





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 						lowerCamelCase       (unittest.TestCase      ):







       """simple docstring"""




       def  A_     ( self     :					List[Any]						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tempfile.mkdtemp()

              SCREAMING_SNAKE_CASE__       :      Dict     =						BlipImageProcessor()
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"						)

              SCREAMING_SNAKE_CASE__       :      Tuple     =						BlipProcessor(_UpperCAmelCase,			_UpperCAmelCase						)

              processor.save_pretrained(self.tmpdirname						)






       def  A_     ( self     :					List[str],			**_UpperCAmelCase     :					int						)				->			Union[str, Any]:





              """simple docstring"""
              return AutoProcessor.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						).tokenizer






       def  A_     ( self     :					Optional[int],			**_UpperCAmelCase     :					Any						)				->			Union[str, Any]:





              """simple docstring"""
              return AutoProcessor.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						).image_processor






       def  A_     ( self     :					Optional[Any]						)				->			Dict:





              """simple docstring"""
              shutil.rmtree(self.tmpdirname						)






       def  A_     ( self     :					List[Any]						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Any     =						[np.random.randint(2_5_5,			size=(3, 3_0, 4_0_0),			dtype=np.uinta						)]

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

              return image_inputs






       def  A_     ( self     :					List[Any]						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BlipProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor()						)
              processor.save_pretrained(self.tmpdirname						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_tokenizer(bos_token="(BOS)",			eos_token="(EOS)"						)
              SCREAMING_SNAKE_CASE__       :      int     =						self.get_image_processor(do_normalize=_UpperCAmelCase,			padding_value=1.0						)

              SCREAMING_SNAKE_CASE__       :      str     =						BlipProcessor.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     :					Optional[Any]						)				->			Optional[int]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      int     =						self.get_image_processor()
              SCREAMING_SNAKE_CASE__       :      int     =						self.get_tokenizer()

              SCREAMING_SNAKE_CASE__       :      Dict     =						BlipProcessor(tokenizer=_UpperCAmelCase,			image_processor=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.prepare_image_inputs()

              SCREAMING_SNAKE_CASE__       :      Any     =						image_processor(_UpperCAmelCase,			return_tensors="np"						)
              SCREAMING_SNAKE_CASE__       :      int     =						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     :					Optional[Any]						)				->			List[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						self.get_image_processor()
              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_tokenizer()

              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BlipProcessor(tokenizer=_UpperCAmelCase,			image_processor=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Any     =						"lower newer"

              SCREAMING_SNAKE_CASE__       :      Tuple     =						processor(text=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      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[int]						)				->			Union[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_image_processor()
              SCREAMING_SNAKE_CASE__       :      Any     =						self.get_tokenizer()

              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BlipProcessor(tokenizer=_UpperCAmelCase,			image_processor=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      str     =						"lower newer"
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.prepare_image_inputs()

              SCREAMING_SNAKE_CASE__       :      Any     =						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]						)				->			Optional[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_image_processor()
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_tokenizer()

              SCREAMING_SNAKE_CASE__       :      Optional[int]     =						BlipProcessor(tokenizer=_UpperCAmelCase,			image_processor=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

              SCREAMING_SNAKE_CASE__       :      Any     =						processor.batch_decode(_UpperCAmelCase						)
              SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer.batch_decode(_UpperCAmelCase						)

              self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)






       def  A_     ( self     :					Any						)				->			str:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_image_processor()
              SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_tokenizer()

              SCREAMING_SNAKE_CASE__       :      str     =						BlipProcessor(tokenizer=_UpperCAmelCase,			image_processor=_UpperCAmelCase						)

              SCREAMING_SNAKE_CASE__       :      Tuple     =						"lower newer"
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.prepare_image_inputs()

              SCREAMING_SNAKE_CASE__       :      List[str]     =						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"]						)













from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_SNAKE_CASE__       )
														else:
																					raise ZeroDivisionError("Could not find root"       ) from None

														# Precision is checked by comparing the difference of consecutive guesses
														if abs(next_guess - prev_guess       ) < precision:
																					return next_guess

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						next_guess


# 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
		# Find fourth Root of 5
		print(f"The root of x**4 - 5 = 0 is {newton_raphson('x**4 -5', 0.4 +5J)}")

		# Find value of e
		print(
		    '''The root of log(y) - 1 = 0 is ''',
		    f"{newton_raphson('log(y) - 1', 2, variable='y')}",
		)

		# Exponential Roots
		print(
		    '''The root of exp(x) - 1 = 0 is''',
		    f"{newton_raphson('exp(x) - 1', 1_0, precision=0.005)}",
		)

		# Find root of cos(x)
		print(f"The root of cos(x) = 0 is {newton_raphson('cos(x)', 0)}")






