


'''simple docstring'''




import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard




class  _lowercase   (  unittest.TestCase       ):
   def a     (	self					:			Any			)   ->       List[Any]:
        __snake_case    			=	{
            'model_details': {
                'Organization': 'testing',
                'Model date': 'today',
                'Model version': 'v2.1, Developed by Test Corp in 2019.',
                'Architecture': 'Convolutional Neural Network.',
            },
            'metrics': 'BLEU and ROUGE-1',
            'evaluation_data': {
                'Datasets': {'BLEU': 'My-great-dataset-v1', 'ROUGE-1': 'My-short-dataset-v2.1'},
                'Preprocessing': 'See details on https://arxiv.org/pdf/1810.03993.pdf',
            },
            'training_data': {
                'Dataset': 'English Wikipedia dump dated 2018-12-01',
                'Preprocessing': (
                    'Using SentencePiece vocabulary of size 52k tokens. See details on'
                    ' https://arxiv.org/pdf/1810.03993.pdf'
                ),
            },
            'quantitative_analyses': {'BLEU': 5_5.1, 'ROUGE-1': 76},
        }
   def a     (	self					:			List[str]			)   ->       List[str]:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'model_details'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'intended_use'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'factors'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'metrics'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'evaluation_data'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'training_data'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'quantitative_analyses'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'ethical_considerations'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'caveats_and_recommendations'			)			)
   def a     (	self					:			str			)   ->       str:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)
        __snake_case    			=	json.loads(modelcard.to_json_string()			)
        for key, value in self.inputs_dict.items():
             self.assertEqual(obj[key]					,      SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			str			)   ->       Union[str, Any]:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)

        with tempfile.TemporaryDirectory() as tmpdirname:
             __snake_case    			=	os.path.join(SCREAMING_SNAKE_CASE_					,      'modelcard.json'			)
             model_card_first.to_json_file(SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	ModelCard.from_json_file(SCREAMING_SNAKE_CASE_			)

        self.assertEqual(model_card_second.to_dict()					,      model_card_first.to_dict()			)





   def a     (	self					:			Optional[Any]			)   ->       Dict:
        __snake_case    			=	ModelCard.from_dict(self.inputs_dict			)

        with tempfile.TemporaryDirectory() as tmpdirname:
             model_card_first.save_pretrained(SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	ModelCard.from_pretrained(SCREAMING_SNAKE_CASE_			)

        self.assertEqual(model_card_second.to_dict()					,      model_card_first.to_dict()			)








'''simple docstring'''




import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

_a      :      Optional[int]       				=      0.25 + 0.0J
_a      :      Dict       				=      -0.4 + 0.6J
_a      :      List[str]       				=      -0.1 + 0.651J
_a      :      Optional[int]       				=      -2.0
_a      :      List[str]       				=      56
_a      :      Tuple       				=      2.0
_a      :      List[str]       				=      666





def      _a		(lowercase__ :      complex       ,      lowercase__ :      numpy.ndarray	) -> numpy.ndarray:






     """simple docstring"""
     return numpy.exp(lowercase__	) + c_parameter





def      _a		(lowercase__ :      complex       ,      lowercase__ :      numpy.ndarray	) -> numpy.ndarray:






     """simple docstring"""
     return z_values * z_values + c_parameter





def      _a		(lowercase__ :      float       ,      lowercase__ :      int	) -> numpy.ndarray:






     """simple docstring"""
     __snake_case    			=	numpy.linspace(-window_size       ,      lowercase__       ,      lowercase__	)
     __snake_case    			=	x.reshape((nb_pixels, 1)	)
     __snake_case    			=	numpy.linspace(-window_size       ,      lowercase__       ,      lowercase__	)
     __snake_case    			=	y.reshape((1, nb_pixels)	)
     return x + 1.0J * y





def      _a		(lowercase__ :      Callable[[Any, numpy.ndarray], numpy.ndarray]       ,      lowercase__ :      Any       ,      lowercase__ :      int       ,      lowercase__ :      numpy.ndarray       ,      lowercase__ :      float | None = None       ,      ) -> numpy.ndarray:






     """simple docstring"""
     __snake_case    			=	z_a.astype('complex64'	)
     for _ in range(lowercase__	):
          __snake_case    			=	eval_function(lowercase__       ,      lowercase__	)
          if infinity is not None:
               numpy.nan_to_num(lowercase__       ,      copy=lowercase__       ,      nan=lowercase__	)
               __snake_case    			=	infinity
     return z_n





def      _a		(lowercase__ :      str       ,      lowercase__ :      Any       ,      lowercase__ :      float       ,      lowercase__ :      numpy.ndarray       ,      ) -> None:






     """simple docstring"""
     __snake_case    			=	(abs(lowercase__	)).transpose()
     __snake_case    			=	abs_z_final[::-1, :]
     pyplot.matshow(abs_z_final < escape_radius	)
     pyplot.title(f'Julia set of ${function_label}$, $c={function_params}$'	)
     pyplot.show()





def      _a		() -> None:






     """simple docstring"""
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in multiply'	)
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='invalid value encountered in multiply'       ,      )
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in absolute'	)
     warnings.filterwarnings(
         'ignore'       ,      category=lowercase__       ,      message='overflow encountered in exp'	)


if __name__ == "__main__":
       _a      :      List[Any]       				=      prepare_grid(window_size, nb_pixels)

       ignore_overflow_warnings()  # See file header for explanations

       _a      :      Optional[Any]       				=      24
       _a      :      Optional[int]       				=      2 * abs(c_cauliflower) + 1
       _a      :      List[str]       				=      iterate_function(
           eval_quadratic_polynomial,
           c_cauliflower,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_cauliflower, escape_radius, z_final)

       _a      :      str       				=      64
       _a      :      Tuple       				=      2 * abs(c_polynomial_a) + 1
       _a      :      List[Any]       				=      iterate_function(
           eval_quadratic_polynomial,
           c_polynomial_a,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

       _a      :      List[Any]       				=      161
       _a      :      Tuple       				=      2 * abs(c_polynomial_a) + 1
       _a      :      int       				=      iterate_function(
           eval_quadratic_polynomial,
           c_polynomial_a,
           nb_iterations,
           z_a,
           infinity=1.1 * escape_radius,
       )
       show_results("z^2+c", c_polynomial_a, escape_radius, z_final)

       _a      :      str       				=      12
       _a      :      Optional[Any]       				=      10_000.0
       _a      :      Dict       				=      iterate_function(
           eval_exponential,
           c_exponential,
           nb_iterations,
           z_a + 2,
           infinity=1.0e1_0,
       )
       show_results("e^z+c", c_exponential, escape_radius, z_final)




