





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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   int			      =  tempfile.mkdtemp()

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

       UpperCamelCase      :   int			      =  BlipaProcessor(A_			,				A_  )

       processor.save_pretrained(self.tmpdirname  )





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self			,				**A_  ):
       '''simple docstring'''




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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   List[str]			      =  [Image.fromarray(np.moveaxis(A_			,				0			,				-1  )  ) for x in image_inputs]

       return image_inputs





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  BlipaProcessor(tokenizer=self.get_tokenizer()			,				image_processor=self.get_image_processor()  )
       processor.save_pretrained(self.tmpdirname  )

       UpperCamelCase      :   List[Any]			      =  self.get_tokenizer(bos_token="(BOS)"			,				eos_token="(EOS)"  )
       UpperCamelCase      :   str			      =  self.get_image_processor(do_normalize=A_			,				padding_value=1.0  )

       UpperCamelCase      :   List[Any]			      =  BlipaProcessor.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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   str			      =  BlipaProcessor(tokenizer=A_			,				image_processor=A_  )

       UpperCamelCase      :   List[Any]			      =  self.prepare_image_inputs()

       UpperCamelCase      :   int			      =  image_processor(A_			,				return_tensors="np"  )
       UpperCamelCase      :   Dict			      =  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  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.get_image_processor()
       UpperCamelCase      :   Optional[int]			      =  self.get_tokenizer()

       UpperCamelCase      :   int			      =  BlipaProcessor(tokenizer=A_			,				image_processor=A_  )

       UpperCamelCase      :   int			      =  "lower newer"

       UpperCamelCase      :   Tuple			      =  processor(text=A_  )

       UpperCamelCase      :   Union[str, Any]			      =  tokenizer(A_			,				return_token_type_ids=A_  )

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   str			      =  self.get_image_processor()
       UpperCamelCase      :   int			      =  self.get_tokenizer()

       UpperCamelCase      :   Optional[Any]			      =  BlipaProcessor(tokenizer=A_			,				image_processor=A_  )

       UpperCamelCase      :   Dict			      =  "lower newer"
       UpperCamelCase      :   Tuple			      =  self.prepare_image_inputs()

       UpperCamelCase      :   Tuple			      =  processor(text=A_			,				images=A_  )

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

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





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




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

       UpperCamelCase      :   List[Any]			      =  BlipaProcessor(tokenizer=A_			,				image_processor=A_  )

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

       UpperCamelCase      :   Union[str, Any]			      =  processor.batch_decode(A_  )
       UpperCamelCase      :   Tuple			      =  tokenizer.batch_decode(A_  )

       self.assertListEqual(A_			,				A_  )





      def 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   List[str]			      =  self.get_image_processor()
       UpperCamelCase      :   Union[str, Any]			      =  self.get_tokenizer()

       UpperCamelCase      :   int			      =  BlipaProcessor(tokenizer=A_			,				image_processor=A_  )

       UpperCamelCase      :   Any			      =  "lower newer"
       UpperCamelCase      :   Optional[int]			      =  self.prepare_image_inputs()

       UpperCamelCase      :   Dict			      =  processor(text=A_			,				images=A_  )

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







from __future__ import annotations

import math



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					float:
 UpperCamelCase      :   Tuple			      =  u
 for i in range(1				,  _lowerCAmelCase	):
  UpperCamelCase      :   Any			      =  temp * (u - i)
 return temp



def 						A_							(	)		->					None:
 UpperCamelCase      :   Union[str, Any]			      =  int(input("enter the numbers of values: "	)	)
 UpperCamelCase      :   list[list[float]]			      =  []
 for _ in range(_lowerCAmelCase	):
  y.append([]	)
 for i in range(_lowerCAmelCase	):
  for j in range(_lowerCAmelCase	):
   y[i].append(_lowerCAmelCase	)
   UpperCamelCase      :   List[str]			      =  0

 print("enter the values of parameters in a list: "	)
 UpperCamelCase      :   Optional[Any]			      =  list(map(_lowerCAmelCase				,  input().split()	)	)

 print("enter the values of corresponding parameters: "	)
 for i in range(_lowerCAmelCase	):
  UpperCamelCase      :   Tuple			      =  float(input()	)

 UpperCamelCase      :   Any			      =  int(input("enter the value to interpolate: "	)	)
 UpperCamelCase      :   List[Any]			      =  (value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1				,  _lowerCAmelCase	):
  for j in range(n - i	):
   UpperCamelCase      :   Optional[Any]			      =  y[j + 1][i - 1] - y[j][i - 1]

 UpperCamelCase      :   List[str]			      =  y[0][0]
 for i in range(1				,  _lowerCAmelCase	):
  summ += (ucal(_lowerCAmelCase				,  _lowerCAmelCase	) * y[0][i]) / math.factorial(_lowerCAmelCase	)

 print(F"""the value at {value} is {summ}"""	)


if __name__ == "__main__":
    main()
