
"""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, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class      _SCREAMING_SNAKE_CASE   (     unittest.TestCase				):






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Dict     	=    tempfile.mkdtemp()

						lowerCAmelCase_       :Tuple     	=    CLIPImageProcessor()
						lowerCAmelCase_       :Any     	=    BertTokenizer.from_pretrained(
						    """hf-internal-testing/tiny-random-BertModel"""			,  model_input_names=["""input_ids""", """attention_mask"""]			)

						lowerCAmelCase_       :int     	=    GitProcessor(__A			,  __A			)

						processor.save_pretrained(self.tmpdirname			)






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	int:
						return AutoProcessor.from_pretrained(self.tmpdirname			,  **__A			).tokenizer






			def 					__lowerCAmelCase					(   self			,  **__A			)     ->	Tuple:
						return AutoProcessor.from_pretrained(self.tmpdirname			,  **__A			).image_processor






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						shutil.rmtree(self.tmpdirname			)






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_       :Optional[Any]     	=    [np.random.randint(255			,  size=(3, 30, 400)			,  dtype=np.uinta			)]

						lowerCAmelCase_       :List[Any]     	=    [Image.fromarray(np.moveaxis(__A			,  0			,  -1			)			) for x in image_inputs]

						return image_inputs






			def 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_       :Dict     	=    GitProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()			)
						processor.save_pretrained(self.tmpdirname			)

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

						lowerCAmelCase_       :Tuple     	=    GitProcessor.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 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :Tuple     	=    self.get_image_processor()
						lowerCAmelCase_       :List[Any]     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[Any]     	=    GitProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :str     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Tuple     	=    image_processor(__A			,  return_tensors="""np"""			)
						lowerCAmelCase_       :Optional[Any]     	=    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 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :str     	=    self.get_image_processor()
						lowerCAmelCase_       :Dict     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[Any]     	=    GitProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :str     	=    """lower newer"""

						lowerCAmelCase_       :Union[str, Any]     	=    processor(text=__A			)

						lowerCAmelCase_       :int     	=    tokenizer(__A			,  return_token_type_ids=__A			)

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






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Tuple     	=    self.get_image_processor()
						lowerCAmelCase_       :Tuple     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[int]     	=    GitProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Optional[Any]     	=    """lower newer"""
						lowerCAmelCase_       :List[Any]     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Union[str, Any]     	=    processor(text=__A			,  images=__A			)

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

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






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						lowerCAmelCase_       :Union[str, Any]     	=    self.get_image_processor()
						lowerCAmelCase_       :str     	=    self.get_tokenizer()

						lowerCAmelCase_       :Any     	=    GitProcessor(tokenizer=__A			,  image_processor=__A			)

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

						lowerCAmelCase_       :Union[str, Any]     	=    processor.batch_decode(__A			)
						lowerCAmelCase_       :Dict     	=    tokenizer.batch_decode(__A			)

						self.assertListEqual(__A			,  __A			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Any     	=    self.get_image_processor()
						lowerCAmelCase_       :Dict     	=    self.get_tokenizer()

						lowerCAmelCase_       :Optional[Any]     	=    GitProcessor(tokenizer=__A			,  image_processor=__A			)

						lowerCAmelCase_       :Tuple     	=    """lower newer"""
						lowerCAmelCase_       :Any     	=    self.prepare_image_inputs()

						lowerCAmelCase_       :Any     	=    processor(text=__A			,  images=__A			)

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








"""simple docstring"""






import math as m






def 		_snake_case			(      lowercase__						:    Any ,	lowercase__						:    Any ,	lowercase__						:    Optional[int]=0.001						)    ->							Tuple:


			'''simple docstring'''





			return (f(a + h						) - f(a - h						)) / (2 * h)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    List[str]=0 ,	lowercase__						:    Union[str, Any]=1_0_0 ,	lowercase__						:    Any=0.0001 ,	lowercase__						:    List[str]=1E-6 ,	lowercase__						:    str=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    xa  # set the initial guess
			lowerCAmelCase_       :int     	=    [a]
			lowerCAmelCase_       :int     	=    abs(f(lowercase__						)						)
			lowerCAmelCase_       :Optional[int]     	=    lambda lowercase__						: calc_derivative(lowercase__ ,	lowercase__ ,	h=lowercase__						)  # noqa: E731  Derivative of f(x)
			for _ in range(lowercase__						):
						if fa(lowercase__						) == 0:
									raise ValueError("""No converging solution found"""						)
						lowerCAmelCase_       :Tuple     	=    a - f(lowercase__						) / fa(lowercase__						)  # Calculate the next estimate
						if logsteps:
									steps.append(lowercase__						)
						if error < maxerror:
									break
			else:
						raise ValueError("""Iteration limit reached, no converging solution found"""						)
			if logsteps:
						# If logstep is true, then log intermediate steps
						return a, error, steps
			return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				__UpperCAmelCase 					=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				__UpperCAmelCase					,					__UpperCAmelCase					,					__UpperCAmelCase 					=    newton_raphson(
				    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel('step')
				plt.ylabel('error')
				plt.show()
				print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")






