
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
_snake_case      :							Union[str, Any]     	=    pytest.mark.integration


_snake_case      :							int     	=    {"comet"}
_snake_case      :							int     	=    importlib.util.find_spec("fairseq") is not None

_snake_case      :							Optional[Any]     	=    {"code_eval"}
_snake_case      :							int     	=    os.name == "nt"

_snake_case      :							int     	=    {"bertscore", "frugalscore", "perplexity"}
_snake_case      :							Optional[int]     	=    importlib.util.find_spec("transformers") is not None

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				@wraps(UpperCamelCase__						)
				def wrapper(self		,    __magic_name__						):
								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							(	__magic_name__						):







				'''simple docstring'''


				@wraps(UpperCamelCase__						)
				def wrapper(self		,    __magic_name__						):
								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							(	__magic_name__						):







				'''simple docstring'''


				@wraps(UpperCamelCase__						)
				def wrapper(self		,    __magic_name__						):
								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							(	):







				'''simple docstring'''


				lowercase         =      [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(
    _a		,    _a		,    _a		)
@local
class   UpperCamelCase_      (      parameterized.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Union[str, Any]				   =					{}
						UpperCamelCase      :  Any				   =					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							:Any		,    lowerCAmelCase__							:Optional[int]     )  ->str:
										lowercase         =      "[...]"
										lowercase         =      importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"		,    A_     )     ).module_path     )
										lowercase         =      datasets.load.import_main_class(metric_module.__name__		,    dataset=A_     )
										# check parameters
										lowercase         =      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(A_		,    metric_module.__name__     ):
														with self.use_local_metrics():
																		try:
																						lowercase         =      doctest.testmod(A_		,    verbose=A_		,    raise_on_error=A_     )
																		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							:Dict		,    lowerCAmelCase__							:Optional[Any]     )  ->List[Any]:
										lowercase         =      "[...]"
										lowercase         =      importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"		,    A_     )     ).module_path     )
										# run doctest
										with self.use_local_metrics():
														lowercase         =      doctest.testmod(A_		,    verbose=A_		,    raise_on_error=A_     )
										self.assertEqual(results.failed		,    0     )
										self.assertGreater(results.attempted		,    1     )





						@contextmanager
						def        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->Any:


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





						@contextmanager
						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Optional[Any]:


										def load_local_metric(lowerCAmelCase__							:Optional[int]		,    *lowerCAmelCase__							:List[str]		,    **lowerCAmelCase__							:List[Any]     ):
														return load_metric(os.path.join("metrics"		,    A_     )		,    *A_		,    **A_     )

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





						@classmethod
						def        SCREAMING_SNAKE_CASE(			cls							:List[Any]		,    lowerCAmelCase__							:Any     )  ->Any:


										def wrapper(lowerCAmelCase__							:Dict     ):
														lowercase         =      contextmanager(A_     )
														lowercase         =      patcher
														return patcher

										return wrapper




@LocalMetricTest.register_intensive_calls_patcher("bleurt"						)
def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


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

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





				class   UpperCamelCase_      (      _a		):







										'''simple docstring'''



										def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:Optional[int]     )  ->Optional[int]:
														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:
								lowercase         =      MockedPredictor()
								yield

@LocalMetricTest.register_intensive_calls_patcher("bertscore"						)
def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				import torch

				def bert_cos_score_idf(__magic_name__		,    __magic_name__		,    *__magic_name__		,    **__magic_name__						):
								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:
								lowercase         =      bert_cos_score_idf
								yield

@LocalMetricTest.register_intensive_calls_patcher("comet"						)
def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				def load_from_checkpoint(__magic_name__						):





								class   UpperCamelCase_      :







														'''simple docstring'''



														def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:List[Any]		,    *lowerCAmelCase__							:Any		,    **lowerCAmelCase__							:Optional[Any]     )  ->int:
																		assert len(A_     ) == 2
																		lowercase         =      [0.19, 0.92]
																		return scores, sum(A_     ) / len(A_     )




								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:
								lowercase         =      None
								with patch("comet.load_from_checkpoint"						) as mock_load_from_checkpoint:
												lowercase         =      load_from_checkpoint
												yield




def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      load_metric(os.path.join("metrics"		,    "seqeval"						)						)
				lowercase         =      "ERROR"
				lowercase         =      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__						)




"""simple docstring"""

class  lowerCamelCase__    :
      def __init__(		self						:						Optional[Any]   ,							A_						:						Dict   ,							A_						:						str   ,							A_						:						Any				):






       '''simple docstring'''


       __lowercase						=						None
       __lowercase						=						None
       __lowercase						=						graph

       self._normalize_graph(A_   ,							A_				)
       __lowercase						=						len(A_				)
       __lowercase						=						None
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str   ,							A_						:						List[Any]				):






