
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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast







@require_vision
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		tempfile.mkdtemp()

												lowercase					=		BlipImageProcessor()
												lowercase					=		BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel''')

												lowercase					=		BlipProcessor(A__     ,A__)

												processor.save_pretrained(self.tmpdirname)




							def 				A__   (       self     ,**A__):
												return AutoProcessor.from_pretrained(self.tmpdirname     ,**A__).tokenizer




							def 				A__   (       self     ,**A__):
												return AutoProcessor.from_pretrained(self.tmpdirname     ,**A__).image_processor




							def 				A__   (       self):
												shutil.rmtree(self.tmpdirname)




							def 				A__   (       self):
												lowercase					=		[np.random.randint(2_5_5     ,size=(3, 3_0, 4_0_0)     ,dtype=np.uinta)]

												lowercase					=		[Image.fromarray(np.moveaxis(A__     ,0     ,-1)) for x in image_inputs]

												return image_inputs




							def 				A__   (       self):
												lowercase					=		BlipProcessor(tokenizer=self.get_tokenizer()     ,image_processor=self.get_image_processor())
												processor.save_pretrained(self.tmpdirname)

												lowercase					=		self.get_tokenizer(bos_token='''(BOS)'''     ,eos_token='''(EOS)''')
												lowercase					=		self.get_image_processor(do_normalize=A__     ,padding_value=1.0)

												lowercase					=		BlipProcessor.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 				A__   (       self):
												lowercase					=		self.get_image_processor()
												lowercase					=		self.get_tokenizer()

												lowercase					=		BlipProcessor(tokenizer=A__     ,image_processor=A__)

												lowercase					=		self.prepare_image_inputs()

												lowercase					=		image_processor(A__     ,return_tensors='''np''')
												lowercase					=		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 				A__   (       self):
												lowercase					=		self.get_image_processor()
												lowercase					=		self.get_tokenizer()

												lowercase					=		BlipProcessor(tokenizer=A__     ,image_processor=A__)

												lowercase					=		'''lower newer'''

												lowercase					=		processor(text=A__)

												lowercase					=		tokenizer(A__     ,return_token_type_ids=A__)

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




							def 				A__   (       self):
												lowercase					=		self.get_image_processor()
												lowercase					=		self.get_tokenizer()

												lowercase					=		BlipProcessor(tokenizer=A__     ,image_processor=A__)

												lowercase					=		'''lower newer'''
												lowercase					=		self.prepare_image_inputs()

												lowercase					=		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 				A__   (       self):
												lowercase					=		self.get_image_processor()
												lowercase					=		self.get_tokenizer()

												lowercase					=		BlipProcessor(tokenizer=A__     ,image_processor=A__)

												lowercase					=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												lowercase					=		processor.batch_decode(A__)
												lowercase					=		tokenizer.batch_decode(A__)

												self.assertListEqual(A__     ,A__)





							def 				A__   (       self):
												lowercase					=		self.get_image_processor()
												lowercase					=		self.get_tokenizer()

												lowercase					=		BlipProcessor(tokenizer=A__     ,image_processor=A__)

												lowercase					=		'''lower newer'''
												lowercase					=		self.prepare_image_inputs()

												lowercase					=		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

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__ = 10**-10	):
					'''simple docstring'''
					lowercase					=		a
					while True:
										lowercase					=		Decimal(lowerCAmelCase__	) - (
										    Decimal(eval(lowerCAmelCase__	)	) / Decimal(eval(str(diff(lowerCAmelCase__	)	)	)	)  # noqa: S307
										)
										# This number dictates the accuracy of the answer
										if abs(eval(lowerCAmelCase__	)	) < precision:  # noqa: S307
															return float(lowerCAmelCase__	)


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')
							# Find root of polynomial
							print(F'The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}')
							# Find Square Root of 5
							print(F'The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}')
							# Exponential Roots
							print(F'The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}')




