


import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
A							:					List[str]     						=     pytest.mark.integration


A							:					Optional[int]     						=     {'comet'}
A							:					Union[str, Any]     						=     importlib.util.find_spec('fairseq') is not None

A							:					List[Any]     						=     {'code_eval'}
A							:					Union[str, Any]     						=     os.name == 'nt'

A							:					List[str]     						=     {'bertscore', 'frugalscore', 'perplexity'}
A							:					Dict     						=     importlib.util.find_spec('transformers') is not None





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						@wraps(_lowerCAmelCase )
						def wrapper(self      ,	_lowerCAmelCase ):
												if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
																		self.skipTest("\"test requires Fairseq\"" )
												else:
																		test_case(self      ,	_lowerCAmelCase )

						return wrapper





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						@wraps(_lowerCAmelCase )
						def wrapper(self      ,	_lowerCAmelCase ):
												if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
																		self.skipTest("\"test requires transformers\"" )
												else:
																		test_case(self      ,	_lowerCAmelCase )

						return wrapper





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






						@wraps(_lowerCAmelCase )
						def wrapper(self      ,	_lowerCAmelCase ):
												if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
																		self.skipTest("\"test not supported on Windows\"" )
												else:
																		test_case(self      ,	_lowerCAmelCase )

						return wrapper





def 	_lowerCAmelCase       (    )						->	int:


						'''simple docstring'''






						__snake_case				=		[metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
						return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished






@parameterized.named_parameters(get_local_metric_names()   )
@for_all_test_methods(
    lowercase_     ,							lowercase_     ,							lowercase_   )
@local
class     UpperCamelCase(  parameterized.TestCase   ):
					snake_case_		:				Optional[Any]										=	{}
					snake_case_		:				Optional[int]										=	None


					@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"						)
					@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Any:




											'''simple docstring'''

											__snake_case				=		"[...]"
											__snake_case				=		importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("metrics"     ,    UpperCamelCase__						)						).module_path						)
											__snake_case				=		datasets.load.import_main_class(metric_module.__name__     ,    dataset=UpperCamelCase__						)
											# check parameters
											__snake_case				=		inspect.signature(metric._compute						).parameters
											self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()						)						)  # no **kwargs
											# run doctest
											with self.patch_intensive_calls(UpperCamelCase__     ,    metric_module.__name__						):
																	with self.use_local_metrics():
																							try:
																													__snake_case				=		doctest.testmod(UpperCamelCase__     ,    verbose=UpperCamelCase__     ,    raise_on_error=UpperCamelCase__						)
																							except doctest.UnexpectedException as e:
																													raise e.exc_info[1]  # raise the exception that doctest caught
											self.assertEqual(results.failed     ,    0						)
											self.assertGreater(results.attempted     ,    1						)


					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		"[...]"
											__snake_case				=		importlib.import_module(
											    datasets.load.metric_module_factory(os.path.join("metrics"     ,    UpperCamelCase__						)						).module_path						)
											# run doctest
											with self.use_local_metrics():
																	__snake_case				=		doctest.testmod(UpperCamelCase__     ,    verbose=UpperCamelCase__     ,    raise_on_error=UpperCamelCase__						)
											self.assertEqual(results.failed     ,    0						)
											self.assertGreater(results.attempted     ,    1						)


					@contextmanager
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	int:




											'''simple docstring'''

											if metric_name in self.INTENSIVE_CALLS_PATCHER:
																	with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCamelCase__						):
																							yield
											else:
																	yield


					@contextmanager
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Any:




											'''simple docstring'''

											def load_local_metric(SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    *SCREAMING_SNAKE_CASE	:		Dict     ,    **SCREAMING_SNAKE_CASE	:		Union[str, Any]						):
																	return load_metric(os.path.join("metrics"     ,    UpperCamelCase__						)     ,    *UpperCamelCase__     ,    **UpperCamelCase__						)

											with patch("datasets.load_metric"						) as mock_load_metric:
																	__snake_case				=		load_local_metric
																	yield






