"""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    __lowercase     (					unittest.TestCase):







						"""simple docstring"""







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					tempfile.mkdtemp()

											snake_case_					:		Optional[Any] 					=					[
											    """[UNK]""",
											    """[CLS]""",
											    """[SEP]""",
											    """[PAD]""",
											    """[MASK]""",
											    """的""",
											    """价""",
											    """格""",
											    """是""",
											    """15""",
											    """便""",
											    """alex""",
											    """##andra""",
											    """，""",
											    """。""",
											    """-""",
											    """t""",
											    """shirt""",
											]
											snake_case_					:		Optional[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]       )       )

											snake_case_					:		str 					=					{
											    """do_resize""": True,
											    """size""": {"""height""": 2_24, """width""": 2_24},
											    """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,
											}
											snake_case_					:		Tuple 					=					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   ,		**lowercase__       ):
											return BertTokenizer.from_pretrained(self.tmpdirname   ,		**__a       )







						def 						__UpperCamelCase							(self   ,		**lowercase__       ):
											return BertTokenizerFast.from_pretrained(self.tmpdirname   ,		**__a       )







						def 						__UpperCamelCase							(self   ,		**lowercase__       ):
											return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname   ,		**__a       )







						def 						__UpperCamelCase							(self       ):
											shutil.rmtree(self.tmpdirname       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					[np.random.randint(2_55   ,		size=(3, 30, 4_00)   ,		dtype=np.uinta       )]

											snake_case_					:		List[Any] 					=					[Image.fromarray(np.moveaxis(__a   ,		0   ,		-1       )       ) for x in image_inputs]

											return image_inputs







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.get_tokenizer()
											snake_case_					:		Optional[Any] 					=					self.get_rust_tokenizer()
											snake_case_					:		Optional[Any] 					=					self.get_image_processor()

											snake_case_					:		Any 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )
											processor_slow.save_pretrained(self.tmpdirname       )
											snake_case_					:		Tuple 					=					ChineseCLIPProcessor.from_pretrained(self.tmpdirname   ,		use_fast=__a       )

											snake_case_					:		Tuple 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )
											processor_fast.save_pretrained(self.tmpdirname       )
											snake_case_					:		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 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					ChineseCLIPProcessor(tokenizer=self.get_tokenizer()   ,		image_processor=self.get_image_processor()       )
											processor.save_pretrained(self.tmpdirname       )

											snake_case_					:		Union[str, Any] 					=					self.get_tokenizer(cls_token="""(CLS)"""   ,		sep_token="""(SEP)"""       )
											snake_case_					:		int 					=					self.get_image_processor(do_normalize=__a       )

											snake_case_					:		Tuple 					=					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       ):
											snake_case_					:		Optional[int] 					=					self.get_image_processor()
											snake_case_					:		Union[str, Any] 					=					self.get_tokenizer()

											snake_case_					:		Any 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )

											snake_case_					:		Union[str, Any] 					=					self.prepare_image_inputs()

											snake_case_					:		Optional[int] 					=					image_processor(__a   ,		return_tensors="""np"""       )
											snake_case_					:		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 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.get_image_processor()
											snake_case_					:		Any 					=					self.get_tokenizer()

											snake_case_					:		Dict 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )

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

											snake_case_					:		Tuple 					=					processor(text=__a       )

											snake_case_					:		str 					=					tokenizer(__a       )

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







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.get_image_processor()
											snake_case_					:		List[Any] 					=					self.get_tokenizer()

											snake_case_					:		Optional[int] 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )

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

											snake_case_					:		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 						__UpperCamelCase							(self       ):
											snake_case_					:		Dict 					=					self.get_image_processor()
											snake_case_					:		int 					=					self.get_tokenizer()

											snake_case_					:		str 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )

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

											snake_case_					:		List[Any] 					=					processor.batch_decode(__a       )
											snake_case_					:		Union[str, Any] 					=					tokenizer.batch_decode(__a       )

											self.assertListEqual(__a   ,		__a       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					self.get_image_processor()
											snake_case_					:		List[Any] 					=					self.get_tokenizer()

											snake_case_					:		List[str] 					=					ChineseCLIPProcessor(tokenizer=__a   ,		image_processor=__a       )

											snake_case_					:		List[str] 					=					"""Alexandra，T-shirt的价格是15便士。"""
											snake_case_					:		Union[str, Any] 					=					self.prepare_image_inputs()

											snake_case_					:		Dict 					=					processor(text=__a   ,		images=__a       )

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









"""simple docstring"""

from random import randint
from tempfile import TemporaryFile

import numpy as np







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict				,    SCREAMING_SNAKE_CASE__ :  Optional[int]			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					0
					if start < end:
										snake_case_					:		Union[str, Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										snake_case_					:		List[Any] 					=					a[end]
										snake_case_					:		Dict 					=					a[pivot]
										snake_case_					:		Any 					=					temp

										snake_case_					,			snake_case_					:		Dict 					=					_in_place_partition(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    p - 1			)
										count += _in_place_quick_sort(SCREAMING_SNAKE_CASE__				,    p + 1				,    SCREAMING_SNAKE_CASE__			)
					return count







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Dict			):



					"""simple docstring"""





					snake_case_					:		Tuple 					=					0
					snake_case_					:		List[Any] 					=					randint(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
					snake_case_					:		Dict 					=					a[end]
					snake_case_					:		List[Any] 					=					a[pivot]
					snake_case_					:		Optional[Any] 					=					temp
					snake_case_					:		List[str] 					=					start - 1
					for index in range(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										count += 1
										if a[index] < a[end]:  # check if current val is less than pivot value
															snake_case_					:		Any 					=					new_pivot_index + 1
															snake_case_					:		Tuple 					=					a[new_pivot_index]
															snake_case_					:		Optional[int] 					=					a[index]
															snake_case_					:		Tuple 					=					temp

					snake_case_					:		Union[str, Any] 					=					a[new_pivot_index + 1]
					snake_case_					:		Union[str, Any] 					=					a[end]
					snake_case_					:		Union[str, Any] 					=					temp
					return new_pivot_index + 1, count


a_			   =					TemporaryFile()
a_			   =					100  # 1000 elements are to be sorted


a_   ,    a_			   =					0, 1  # mean and standard deviation
a_			   =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)


outfile.seek(0)  # using the same array
a_			   =					np.load(outfile)
a_			   =					len(M) - 1
a_			   =					_in_place_quick_sort(M, 0, r)

print(
    '''No of Comparisons for 100 elements selected from a standard normal distribution'''
    '''is :'''
)


print(z)




