






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
lowerCAmelCase_           = pytest.mark.integration


lowerCAmelCase_           = {'comet'}
lowerCAmelCase_           = importlib.util.find_spec('fairseq') is not None

lowerCAmelCase_           = {'code_eval'}
lowerCAmelCase_           = os.name == 'nt'

lowerCAmelCase_           = {'bertscore', 'frugalscore', 'perplexity'}
lowerCAmelCase_           = importlib.util.find_spec('transformers') is not None







def      snake_case		(							UpperCAmelCase	:       int   ):
				@wraps(UpperCAmelCase   )
				def wrapper(self	:       Tuple,						UpperCAmelCase	:       List[str]   ):
								if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
												self.skipTest('"test requires Fairseq"'   )
								else:
												test_case(self,						UpperCAmelCase   )

				return wrapper







def      snake_case		(							UpperCAmelCase	:       Tuple   ):
				@wraps(UpperCAmelCase   )
				def wrapper(self	:       int,						UpperCAmelCase	:       str   ):
								if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
												self.skipTest('"test requires transformers"'   )
								else:
												test_case(self,						UpperCAmelCase   )

				return wrapper







def      snake_case		(							UpperCAmelCase	:       Union[str, Any]   ):
				@wraps(UpperCAmelCase   )
				def wrapper(self	:       str,						UpperCAmelCase	:       Dict   ):
								if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
												self.skipTest('"test not supported on Windows"'   )
								else:
												test_case(self,						UpperCAmelCase   )

				return wrapper







def      snake_case		(							):
				A	 =      [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(
    snake_case__			,       snake_case__			,       snake_case__ )
@local
class      UpperCamelCase (				parameterized.TestCase ):







		"""simple docstring"""



		snake_case    =   {}
		snake_case    =   None
		@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'		)
		@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						A	 =      '[...]'
						A	 =      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,_SCREAMING_SNAKE_CASE		)		).module_path		)
						A	 =      datasets.load.import_main_class(metric_module.__name__						,dataset=_SCREAMING_SNAKE_CASE		)
						# check parameters
						A	 =      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(_SCREAMING_SNAKE_CASE						,metric_module.__name__		):
										with self.use_local_metrics():
														try:
																		A	 =      doctest.testmod(_SCREAMING_SNAKE_CASE						,verbose=_SCREAMING_SNAKE_CASE						,raise_on_error=_SCREAMING_SNAKE_CASE		)
														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       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Tuple:


						'''simple docstring'''
						A	 =      '[...]'
						A	 =      importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'						,_SCREAMING_SNAKE_CASE		)		).module_path		)
						# run doctest
						with self.use_local_metrics():
										A	 =      doctest.testmod(_SCREAMING_SNAKE_CASE						,verbose=_SCREAMING_SNAKE_CASE						,raise_on_error=_SCREAMING_SNAKE_CASE		)
						self.assertEqual(results.failed						,0		)
						self.assertGreater(results.attempted						,1		)
		@contextmanager
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[int]:


						'''simple docstring'''
						if metric_name in self.INTENSIVE_CALLS_PATCHER:
										with self.INTENSIVE_CALLS_PATCHER[metric_name](_SCREAMING_SNAKE_CASE		):
														yield
						else:
										yield
		@contextmanager
		def       A(  self		:       Union[str, Any]		)    ->   List[Any]:


						'''simple docstring'''
						def load_local_metric(_SCREAMING_SNAKE_CASE		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       List[str]						,**_SCREAMING_SNAKE_CASE		:       Dict		):
										return load_metric(os.path.join('metrics'						,_SCREAMING_SNAKE_CASE		)						,*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						with patch('datasets.load_metric'		) as mock_load_metric:
										A	 =      load_local_metric
										yield







		@classmethod
		def       A(  cls		:       List[str]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Any:


						'''simple docstring'''
						def wrapper(_SCREAMING_SNAKE_CASE		:       str		):
										A	 =      contextmanager(_SCREAMING_SNAKE_CASE		)
										A	 =      patcher
										return patcher

						return wrapper







@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def      snake_case		(							UpperCAmelCase	:       Any   ):
				import tensorflow.compat.va as tf
				from bleurt.score import Predictor

				tf.flags.DEFINE_string('sv',						'',						''   )  # handle pytest cli flags
				class      UpperCamelCase (				snake_case__ ):







