


"""simple docstring"""


import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
	from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							def      snake_case							(	self       ,  __a      ):
										for model_result in results.values():
													for batch_size, sequence_length in zip(model_result["bs"]       ,  model_result["ss"]      ):
																__lowerCAmelCase							       =     model_result["result"][batch_size][sequence_length]
																self.assertIsNotNone(__a      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sgugger/tiny-distilbert-classification"
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  only_pretrain_model=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  torchscript=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							@unittest.skipIf(torch_device == "cpu"       ,  "Cant do half precision"      )
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  fpaa=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     AutoConfig.from_pretrained(__a      )
										# set architectures equal to `None`
										__lowerCAmelCase							       =     None
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a       ,  configs=[config]      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result      )
										self.check_results_dict_not_empty(results.memory_train_result      )

							@unittest.skipIf(torch_device == "cpu"       ,  "Can't do half precision"      )
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  fpaa=__a       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result      )
										self.check_results_dict_not_empty(results.memory_train_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     AutoConfig.from_pretrained(__a      )
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a       ,  configs=[config]      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tinier_bart"
										__lowerCAmelCase							       =     AutoConfig.from_pretrained(__a      )
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a       ,  configs=[config]      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_inference_result      )
										self.check_results_dict_not_empty(results.memory_inference_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										__lowerCAmelCase							       =     AutoConfig.from_pretrained(__a      )
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a       ,  configs=[config]      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result      )
										self.check_results_dict_not_empty(results.memory_train_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tinier_bart"
										__lowerCAmelCase							       =     AutoConfig.from_pretrained(__a      )
										__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
										    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  multi_process=__a       ,  )
										__lowerCAmelCase							       =     PyTorchBenchmark(__a       ,  configs=[config]      )
										__lowerCAmelCase							       =     benchmark.run()
										self.check_results_dict_not_empty(results.time_train_result      )
										self.check_results_dict_not_empty(results.memory_train_result      )

							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"
										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
													    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  save_to_csv=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  inference_time_csv_file=os.path.join(__a       ,  "inf_time.csv"      )       ,  train_memory_csv_file=os.path.join(__a       ,  "train_mem.csv"      )       ,  inference_memory_csv_file=os.path.join(__a       ,  "inf_mem.csv"      )       ,  train_time_csv_file=os.path.join(__a       ,  "train_time.csv"      )       ,  env_info_csv_file=os.path.join(__a       ,  "env.csv"      )       ,  multi_process=__a       ,  )
													__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
													benchmark.run()
													self.assertTrue(Path(os.path.join(__a       ,  "inf_time.csv"      )      ).exists()      )
													self.assertTrue(Path(os.path.join(__a       ,  "train_time.csv"      )      ).exists()      )
													self.assertTrue(Path(os.path.join(__a       ,  "inf_mem.csv"      )      ).exists()      )
													self.assertTrue(Path(os.path.join(__a       ,  "train_mem.csv"      )      ).exists()      )
													self.assertTrue(Path(os.path.join(__a       ,  "env.csv"      )      ).exists()      )



							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     "sshleifer/tiny-gpt2"

										def _check_summary_is_not_empty(__a      ):
													self.assertTrue(hasattr(__a       ,  "sequential"      )      )
													self.assertTrue(hasattr(__a       ,  "cumulative"      )      )
													self.assertTrue(hasattr(__a       ,  "current"      )      )
													self.assertTrue(hasattr(__a       ,  "total"      )      )

										with tempfile.TemporaryDirectory() as tmp_dir:
													__lowerCAmelCase							       =     PyTorchBenchmarkArguments(
													    models=[MODEL_ID]       ,  training=__a       ,  inference=__a       ,  sequence_lengths=[8]       ,  batch_sizes=[1]       ,  log_filename=os.path.join(__a       ,  "log.txt"      )       ,  log_print=__a       ,  trace_memory_line_by_line=__a       ,  multi_process=__a       ,  )
													__lowerCAmelCase							       =     PyTorchBenchmark(__a      )
													__lowerCAmelCase							       =     benchmark.run()
													_check_summary_is_not_empty(result.inference_summary      )
													_check_summary_is_not_empty(result.train_summary      )
													self.assertTrue(Path(os.path.join(__a       ,  "log.txt"      )      ).exists()      )