					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	Tuple:




											'''simple docstring'''

											def wrapper(SCREAMING_SNAKE_CASE	:		Dict						):
																	__snake_case				=		contextmanager(UpperCamelCase__						)
																	__snake_case				=		patcher
																	return patcher

											return wrapper





@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Tuple:


						'''simple docstring'''






						import tensorflow.compat.va as tf
						from bleurt.score import Predictor

						tf.flags.DEFINE_string("sv"      ,	""      ,	"" )  # handle pytest cli flags






						class     UpperCamelCase(  lowercase_   ):


											def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Dict						)			->	Dict:




																	'''simple docstring'''

																	assert len(input_dict["input_ids"]						) == 2
																	return np.array([1.03, 1.04]						)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
						with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
												__snake_case				=		MockedPredictor()
												yield





@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						import torch

						def bert_cos_score_idf(_lowerCAmelCase      ,	_lowerCAmelCase      ,	*_lowerCAmelCase      ,	**_lowerCAmelCase ):
												return torch.tensor([[1.0, 1.0, 1.0]] * len(_lowerCAmelCase ) )

						# mock get_model which is supposed to do download a bert model
						# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
						with patch("bert_score.scorer.get_model" ), patch(
						    "bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
												__snake_case				=		bert_cos_score_idf
												yield





@LocalMetricTest.register_intensive_calls_patcher("comet" )
def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Union[str, Any]:


						'''simple docstring'''






						def load_from_checkpoint(_lowerCAmelCase ):






												class     UpperCamelCase:


																	def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    *SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    **SCREAMING_SNAKE_CASE	:		str						)			->	int:




																							'''simple docstring'''

																							assert len(UpperCamelCase__						) == 2
																							__snake_case				=		[0.19, 0.92]
																							return scores, sum(UpperCamelCase__						) / len(UpperCamelCase__						)




												return Model()

						# mock load_from_checkpoint which is supposed to do download a bert model
						# mock load_from_checkpoint which is supposed to do download a bert model
						with patch("comet.download_model" ) as mock_download_model:
												__snake_case				=		None
												with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
																		__snake_case				=		load_from_checkpoint
																		yield





def 	_lowerCAmelCase       (    )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		load_metric(os.path.join("metrics"      ,	"seqeval" ) )
						__snake_case				=		"ERROR"
						__snake_case				=		F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
						with pytest.raises(_lowerCAmelCase      ,	match=re.escape(_lowerCAmelCase ) ):
												metric.compute(predictions=[]      ,	references=[]      ,	scheme=_lowerCAmelCase )



from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def 	_lowerCAmelCase       (    _lowerCAmelCase = "" )						->	dict[str, float]:


						'''simple docstring'''






						__snake_case				=		url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
						__snake_case				=		BeautifulSoup(requests.get(_lowerCAmelCase ).text      ,	"html.parser" )
						__snake_case				=		soup.find_all("td"      ,	attrs="titleColumn" )
						__snake_case				=		soup.find_all("td"      ,	class_="ratingColumn imdbRating" )
						return {
						    title.a.text: float(rating.strong.text )
						    for title, rating in zip(_lowerCAmelCase      ,	_lowerCAmelCase )
						}





def 	_lowerCAmelCase       (    _lowerCAmelCase = "IMDb_Top_250_Movies.csv" )						->	None:


						'''simple docstring'''






						__snake_case				=		get_imdb_top_aaa_movies()
						with open(_lowerCAmelCase      ,	"w"      ,	newline="" ) as out_file:
												__snake_case				=		csv.writer(_lowerCAmelCase )
												writer.writerow(["Movie title", "IMDb rating"] )
												for title, rating in movies.items():
																		writer.writerow([title, rating] )


if __name__ == "__main__":
	write_movies()





