

'''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 IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import AlignProcessor, EfficientNetImageProcessor



@require_vision
class lowerCamelCase					(    unittest.TestCase		):



			'''simple docstring'''






			def lowercase__		( self			:		int					)     ->						Tuple:





									'''simple docstring'''
									A__	:  Dict           =tempfile.mkdtemp()

									A__	:  Union[str, Any]           =[
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									A__	:  Dict           =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__	:  Tuple           ={
									    """do_resize""": True,
									    """size""": 20,
									    """do_center_crop""": True,
									    """crop_size""": 18,
									    """do_normalize""": True,
									    """image_mean""": [0.48145466, 0.4578275, 0.40821073],
									    """image_std""": [0.26862954, 0.26130258, 0.27577711],
									}
									A__	:  int           =os.path.join(self.tmpdirname						,     a_					)
									with open(self.image_processor_file						,     """w"""						,     encoding="""utf-8"""					) as fp:
															json.dump(a_						,     a_					)


			def lowercase__		( self			:		Union[str, Any]						,     **lowerCAmelCase_			:		Union[str, Any]					)     ->						str:





									'''simple docstring'''
									return BertTokenizer.from_pretrained(self.tmpdirname						,     **a_					)


			def lowercase__		( self			:		Tuple						,     **lowerCAmelCase_			:		Tuple					)     ->						List[Any]:





									'''simple docstring'''
									return BertTokenizerFast.from_pretrained(self.tmpdirname						,     **a_					)


			def lowercase__		( self			:		List[Any]						,     **lowerCAmelCase_			:		Union[str, Any]					)     ->						Any:





									'''simple docstring'''
									return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,     **a_					)


			def lowercase__		( self			:		Any					)     ->						List[Any]:





									'''simple docstring'''
									shutil.rmtree(self.tmpdirname					)


			def lowercase__		( self			:		Tuple					)     ->						Tuple:





									'''simple docstring'''
									A__	:  Union[str, Any]           =[np.random.randint(2_55						,     size=(3, 30, 4_00)						,     dtype=np.uinta					)]
									A__	:  List[Any]           =[Image.fromarray(np.moveaxis(a_						,     0						,     -1					)					) for x in image_inputs]
									return image_inputs


			def lowercase__		( self			:		Tuple					)     ->						int:





									'''simple docstring'''
									A__	:  Any           =self.get_tokenizer()
									A__	:  Union[str, Any]           =self.get_rust_tokenizer()
									A__	:  Optional[Any]           =self.get_image_processor()

									A__	:  Tuple           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)
									processor_slow.save_pretrained(self.tmpdirname					)
									A__	:  List[str]           =AlignProcessor.from_pretrained(self.tmpdirname						,     use_fast=a_					)

									A__	:  List[Any]           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)
									processor_fast.save_pretrained(self.tmpdirname					)
									A__	:  Optional[Any]           =AlignProcessor.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 lowercase__		( self			:		List[str]					)     ->						Union[str, Any]:





									'''simple docstring'''
									A__	:  Any           =AlignProcessor(tokenizer=self.get_tokenizer()						,     image_processor=self.get_image_processor()					)
									processor.save_pretrained(self.tmpdirname					)

									A__	:  Union[str, Any]           =self.get_tokenizer(bos_token="""(BOS)"""						,     eos_token="""(EOS)"""					)
									A__	:  Union[str, Any]           =self.get_image_processor(do_normalize=a_						,     padding_value=1.0					)

									A__	:  Optional[Any]           =AlignProcessor.from_pretrained(
									    self.tmpdirname						,     bos_token="""(BOS)"""						,     eos_token="""(EOS)"""						,     do_normalize=a_						,     padding_value=1.0					)

									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 lowercase__		( self			:		str					)     ->						Optional[int]:





									'''simple docstring'''
									A__	:  Dict           =self.get_image_processor()
									A__	:  Tuple           =self.get_tokenizer()

									A__	:  str           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)

									A__	:  List[str]           =self.prepare_image_inputs()

									A__	:  Optional[Any]           =image_processor(a_						,     return_tensors="""np"""					)
									A__	:  Tuple           =processor(images=a_						,     return_tensors="""np"""					)

									for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()						,     input_processor[key].sum()						,     delta=1e-2					)


			def lowercase__		( self			:		str					)     ->						Optional[int]:





									'''simple docstring'''
									A__	:  List[Any]           =self.get_image_processor()
									A__	:  Dict           =self.get_tokenizer()

									A__	:  List[Any]           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)

									A__	:  str           ="""lower newer"""

									A__	:  Union[str, Any]           =processor(text=a_					)

									A__	:  List[str]           =tokenizer(a_						,     padding="""max_length"""						,     max_length=64					)

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


			def lowercase__		( self			:		Union[str, Any]					)     ->						str:





									'''simple docstring'''
									A__	:  int           =self.get_image_processor()
									A__	:  Optional[int]           =self.get_tokenizer()

									A__	:  Optional[int]           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)

									A__	:  Union[str, Any]           ="""lower newer"""
									A__	:  Tuple           =self.prepare_image_inputs()

									A__	:  List[Any]           =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 lowercase__		( self			:		Optional[int]					)     ->						List[Any]:





									'''simple docstring'''
									A__	:  Any           =self.get_image_processor()
									A__	:  Any           =self.get_tokenizer()

									A__	:  Any           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)

									A__	:  Optional[Any]           =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									A__	:  List[Any]           =processor.batch_decode(a_					)
									A__	:  Dict           =tokenizer.batch_decode(a_					)

									self.assertListEqual(a_						,     a_					)



			def lowercase__		( self			:		Optional[Any]					)     ->						Optional[Any]:





									'''simple docstring'''
									A__	:  Optional[Any]           =self.get_image_processor()
									A__	:  int           =self.get_tokenizer()

									A__	:  Tuple           =AlignProcessor(tokenizer=a_						,     image_processor=a_					)

									A__	:  int           ="""lower newer"""
									A__	:  str           =self.prepare_image_inputs()

									A__	:  List[Any]           =processor(text=a_						,     images=a_					)

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






from bisect import bisect
from itertools import accumulate







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  sorted(zip(snake_case__    ,			snake_case__					)    ,			key=lambda snake_case__					: x[0] / x[1]    ,			reverse=snake_case__					)
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__		  =  list(accumulate(snake_case__					)					)
				lowerCAmelCase__		  =  bisect(snake_case__    ,			snake_case__					)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]					) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k]					)
				)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
