

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

import numpy
from matplotlib import pyplot

SCREAMING_SNAKE_CASE						:str      =			0.2_5 + 0.0j
SCREAMING_SNAKE_CASE						:str      =			-0.4 + 0.6j
SCREAMING_SNAKE_CASE						:Optional[Any]      =			-0.1 + 0.6_51j
SCREAMING_SNAKE_CASE						:str      =			-2.0
SCREAMING_SNAKE_CASE						:Tuple      =			56
SCREAMING_SNAKE_CASE						:List[Any]      =			2.0
SCREAMING_SNAKE_CASE						:Tuple      =			666




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		numpy.ndarray:



    """simple docstring"""






    return numpy.exp(lowercase_       ) + c_parameter




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		numpy.ndarray:



    """simple docstring"""






    return z_values * z_values + c_parameter




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		numpy.ndarray:



    """simple docstring"""






    UpperCamelCase_          =		numpy.linspace(-window_size       ,		lowercase_       ,		lowercase_       )
    UpperCamelCase_          =		x.reshape((nb_pixels, 1)       )
    UpperCamelCase_          =		numpy.linspace(-window_size       ,		lowercase_       ,		lowercase_       )
    UpperCamelCase_          =		y.reshape((1, nb_pixels)       )
    return x + 1.0J * y




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = None       ,		)->		numpy.ndarray:



    """simple docstring"""






    UpperCamelCase_          =		z_a.astype("complex64"       )
    for _ in range(lowercase_       ):
        UpperCamelCase_          =		eval_function(lowercase_       ,		lowercase_       )
        if infinity is not None:
            numpy.nan_to_num(lowercase_       ,		copy=lowercase_       ,		nan=lowercase_       )
            UpperCamelCase_          =		infinity
    return z_n




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		)->		None:



    """simple docstring"""






    UpperCamelCase_          =		(abs(lowercase_       )).transpose()
    UpperCamelCase_          =		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        lowerCAmelCase(	)->		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__":
 SCREAMING_SNAKE_CASE						:Optional[Any]      =			prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 SCREAMING_SNAKE_CASE						:int      =			24
 SCREAMING_SNAKE_CASE						:Optional[Any]      =			2 * abs(c_cauliflower) + 1
 SCREAMING_SNAKE_CASE						:Union[str, Any]      =			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)

 SCREAMING_SNAKE_CASE						:Any      =			64
 SCREAMING_SNAKE_CASE						:Dict      =			2 * abs(c_polynomial_a) + 1
 SCREAMING_SNAKE_CASE						:Union[str, 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)

 SCREAMING_SNAKE_CASE						:int      =			161
 SCREAMING_SNAKE_CASE						:Union[str, Any]      =			2 * abs(c_polynomial_a) + 1
 SCREAMING_SNAKE_CASE						:Tuple      =			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)

 SCREAMING_SNAKE_CASE						:Optional[Any]      =			12
 SCREAMING_SNAKE_CASE						:List[Any]      =			1_0000.0
 SCREAMING_SNAKE_CASE						:int      =			iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0e10,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)



import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class  __lowerCamelCase				(	unittest.TestCase	):
      lowerCamelCase_	:      Tuple          =					MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      lowerCamelCase_	:      int          =					TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )						->       Any:
        if isinstance(model.config							,  lowerCamelCase   ):
          snake_case_						,				snake_case_       						=      list(tokenizer.lang_code_to_id.keys()   )[:2]
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang=lowerCamelCase							,  tgt_lang=lowerCamelCase   )
        else:
          snake_case_       						=      TranslationPipeline(model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        return translator, ["Some string", "Some other text"]







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase   )						->       List[str]:
        snake_case_       						=      translator("""Some string"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

        snake_case_       						=      translator(["""Some string"""]   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}]   )

        snake_case_       						=      translator(["""Some string""", """other string"""]   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": ANY(lowerCamelCase   )}, {"""translation_text""": ANY(lowerCamelCase   )}]   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                        """ Beide Beide"""
                    )
                }
            ]							,  )







      @require_tf
      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      pipeline("""translation_en_to_ro"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                        """ Beide Beide"""
                    )
                }
            ]							,  )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""pt"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                        """ urine urine urine urine urine urine urine"""
                    )
                }
            ]							,  )







      @require_tf
      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      pipeline("""translation_en_to_de"""							,  model="""patrickvonplaten/t5-tiny-random"""							,  framework="""tf"""   )
        snake_case_       						=      translator("""This is a test string"""							,  max_length=20   )
        self.assertEqual(
            lowerCamelCase							,  [
                {
                    """translation_text""": (
                        """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                        """ urine urine urine urine urine urine urine"""
                    )
                }
            ]							,  )







class  __lowerCamelCase				(	unittest.TestCase	):







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       Tuple:
        # We don't provide a default for this pair
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation_cn_to_ar"""   )

        # but we do for this one
        snake_case_       						=      pipeline(task="""translation_en_to_de"""   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """en"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """de"""   )







      @require_torch
      @slow
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )
        snake_case_       						=      MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""   )

        snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase							,  tokenizer=lowerCamelCase   )
        # Missing src_lang, tgt_lang
        with self.assertRaises(lowerCamelCase   ):
          translator("""This is a test"""   )

        snake_case_       						=      translator("""This is a test"""							,  src_lang="""en_XX"""							,  tgt_lang="""ar_AR"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """هذا إختبار"""}]   )

        snake_case_       						=      translator("""This is a test"""							,  src_lang="""en_XX"""							,  tgt_lang="""hi_IN"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """यह एक परीक्षण है"""}]   )

        # src_lang, tgt_lang can be defined at pipeline call time
        snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase							,  tokenizer=lowerCamelCase							,  src_lang="""en_XX"""							,  tgt_lang="""ar_AR"""   )
        snake_case_       						=      translator("""This is a test"""   )
        self.assertEqual(lowerCamelCase							,  [{"""translation_text""": """هذا إختبار"""}]   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        snake_case_       						=      pipeline(task="""translation_cn_to_ar"""							,  model=lowerCamelCase   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """cn"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """ar"""   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      """patrickvonplaten/t5-tiny-random"""
        with pytest.warns(lowerCamelCase							,  match=r""".*translation_en_to_de.*"""   ):
          snake_case_       						=      pipeline(task="""translation"""							,  model=lowerCamelCase   )
        self.assertEqual(translator.task							,  """translation_en_to_de"""   )
        self.assertEqual(translator._preprocess_params["""src_lang"""]							,  """en"""   )
        self.assertEqual(translator._preprocess_params["""tgt_lang"""]							,  """de"""   )







      @require_torch
      def 	lowerCAmelCase_    (     self   )						->       Any:
        with self.assertRaises(lowerCamelCase   ):
          pipeline(task="""translation"""   )