					self       ,	**A		)							->					str:
         return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname       ,	**A		)





     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         shutil.rmtree(self.tmpdirname		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Optional[int]											=    [np.random.randint(2_5_5       ,	size=(3, 3_0, 4_0_0)       ,	dtype=np.uinta		)]

         _UpperCAmelCase							:  Optional[Any]											=    [Image.fromarray(np.moveaxis(A       ,	0       ,	-1		)		) for x in image_inputs]

         return image_inputs





     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Optional[int]											=    self.get_tokenizer()
         _UpperCAmelCase							:  int											=    self.get_rust_tokenizer()
         _UpperCAmelCase							:  Optional[Any]											=    self.get_image_processor()

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

         _UpperCAmelCase							:  Dict											=    ChineseCLIPProcessor(tokenizer=A       ,	image_processor=A		)
         processor_fast.save_pretrained(self.tmpdirname		)
         _UpperCAmelCase							:  List[str]											=    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  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  Any											=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor()		)
         processor.save_pretrained(self.tmpdirname		)

         _UpperCAmelCase							:  Union[str, Any]											=    self.get_tokenizer(cls_token='''(CLS)'''       ,	sep_token='''(SEP)'''		)
         _UpperCAmelCase							:  str											=    self.get_image_processor(do_normalize=A		)

         _UpperCAmelCase							:  List[Any]											=    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  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  int											=    self.get_image_processor()
         _UpperCAmelCase							:  Optional[int]											=    self.get_tokenizer()

         _UpperCAmelCase							:  Dict											=    ChineseCLIPProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  Dict											=    self.prepare_image_inputs()

         _UpperCAmelCase							:  List[Any]											=    image_processor(A       ,	return_tensors='''np'''		)
         _UpperCAmelCase							:  List[Any]											=    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  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  Any											=    self.get_image_processor()
         _UpperCAmelCase							:  List[str]											=    self.get_tokenizer()

         _UpperCAmelCase							:  Dict											=    ChineseCLIPProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  str											=    '''Alexandra，T-shirt的价格是15便士。'''

         _UpperCAmelCase							:  List[Any]											=    processor(text=A		)

         _UpperCAmelCase							:  Union[str, Any]											=    tokenizer(A		)

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





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  List[Any]											=    self.get_image_processor()
         _UpperCAmelCase							:  List[Any]											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[Any]											=    ChineseCLIPProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  Union[str, Any]											=    '''Alexandra，T-shirt的价格是15便士。'''
         _UpperCAmelCase							:  int											=    self.prepare_image_inputs()

         _UpperCAmelCase							:  Optional[int]											=    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  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  int											=    self.get_image_processor()
         _UpperCAmelCase							:  Optional[Any]											=    self.get_tokenizer()

         _UpperCAmelCase							:  List[str]											=    ChineseCLIPProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  int											=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         _UpperCAmelCase							:  Dict											=    processor.batch_decode(A		)
         _UpperCAmelCase							:  Any											=    tokenizer.batch_decode(A		)

         self.assertListEqual(A       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Any											=    self.get_image_processor()
         _UpperCAmelCase							:  Optional[int]											=    self.get_tokenizer()

         _UpperCAmelCase							:  Tuple											=    ChineseCLIPProcessor(tokenizer=A       ,	image_processor=A		)

         _UpperCAmelCase							:  List[str]											=    '''Alexandra，T-shirt的价格是15便士。'''
         _UpperCAmelCase							:  Dict											=    self.prepare_image_inputs()

         _UpperCAmelCase							:  str											=    processor(text=A       ,	images=A		)

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







"""simple docstring"""
import math

from numpy import inf
from scipy.integrate import quad





def  lowerCamelCase_						(UpperCamelCase__		:		float							):
    if num <= 0:
        raise ValueError('''math domain error'''							)

    return quad(UpperCamelCase__     ,       0     ,       UpperCamelCase__     ,       args=(UpperCamelCase__)							)[0]





def  lowerCamelCase_						(UpperCamelCase__		:		float     ,       UpperCamelCase__		:		float							):
    return math.pow(UpperCamelCase__     ,       z - 1							) * math.exp(-x							)


if __name__ == "__main__":
  from doctest import testmod

  testmod()


