




"""simple docstring"""





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      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    str):


						"""simple docstring"""




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

						__UpperCAmelCase	: Union[str, Any]		=					BlipImageProcessor()
						__UpperCAmelCase	: Union[str, Any]		=					GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")

						__UpperCAmelCase	: Any		=					BlipaProcessor(UpperCamelCase__      ,  UpperCamelCase__)

						processor.save_pretrained(self.tmpdirname)


			def       a_     (      self      :    List[Any]      ,  **UpperCamelCase_      :    Optional[Any]):


						"""simple docstring"""




						return AutoProcessor.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__).tokenizer


			def       a_     (      self      :    Union[str, Any]      ,  **UpperCamelCase_      :    Any):


						"""simple docstring"""




						return AutoProcessor.from_pretrained(self.tmpdirname      ,  **UpperCamelCase__).image_processor


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						shutil.rmtree(self.tmpdirname)


			def       a_     (      self      :    Dict):


						"""simple docstring"""




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

						__UpperCAmelCase	: Dict		=					[Image.fromarray(np.moveaxis(UpperCamelCase__      ,  0      ,  -1)) for x in image_inputs]

						return image_inputs


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					BlipaProcessor(tokenizer=self.get_tokenizer()      ,  image_processor=self.get_image_processor())
						processor.save_pretrained(self.tmpdirname)

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

						__UpperCAmelCase	: int		=					BlipaProcessor.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       a_     (      self      :    List[Any]):


						"""simple docstring"""




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

						__UpperCAmelCase	: List[Any]		=					BlipaProcessor(tokenizer=UpperCamelCase__      ,  image_processor=UpperCamelCase__)

						__UpperCAmelCase	: Any		=					self.prepare_image_inputs()

						__UpperCAmelCase	: Dict		=					image_processor(UpperCamelCase__      ,  return_tensors="np")
						__UpperCAmelCase	: Tuple		=					processor(images=UpperCamelCase__      ,  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       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					self.get_image_processor()
						__UpperCAmelCase	: Tuple		=					self.get_tokenizer()

						__UpperCAmelCase	: Any		=					BlipaProcessor(tokenizer=UpperCamelCase__      ,  image_processor=UpperCamelCase__)

						__UpperCAmelCase	: List[Any]		=					'''lower newer'''

						__UpperCAmelCase	: Tuple		=					processor(text=UpperCamelCase__)

						__UpperCAmelCase	: Union[str, Any]		=					tokenizer(UpperCamelCase__      ,  return_token_type_ids=UpperCamelCase__)

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


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					self.get_image_processor()
						__UpperCAmelCase	: int		=					self.get_tokenizer()

						__UpperCAmelCase	: List[str]		=					BlipaProcessor(tokenizer=UpperCamelCase__      ,  image_processor=UpperCamelCase__)

						__UpperCAmelCase	: List[Any]		=					'''lower newer'''
						__UpperCAmelCase	: Tuple		=					self.prepare_image_inputs()

						__UpperCAmelCase	: Optional[int]		=					processor(text=UpperCamelCase__      ,  images=UpperCamelCase__)

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

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


			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Union[str, Any]		=					self.get_image_processor()
						__UpperCAmelCase	: Optional[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: Tuple		=					BlipaProcessor(tokenizer=UpperCamelCase__      ,  image_processor=UpperCamelCase__)

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

						__UpperCAmelCase	: Union[str, Any]		=					processor.batch_decode(UpperCamelCase__)
						__UpperCAmelCase	: Dict		=					tokenizer.batch_decode(UpperCamelCase__)

						self.assertListEqual(UpperCamelCase__      ,  UpperCamelCase__)


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					self.get_image_processor()
						__UpperCAmelCase	: List[Any]		=					self.get_tokenizer()

						__UpperCAmelCase	: List[Any]		=					BlipaProcessor(tokenizer=UpperCamelCase__      ,  image_processor=UpperCamelCase__)

						__UpperCAmelCase	: str		=					'''lower newer'''
						__UpperCAmelCase	: List[str]		=					self.prepare_image_inputs()

						__UpperCAmelCase	: List[Any]		=					processor(text=UpperCamelCase__      ,  images=UpperCamelCase__)

						# 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"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