						"""simple docstring"""



						def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[str]:


										'''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:
								A	 =      MockedPredictor()
								yield







@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def      snake_case		(							UpperCAmelCase	:       str   ):
				import torch

				def bert_cos_score_idf(UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       Optional[int],						*UpperCAmelCase	:       List[str],						**UpperCAmelCase	:       List[Any]   ):
								return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCAmelCase   )   )

				# 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:
								A	 =      bert_cos_score_idf
								yield







@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def      snake_case		(							UpperCAmelCase	:       str   ):
				def load_from_checkpoint(UpperCAmelCase	:       List[str]   ):
								class      UpperCamelCase :







										"""simple docstring"""



										def       A(  self		:       Dict						,_SCREAMING_SNAKE_CASE		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Dict:


														'''simple docstring'''
														assert len(_SCREAMING_SNAKE_CASE		) == 2
														A	 =      [0.19, 0.92]
														return scores, sum(_SCREAMING_SNAKE_CASE		) / len(_SCREAMING_SNAKE_CASE		)


								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:
								A	 =      None
								with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
												A	 =      load_from_checkpoint
												yield







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













from manim import *
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       Dict		)    ->   Tuple:


						'''simple docstring'''
						A	 =      Rectangle(height=0.5						,width=0.5		)
						A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('CPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						cpu.move_to([-2.5, -0.5, 0]		)
						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      [mem.copy() for i in range(1		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('GPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						gpu.align_to(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						gpu.set_x(gpu.get_x() - 1		)

						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)

						A	 =      Text('Model'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						model.move_to([3, -1.0, 0]		)

						self.play(
						    Create(_SCREAMING_SNAKE_CASE						,run_time=1		)						,Create(_SCREAMING_SNAKE_CASE						,run_time=1		)						,Create(_SCREAMING_SNAKE_CASE						,run_time=1		)						,)

						A	 =      MarkupText(
						    f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'						,font_size=2_4						,)

						A	 =      Square(side_length=2.2		)
						key.move_to([-5, 2, 0]		)

						A	 =      MarkupText(
						    f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'						,font_size=1_8						,)

						key_text.move_to([-5, 2.4, 0]		)

						step_a.move_to([2, 2, 0]		)
						self.play(Write(_SCREAMING_SNAKE_CASE						,run_time=2.5		)						,Write(_SCREAMING_SNAKE_CASE		)						,Write(_SCREAMING_SNAKE_CASE		)		)

						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      []
						A	 =      []
						A	 =      []
						for i, rect in enumerate(_SCREAMING_SNAKE_CASE		):
										A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0.0		).set_fill(_SCREAMING_SNAKE_CASE						,opacity=0.7		)
										cpu_target.move_to(_SCREAMING_SNAKE_CASE		)
										cpu_target.generate_target()
										A	 =      0.46 / 4
										A	 =      0.46 / 3

										if i == 0:
														cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)						,buff=0.02						,direction=_SCREAMING_SNAKE_CASE		)
														cpu_target.target.set_x(cpu_target.target.get_x() + 0.1		)
										elif i == 3:
														cpu_target.target.next_to(cpu_targs[0].target						,direction=_SCREAMING_SNAKE_CASE						,buff=0.0		)
										else:
														cpu_target.target.next_to(cpu_targs[i - 1].target						,direction=_SCREAMING_SNAKE_CASE						,buff=0.0		)
										cpu_targs.append(_SCREAMING_SNAKE_CASE		)

										first_animations.append(rect.animate(run_time=0.5		).set_stroke(_SCREAMING_SNAKE_CASE		)		)
										second_animations.append(MoveToTarget(_SCREAMING_SNAKE_CASE						,run_time=1.5		)		)

						self.play(*_SCREAMING_SNAKE_CASE		)
						self.play(*_SCREAMING_SNAKE_CASE		)

						self.wait()





