

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__				=		pytest.mark.integration


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

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

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



def    __UpperCAmelCase (	__a      :			List[Any]	)			->     Tuple:

					"""simple docstring"""

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

					return wrapper



def    __UpperCAmelCase (	__a      :			Optional[int]	)			->     List[Any]:

					"""simple docstring"""

					@wraps(__a	)
					def wrapper(self      :			int		,__a      :			Union[str, Any]	):
										if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
															self.skipTest('''"test requires transformers"'''	)
										else:
															test_case(self		,__a	)

					return wrapper



def    __UpperCAmelCase (	__a      :			Dict	)			->     Tuple:

					"""simple docstring"""

					@wraps(__a	)
					def wrapper(self      :			int		,__a      :			Optional[Any]	):
										if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
															self.skipTest('''"test not supported on Windows"'''	)
										else:
															test_case(self		,__a	)

					return wrapper



def    __UpperCAmelCase (	)			->     List[Any]:

					"""simple docstring"""

					_a	:    List[str]         =		[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   ):
							"""simple docstring"""







							UpperCAmelCase__       :						int	    =    {}
							UpperCAmelCase__       :						List[str]	    =    None


							@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''       )
							@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''       )
							def 				__lowercase				(			self       ,							_a       )    ->    Tuple:
												_a	:    List[Any]         =		'''[...]'''
												_a	:    List[str]         =		importlib.import_module(
												    datasets.load.metric_module_factory(os.path.join('''metrics'''       ,							_SCREAMING_SNAKE_CASE       )       ).module_path       )
												_a	:    Union[str, Any]         =		datasets.load.import_main_class(metric_module.__name__       ,							dataset=_SCREAMING_SNAKE_CASE       )
												# check parameters
												_a	:    str         =		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	:    Union[str, Any]         =		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 				__lowercase				(			self       ,							_a       )    ->    str:
												_a	:    List[Any]         =		'''[...]'''
												_a	:    Optional[Any]         =		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	:    int         =		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 				__lowercase				(			self       ,							_a       ,							_a       )    ->    Dict:





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


							@contextmanager
							def 				__lowercase				(			self       )    ->    Optional[int]:





												def load_local_metric(_a       ,							*_a       ,							**_a       ):
																	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	:    str         =		load_local_metric
																	yield




							@classmethod
							def 				__lowercase				(			cls       ,							_a       )    ->    List[str]:





												def wrapper(_a       ):
																	_a	:    List[str]         =		contextmanager(_SCREAMING_SNAKE_CASE       )
																	_a	:    int         =		patcher
																	return patcher

												return wrapper



@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''	)
def    __UpperCAmelCase (	__a      :			Union[str, Any]	)			->     Optional[int]:

					"""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   ):
												"""simple docstring"""







												def 				__lowercase				(			self       ,							_a       )    ->    List[Any]:
																	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	:    List[str]         =		MockedPredictor()
										yield



@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''	)
def    __UpperCAmelCase (	__a      :			Union[str, Any]	)			->     Any:

					"""simple docstring"""

					import torch

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

					# 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	:    Optional[int]         =		bert_cos_score_idf
										yield



@LocalMetricTest.register_intensive_calls_patcher('''comet'''	)
def    __UpperCAmelCase (	__a      :			Dict	)			->     str:

					"""simple docstring"""

					def load_from_checkpoint(__a      :			Optional[int]	):

										class 				UpperCAmelCase_		:
																	"""simple docstring"""







																	def 				__lowercase				(			self       ,							_a       ,							*_a       ,							**_a       )    ->    str:
																						assert len(_SCREAMING_SNAKE_CASE       ) == 2
																						_a	:    int         =		[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	:    List[Any]         =		None
										with patch('''comet.load_from_checkpoint'''	) as mock_load_from_checkpoint:
															_a	:    Dict         =		load_from_checkpoint
															yield







def    __UpperCAmelCase (	)			->     List[Any]:

					"""simple docstring"""