       '''simple docstring'''


       if sources is int:
        __lowercase						=						[sources]
       if sinks is int:
        __lowercase						=						[sinks]

       if len(A_				) == 0 or len(A_				) == 0:
        return

       __lowercase						=						sources[0]
       __lowercase						=						sinks[0]

       # make fake vertex if there are more
       # than one source or sink
       if len(A_				) > 1 or len(A_				) > 1:
        __lowercase						=						0
        for i in sources:
         max_input_flow += sum(self.graph[i]				)

        __lowercase						=						len(self.graph				) + 1
        for room in self.graph:
         room.insert(0   ,							0				)
        self.graph.insert(0   ,							[0] * size				)
        for i in sources:
         __lowercase						=						max_input_flow
        __lowercase						=						0

        __lowercase						=						len(self.graph				) + 1
        for room in self.graph:
         room.append(0				)
        self.graph.append([0] * size				)
        for i in sinks:
         __lowercase						=						max_input_flow
        __lowercase						=						size - 1
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       if self.maximum_flow_algorithm is None:
        raise Exception("""You need to set maximum flow algorithm before."""				)
       if self.source_index is None or self.sink_index is None:
        return 0

       self.maximum_flow_algorithm.execute()
       return self.maximum_flow_algorithm.getMaximumFlow()






      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						algorithm(self				)






class  lowerCamelCase__    :
      def __init__(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						flow_network
       __lowercase						=						flow_network.verticesCount
       __lowercase						=						flow_network.sourceIndex
       __lowercase						=						flow_network.sinkIndex
       # it's just a reference, so you shouldn't change
       # it in your algorithms, use deep copy before doing that
       __lowercase						=						flow_network.graph
       __lowercase						=						False
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       if not self.executed:
        self._algorithm()
        __lowercase						=						True






      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       pass






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Union[str, Any]   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       # use this to save your result
       __lowercase						=						-1






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       if not self.executed:
        raise Exception("""You should execute algorithm before using its result!"""				)

       return self.maximum_flow






class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						List[str]   ,							A_						:						Tuple				):






       '''simple docstring'''


       super().__init__(A_				)

       __lowercase						=						[[0] * self.verticies_count for i in range(self.verticies_count				)]

       __lowercase						=						[0] * self.verticies_count
       __lowercase						=						[0] * self.verticies_count
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.verticies_count

       # push some substance to graph
       for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]				):
        self.preflow[self.source_index][nextvertex_index] += bandwidth
        self.preflow[nextvertex_index][self.source_index] -= bandwidth
        self.excesses[nextvertex_index] += bandwidth

       # Relabel-to-front selection rule
       __lowercase						=						[
           i
           for i in range(self.verticies_count				)
           if i != self.source_index and i != self.sink_index
       ]

       # move through list
       __lowercase						=						0
       while i < len(A_				):
        __lowercase						=						vertices_list[i]
        __lowercase						=						self.heights[vertex_index]
        self.process_vertex(A_				)
        if self.heights[vertex_index] > previous_height:
         # if it was relabeled, swap elements
         # and start from 0 index
         vertices_list.insert(0   ,							vertices_list.pop(A_				)				)
         __lowercase						=						0
        else:
         i += 1

       __lowercase						=						sum(self.preflow[self.source_index]				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       while self.excesses[vertex_index] > 0:
        for neighbour_index in range(self.verticies_count				):
         # if it's neighbour and current vertex is higher
         if (
             self.graph[vertex_index][neighbour_index]
             - self.preflow[vertex_index][neighbour_index]
             > 0
             and self.heights[vertex_index] > self.heights[neighbour_index]
         ):
          self.push(A_   ,							A_				)

        self.relabel(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Union[str, Any]   ,							A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						min(
           self.excesses[from_index]   ,							self.graph[from_index][to_index] - self.preflow[from_index][to_index]   ,							)
       self.preflow[from_index][to_index] += preflow_delta
       self.preflow[to_index][from_index] -= preflow_delta
       self.excesses[from_index] -= preflow_delta
       self.excesses[to_index] += preflow_delta






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						None
       for to_index in range(self.verticies_count				):
        if (
            self.graph[vertex_index][to_index]
            - self.preflow[vertex_index][to_index]
            > 0
        ) and (min_height is None or self.heights[to_index] < min_height):
         __lowercase						=						self.heights[to_index]

       if min_height is not None:
        __lowercase						=						min_height + 1


if __name__ == "__main__":
       UpperCAmelCase__					=[0]
       UpperCAmelCase__					=[3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       UpperCAmelCase__					=[[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       UpperCAmelCase__					=FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       UpperCAmelCase__					=flow_network.find_maximum_flow()

       print(f"""maximum flow is {maximum_flow}""")
