


'''simple docstring'''

import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase:		Union[str, Any]    						=  (30, 487)
__lowercase:		Union[str, Any]    						=  (36, 1_024)
__lowercase:		Optional[Any]    						=  (100, 100)
__lowercase:		str    						=  100

__lowercase:		List[str]    						=  datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

__lowercase						,__lowercase:		Optional[int]    						=  os.path.split(__file__)
__lowercase:		Any    						=  os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					List[str] )		->       Dict:





   '''simple docstring'''







   with ArrowWriter(features=_UpperCamelCase     ,		path=os.path.join(_UpperCamelCase     ,		"beta.arrow" ) ) as writer:
      for key, record in dummy_data:
         UpperCamelCase__			    =    my_features.encode_example(_UpperCamelCase )
         writer.write(_UpperCamelCase )
      UpperCamelCase__    ,		UpperCamelCase__			    =    writer.finalize()



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					Dict )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   for _ in dataset:
      pass



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Union[str, Any]     ,		_UpperCamelCase   :					Optional[int] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   dataset.set_format("numpy" )
   for _ in dataset:
      pass



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					Union[str, Any] )		->       List[str]:





   '''simple docstring'''







   UpperCamelCase__			    =    10
   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   for i in range(0     ,		len(_UpperCamelCase )     ,		_UpperCamelCase ):
      UpperCamelCase__			    =    dataset[i : i + batch_size]



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					List[Any]     ,		_UpperCamelCase   :					Any )		->       Dict:





   '''simple docstring'''







   UpperCamelCase__			    =    10
   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   dataset.set_format("numpy" )
   for i in range(0     ,		len(_UpperCamelCase )     ,		_UpperCamelCase ):
      UpperCamelCase__			    =    dataset[i : i + batch_size]



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Any     ,		_UpperCamelCase   :					Union[str, Any] )		->       Optional[Any]:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   for col in feats:
      UpperCamelCase__			    =    dataset[col]



@get_duration
def 							SCREAMING_SNAKE_CASE__(		_UpperCamelCase   :					Optional[int]     ,		_UpperCamelCase   :					List[Any] )		->       str:





   '''simple docstring'''







   UpperCamelCase__			    =    datasets.Dataset.from_file(
       filename=os.path.join(_UpperCamelCase     ,		"beta.arrow" )     ,		info=datasets.DatasetInfo(features=_UpperCamelCase ) )
   dataset.set_format("numpy" )
   for col in feats:
      UpperCamelCase__			    =    dataset[col]



def 							SCREAMING_SNAKE_CASE__(		)		->       Dict:





   '''simple docstring'''







   UpperCamelCase__			    =    {}
   UpperCamelCase__			    =    (
       read_unformated,
       read_formatted_as_numpy,
       read_batch_unformated,
       read_batch_formatted_as_numpy,
       read_col_unformated,
       read_col_formatted_as_numpy,
   )
   with tempfile.TemporaryDirectory() as tmp_dir:
      UpperCamelCase__			    =    datasets.Features({"image": ArrayaD(_UpperCamelCase     ,		dtype="float32" )} )
      UpperCamelCase__			    =    generate_examples(features=_UpperCamelCase     ,		num_examples=_UpperCamelCase )
      UpperCamelCase__			    =    write(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      for read_func in read_functions:
         UpperCamelCase__			    =    read_func(_UpperCamelCase     ,		_UpperCamelCase )

   with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
      # )
      UpperCamelCase__			    =    datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32" ) ) )} )
      UpperCamelCase__			    =    generate_examples(
          features=_UpperCamelCase     ,		num_examples=_UpperCamelCase     ,		seq_shapes={"image": SPEED_TEST_SHAPE} )
      UpperCamelCase__			    =    write(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      for read_func in read_functions:
         UpperCamelCase__			    =    read_func(_UpperCamelCase     ,		_UpperCamelCase )

   with tempfile.TemporaryDirectory() as tmp_dir:
      # don't use fixed length for fair comparison
      # feats = datasets.Features(
      #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
      # )
      UpperCamelCase__			    =    datasets.Features({"image": datasets.Sequence(datasets.Value("float32" ) )} )
      UpperCamelCase__			    =    generate_examples(
          features=_UpperCamelCase     ,		num_examples=_UpperCamelCase     ,		seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}     ,		)
      UpperCamelCase__			    =    write(_UpperCamelCase     ,		_UpperCamelCase     ,		_UpperCamelCase )
      for read_func in read_functions:
         UpperCamelCase__			    =    read_func(_UpperCamelCase     ,		_UpperCamelCase )

   with open(_UpperCamelCase     ,		"wb" ) as f:
      f.write(json.dumps(_UpperCamelCase ).encode("utf-8" ) )


if __name__ == "__main__":  # useful to run the profiler
       benchmark_array_xd()

"""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}}}""")






