



"""simple docstring"""






def 			_lowercase				(  __snake_case	)						->   int:
			if not numbers:
						return 0

			if not isinstance(__snake_case					,(list, tuple)	) or not all(
			    isinstance(__snake_case					,__snake_case	) for number in numbers	):
						raise ValueError("numbers must be an iterable of integers"	)

			__lowerCAmelCase      :  int												=       numbers[0]

			for i in range(1					,len(__snake_case	)	):
						# update the maximum and minimum subarray products
						__lowerCAmelCase      :  Tuple												=       numbers[i]
						if number < 0:
									__lowerCAmelCase      :  Any												=       min_till_now, max_till_now
						__lowerCAmelCase      :  Optional[int]												=       max(__snake_case					,max_till_now * number	)
						__lowerCAmelCase      :  List[str]												=       min(__snake_case					,min_till_now * number	)

						# update the maximum product found till now
						__lowerCAmelCase      :  Any												=       max(__snake_case					,__snake_case	)

			return max_prod






'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor


@require_vision
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          # fmt: off
          __A						: Optional[int]      =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
          # fmt: on
          __A						: Tuple      =       dict(zip(_UpperCAmelCase						,	range(len(_UpperCAmelCase))))
          __A						: Any      =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          __A						: List[str]      =       {'unk_token': '<unk>'}

          __A						: int      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['vocab_file'])
          __A						: Optional[int]      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file						,	'w'						,	encoding='utf-8') as fp:
               fp.write(json.dumps(_UpperCAmelCase) + '\n')
          with open(self.merges_file						,	'w'						,	encoding='utf-8') as fp:
               fp.write('\n'.join(_UpperCAmelCase))

          __A						: str      =       {
              '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						: Union[str, Any]      =       os.path.join(self.tmpdirname						,	_UpperCAmelCase)
          with open(self.image_processor_file						,	'w'						,	encoding='utf-8') as fp:
               json.dump(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPImageProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       self.get_tokenizer()
          __A						: int      =       self.get_rust_tokenizer()
          __A						: Any      =       self.get_image_processor()

          __A						: Union[str, Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          processor_slow.save_pretrained(self.tmpdirname)
          __A						: Dict      =       CLIPProcessor.from_pretrained(self.tmpdirname						,	use_fast=_UpperCAmelCase)

          __A						: Union[str, Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          processor_fast.save_pretrained(self.tmpdirname)
          __A						: Union[str, Any]      =       CLIPProcessor.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						,	_UpperCAmelCase)
          self.assertIsInstance(processor_fast.tokenizer						,	_UpperCAmelCase)

          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						,	_UpperCAmelCase)
          self.assertIsInstance(processor_fast.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       CLIPProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						: Dict      =       self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)')
          __A						: Any      =       self.get_image_processor(do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          __A						: Tuple      =       CLIPProcessor.from_pretrained(
              self.tmpdirname						,	bos_token='(BOS)'						,	eos_token='(EOS)'						,	do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.get_image_processor()
          __A						: List[Any]      =       self.get_tokenizer()

          __A						: List[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

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

          __A						: Dict      =       image_processor(_UpperCAmelCase						,	return_tensors='np')
          __A						: str      =       processor(images=_UpperCAmelCase						,	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_image_processor()
          __A						: List[str]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       'lower newer'

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

          __A						: Optional[int]      =       tokenizer(_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.get_image_processor()
          __A						: Optional[int]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: int      =       'lower newer'
          __A						: Any      =       self.prepare_image_inputs()

          __A						: Tuple      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	['input_ids', 'attention_mask', 'pixel_values'])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCAmelCase):
               processor()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						: str      =       processor.batch_decode(_UpperCAmelCase)
          __A						: Optional[Any]      =       tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.get_image_processor()
          __A						: int      =       self.get_tokenizer()

          __A						: Any      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Any      =       'lower newer'
          __A						: Any      =       self.prepare_image_inputs()

          __A						: Any      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

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