


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
   from PIL import Image

   from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast
@require_vision
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Dict:
             a_					:		Optional[Any]							=  tempfile.mkdtemp()

             a_					:		Any							=  BlipImageProcessor()
             a_					:		Tuple							=  GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'     )

             a_					:		Any							=  BlipaProcessor(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             processor.save_pretrained(self.tmpdirname     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							**SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			List[str]:
             return AutoProcessor.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     ).tokenizer
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     ,							**SCREAMING_SNAKE_CASE__				:       Dict     )		->			str:
             return AutoProcessor.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     ).image_processor
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Optional[Any]:
             shutil.rmtree(self.tmpdirname     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Dict:
             a_					:		Optional[int]							=  [np.random.randint(2_5_5     ,							size=(3, 3_0, 4_0_0)     ,							dtype=np.uinta     )]

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

             return image_inputs
      def        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Any:
             a_					:		Dict							=  BlipaProcessor(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_					:		Optional[Any]							=  self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__     ,							padding_value=1.0     )

             a_					:		Union[str, Any]							=  BlipaProcessor.from_pretrained(
                 self.tmpdirname     ,							bos_token='(BOS)'     ,							eos_token='(EOS)'     ,							do_normalize=SCREAMING_SNAKE_CASE__     ,							padding_value=1.0     )

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

             self.assertEqual(processor.image_processor.to_json_string()     ,							image_processor_add_kwargs.to_json_string()     )
             self.assertIsInstance(processor.image_processor     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Optional[int]:
             a_					:		str							=  self.get_image_processor()
             a_					:		Dict							=  self.get_tokenizer()

             a_					:		Dict							=  BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							image_processor=SCREAMING_SNAKE_CASE__     )

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

             a_					:		int							=  image_processor(SCREAMING_SNAKE_CASE__     ,							return_tensors='np'     )
             a_					:		Union[str, Any]							=  processor(images=SCREAMING_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				:       Dict     )		->			Union[str, Any]:
             a_					:		Optional[Any]							=  self.get_image_processor()
             a_					:		int							=  self.get_tokenizer()

             a_					:		List[Any]							=  BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							image_processor=SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  'lower newer'

             a_					:		Any							=  processor(text=SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  tokenizer(SCREAMING_SNAKE_CASE__     ,							return_token_type_ids=SCREAMING_SNAKE_CASE__     )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]     ,							encoded_processor[key]     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			int:
             a_					:		Union[str, Any]							=  self.get_image_processor()
             a_					:		Tuple							=  self.get_tokenizer()

             a_					:		str							=  BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							image_processor=SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  'lower newer'
             a_					:		Union[str, Any]							=  self.prepare_image_inputs()

             a_					:		Tuple							=  processor(text=SCREAMING_SNAKE_CASE__     ,							images=SCREAMING_SNAKE_CASE__     )

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

             # test if it raises when no input is passed
             with pytest.raises(SCREAMING_SNAKE_CASE__     ):
                    processor()
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Union[str, Any]:
             a_					:		int							=  self.get_image_processor()
             a_					:		int							=  self.get_tokenizer()

             a_					:		int							=  BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							image_processor=SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             a_					:		List[str]							=  processor.batch_decode(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__     )

             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			int:
             a_					:		str							=  self.get_image_processor()
             a_					:		List[Any]							=  self.get_tokenizer()

             a_					:		int							=  BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE__     ,							image_processor=SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  'lower newer'
             a_					:		Optional[Any]							=  self.prepare_image_inputs()

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

             # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
             self.assertListEqual(list(inputs.keys()     )     ,							['pixel_values', 'input_ids', 'attention_mask']     )









'''simple docstring'''

from collections.abc import Sequence





def lowerCamelCase						(     __lowerCamelCase :    Sequence[float]					,       __lowerCamelCase :    bool = False    )						->float:
    if not arr:
        return 0

    _SCREAMING_SNAKE_CASE									=			0 if allow_empty_subarrays else float("""-inf"""    )
    _SCREAMING_SNAKE_CASE									=			0.0
    for num in arr:
        _SCREAMING_SNAKE_CASE									=			max(0 if allow_empty_subarrays else num					,       curr_sum + num    )
        _SCREAMING_SNAKE_CASE									=			max(__lowerCamelCase					,       __lowerCamelCase    )

    return max_sum


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

      testmod()

      lowercase_					= [-2, 1, -3, 4, -1, 2, 1, -5, 4]
      print(f"""{max_subarray_sum(nums) = }""")