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







def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				List[Any]		     =					current_set.copy()
  for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					row[0]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      if magnitude == 0:
        A_      :				Union[str, Any]		     =					column
        continue
      A_      :				Dict		     =					column / magnitude
    # Subtract to cancel term
  A_      :				Union[str, Any]		     =					current_set[0]
  A_      :				Tuple		     =					[first_row]
  A_      :				int		     =					current_set[1::]
  for row in current_set:
    A_      :				Tuple		     =					[]
    # If first term is 0, it is already in form we want, so we preserve it
    if row[0] == 0:
      final_set.append(SCREAMING_SNAKE_CASE       )
      continue
    for column_index in range(len(SCREAMING_SNAKE_CASE       )       ):
      temp_row.append(first_row[column_index] - row[column_index]       )
    final_set.append(SCREAMING_SNAKE_CASE       )
  # Create next recursion iteration set
  if len(final_set[0]       ) != 3:
    A_      :				Optional[Any]		     =					final_set[0]
    A_      :				Any		     =					[]
    A_      :				str		     =					[]
    for row in final_set[1::]:
      current_first_column.append(row[0]       )
      next_iteration.append(row[1::]       )
    A_      :				Optional[Any]		     =					simplify(SCREAMING_SNAKE_CASE       )
    for i in range(len(SCREAMING_SNAKE_CASE       )       ):
      resultant[i].insert(0 ,			current_first_column[i]       )
    resultant.insert(0 ,			SCREAMING_SNAKE_CASE       )
    A_      :				List[Any]		     =					resultant
  return final_set


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if len(SCREAMING_SNAKE_CASE       ) == 0:
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  A_      :				str		     =					len(SCREAMING_SNAKE_CASE       ) + 1
  if any(len(SCREAMING_SNAKE_CASE       ) != _length for item in equations       ):
    raise IndexError('''solve_simultaneous() requires n lists of length n+1'''       )
  for row in equations:
    if any(not isinstance(SCREAMING_SNAKE_CASE ,			(int, float)       ) for column in row       ):
      raise ValueError('''solve_simultaneous() requires lists of integers'''       )
  if len(SCREAMING_SNAKE_CASE       ) == 1:
    return [equations[0][-1] / equations[0][0]]
  A_      :				Dict		     =					equations.copy()
  if any(0 in row for row in data_set       ):
    A_      :				Tuple		     =					data_set.copy()
    A_      :				Optional[Any]		     =					[]
    for row_index, row in enumerate(SCREAMING_SNAKE_CASE       ):
      if 0 not in row:
        A_      :				str		     =					data_set.pop(SCREAMING_SNAKE_CASE       )
        break
    if not full_row:
      raise ValueError('''solve_simultaneous() requires at least 1 full equation'''       )
    data_set.insert(0 ,			SCREAMING_SNAKE_CASE       )
  A_      :				int		     =					data_set.copy()
  A_      :				Dict		     =					simplify(SCREAMING_SNAKE_CASE       )
  A_      :				Dict		     =					simplified[::-1]
  A_      :				list		     =					[]
  for row in simplified:
    A_      :				Union[str, Any]		     =					row[-1]
    if not solutions:
      if row[-2] == 0:
        solutions.append(0       )
        continue
      solutions.append(current_solution / row[-2]       )
      continue
    A_      :				Optional[Any]		     =					row.copy()[: len(SCREAMING_SNAKE_CASE       ) - 1 :]
    while temp_row[0] == 0:
      temp_row.pop(0       )
    if len(SCREAMING_SNAKE_CASE       ) == 0:
      solutions.append(0       )
      continue
    A_      :				int		     =					temp_row[1::]
    A_      :				int		     =					temp_row[::-1]
    for column_index, column in enumerate(SCREAMING_SNAKE_CASE       ):
      current_solution -= column * solutions[column_index]
    solutions.append(SCREAMING_SNAKE_CASE       )
  A_      :				Union[str, Any]		     =					[]
  for item in solutions:
    final.append(float(round(SCREAMING_SNAKE_CASE ,			5       )       )       )
  return final[::-1]


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     UpperCamelCase								=	[
         [2, 1, 1, 1, 1, 4],
         [1, 2, 1, 1, 1, 5],
         [1, 1, 2, 1, 1, 6],
         [1, 1, 1, 2, 1, 7],
         [1, 1, 1, 1, 2, 8],
     ]
     print(solve_simultaneous(eq))
     print(solve_simultaneous([[4, 2]]))