"""simple docstring"""


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[Any]            =     {"comet"}
A	:  int            =     importlib.util.find_spec("fairseq") is not None

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

A	:  Dict            =     {"bertscore", "frugalscore", "perplexity"}
A	:  Any            =     importlib.util.find_spec("transformers") is not None


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

			return wrapper


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

			return wrapper


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			@wraps(_UpperCamelCase )
			def wrapper(self       ,					_UpperCamelCase ):
						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 _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     [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(
    lowerCAmelCase__			,lowerCAmelCase__			,lowerCAmelCase__						)
@local
class     _UpperCamelCase    (  parameterized.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       Any							 ={}
							__UpperCAmelCase		:       List[str]							 =None

							@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"      )
							@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"      )
							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "[...]"
										__lowerCAmelCase							       =     importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"       ,  __a      )      ).module_path      )
										__lowerCAmelCase							       =     datasets.load.import_main_class(metric_module.__name__       ,  dataset=__a      )
										# check parameters
										__lowerCAmelCase							       =     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:
																			__lowerCAmelCase							       =     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      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     "[...]"
										__lowerCAmelCase							       =     importlib.import_module(
										    datasets.load.metric_module_factory(os.path.join("metrics"       ,  __a      )      ).module_path      )
										# run doctest
										with self.use_local_metrics():
													__lowerCAmelCase							       =     doctest.testmod(__a       ,  verbose=__a       ,  raise_on_error=__a      )
										self.assertEqual(results.failed       ,  0      )
										self.assertGreater(results.attempted       ,  1      )

							@contextmanager
							def      snake_case							(	self       ,  __a       ,  __a      ):
										if metric_name in self.INTENSIVE_CALLS_PATCHER:
													with self.INTENSIVE_CALLS_PATCHER[metric_name](__a      ):
																yield
										else:
													yield

							@contextmanager
							def      snake_case							(	self      ):
										def load_local_metric(__a       ,  *__a       ,  **__a      ):
													return load_metric(os.path.join("metrics"       ,  __a      )       ,  *__a       ,  **__a      )

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



							@classmethod
							def      snake_case							(	cls       ,  __a      ):
										def wrapper(__a      ):
													__lowerCAmelCase							       =     contextmanager(__a      )
													__lowerCAmelCase							       =     patcher
													return patcher

										return wrapper


@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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

			tf.flags.DEFINE_string("sv"       ,					""       ,					"" )  # handle pytest cli flags
			class     _UpperCamelCase    (  lowerCAmelCase__						):
										'''simple docstring'''







										def      snake_case							(	self       ,  __a      ):
													assert len(input_dict["input_ids"]      ) == 2
													return np.array([1.0_3, 1.0_4]      )

    # 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:
						__lowerCAmelCase							       =     MockedPredictor()
						yield


@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			import torch

			def bert_cos_score_idf(_UpperCamelCase       ,					_UpperCamelCase       ,					*_UpperCamelCase       ,					**_UpperCamelCase ):
						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:
						__lowerCAmelCase							       =     bert_cos_score_idf
						yield


@LocalMetricTest.register_intensive_calls_patcher("comet" )
def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			def load_from_checkpoint(_UpperCamelCase ):
						class     _UpperCamelCase    :
													'''simple docstring'''







													def      snake_case							(	self       ,  __a       ,  *__a       ,  **__a      ):
																assert len(__a      ) == 2
																__lowerCAmelCase							       =     [0.1_9, 0.9_2]
																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:
						__lowerCAmelCase							       =     None
						with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
									__lowerCAmelCase							       =     load_from_checkpoint
									yield


def _lowerCamelCase      (						):

			'''simple docstring'''




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




