
import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets






@contextmanager
def    lowercase	(  ):






			def filter_log(lowerCAmelCase__	):
						return False if "This IS expected if you are initializing" in record.msg else True

			lowerCamelCase_ 		=     datasets.utils.logging.get_logger('''transformers.modeling_utils'''	)
			logger.addFilter(UpperCamelCase_	)
			try:
						yield
			finally:
						logger.removeFilter(UpperCamelCase_	)


A_						       =				"\\n@inproceedings{bert-score,\n  title={BERTScore: Evaluating Text Generation with BERT},\n  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},\n  booktitle={International Conference on Learning Representations},\n  year={2020},\n  url={https://openreview.net/forum?id=SkeHuCVFDr}\n}\n"

A_						       =				"\\nBERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference\nsentences by cosine similarity.\nIt has been shown to correlate with human judgment on sentence-level and system-level evaluation.\nMoreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language\ngeneration tasks.\n\nSee the project's README at https://github.com/Tiiiger/bert_score#readme for more information.\n"

A_						       =				"\nBERTScore Metrics with the hashcode from a source against one or more references.\n\nArgs:\n    predictions (list of str): Prediction/candidate sentences.\n    references (list of str or list of list of str): Reference sentences.\n    lang (str): Language of the sentences; required (e.g. 'en').\n    model_type (str): Bert specification, default using the suggested\n        model for the target language; has to specify at least one of\n        `model_type` or `lang`.\n    num_layers (int): The layer of representation to use,\n        default using the number of layers tuned on WMT16 correlation data.\n    verbose (bool): Turn on intermediate status update.\n    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.\n    device (str): On which the contextual embedding model will be allocated on.\n        If this argument is None, the model lives on cuda:0 if cuda is available.\n    nthreads (int): Number of threads.\n    batch_size (int): Bert score processing batch size,\n        at least one of `model_type` or `lang`. `lang` needs to be\n        specified when `rescale_with_baseline` is True.\n    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.\n    baseline_path (str): Customized baseline file.\n    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.\n\nReturns:\n    precision: Precision.\n    recall: Recall.\n    f1: F1 score.\n    hashcode: Hashcode of the library.\n\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bertscore = datasets.load_metric(\"bertscore\")\n    >>> results = bertscore.compute(predictions=predictions, references=references, lang=\"en\")\n    >>> print([round(v, 2) for v in results[\"f1\"]])\n    [1.0, 1.0]\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION )
class    __lowerCamelCase     (   datasets.Metric ):




				def 					UpperCAmelCase__	(							self      ):
							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,	citation=_CITATION       ,	homepage='''https://github.com/Tiiiger/bert_score'''       ,	inputs_description=_KWARGS_DESCRIPTION       ,	features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''string'''       ,	id='''sequence'''      ),
							            '''references''': datasets.Sequence(datasets.Value('''string'''       ,	id='''sequence'''      )       ,	id='''references'''      ),
							        }      )       ,	codebase_urls=['''https://github.com/Tiiiger/bert_score''']       ,	reference_urls=[
							        '''https://github.com/Tiiiger/bert_score''',
							        '''https://arxiv.org/abs/1904.09675''',
							    ]       ,	)




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	UpperCAmelCase=False       ,	UpperCAmelCase=False       ,	UpperCAmelCase=None       ,	UpperCAmelCase=64       ,	UpperCAmelCase=4       ,	UpperCAmelCase=False       ,	UpperCAmelCase=False       ,	UpperCAmelCase=None       ,	UpperCAmelCase=False       ,	):
							lowerCamelCase_ 		=     bert_score.utils.get_hash
							lowerCamelCase_ 		=     bert_score.BERTScorer

							if version.parse(bert_score.__version__      ) >= version.parse('''0.3.10'''      ):
										lowerCamelCase_ 		=     functools.partial(__snake_case       ,	use_fast_tokenizer=__snake_case      )
										lowerCamelCase_ 		=     functools.partial(__snake_case       ,	use_fast_tokenizer=__snake_case      )
							elif use_fast_tokenizer:
										raise ImportWarning(
										    '''To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn\'t match this condition.\n'''
										    '''You can install it with `pip install "bert-score>=0.3.10"`.'''      )

							if model_type is None:
										assert lang is not None, "either lang or model_type should be specified"
										lowerCamelCase_ 		=     bert_score.utils.langamodel[lang.lower()]

							if num_layers is None:
										lowerCamelCase_ 		=     bert_score.utils.modelalayers[model_type]

							lowerCamelCase_ 		=     get_hash(
							    model=__snake_case       ,	num_layers=__snake_case       ,	idf=__snake_case       ,	rescale_with_baseline=__snake_case       ,	use_custom_baseline=baseline_path is not None       ,	)

							with filter_logging_context():
										if not hasattr(self       ,	'''cached_bertscorer'''      ) or self.cached_bertscorer.hash != hashcode:
													lowerCamelCase_ 		=     scorer(
													    model_type=__snake_case       ,	num_layers=__snake_case       ,	batch_size=__snake_case       ,	nthreads=__snake_case       ,	all_layers=__snake_case       ,	idf=__snake_case       ,	device=__snake_case       ,	lang=__snake_case       ,	rescale_with_baseline=__snake_case       ,	baseline_path=__snake_case       ,	)

							((lowerCamelCase_)		,   (lowerCamelCase_)		,   (lowerCamelCase_)) 		=     self.cached_bertscorer.score(
							    cands=__snake_case       ,	refs=__snake_case       ,	verbose=__snake_case       ,	batch_size=__snake_case       ,	)
							lowerCamelCase_ 		=     {
							    '''precision''': P.tolist(),
							    '''recall''': R.tolist(),
							    '''f1''': F.tolist(),
							    '''hashcode''': hashcode,
							}
							return output_dict




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	**UpperCAmelCase      ):
							# References can be strings or lists of strings
							# Let's change strings to lists of strings with one element
							if references is not None:
										lowerCamelCase_ 		=     [[ref] if isinstance(__snake_case       ,	__snake_case      ) else ref for ref in references]
							super().add_batch(predictions=__snake_case       ,	references=__snake_case       ,	**__snake_case      )





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase=None       ,	UpperCAmelCase=None       ,	**UpperCAmelCase      ):
							# References can be strings or lists of strings
							# Let's change strings to lists of strings with one element
							if isinstance(__snake_case       ,	__snake_case      ):
										lowerCamelCase_ 		=     [reference]
							super().add(prediction=__snake_case       ,	reference=__snake_case       ,	**__snake_case      )





"""simple docstring"""


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

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     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    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


if __name__ == "__main__":
						A_						       =				prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				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_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				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_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				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_						       =				12
						A_						       =				10000.0
						A_						       =				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)



