




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

			return wrapper







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

			return wrapper







def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			@wraps(__snake_case     )
			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     ,							__snake_case     )

			return wrapper







def 			__UpperCAmelCase			(      ):
			__lowercase       :   Optional[Any]        =    [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						):
		UpperCamelCase						 ={}
		UpperCamelCase						 =None
		@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''    )
		@pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Optional[Any]:
					__lowercase       :   Any        =    "[...]"
					__lowercase       :   Dict        =    importlib.import_module(
					    datasets.load.metric_module_factory(os.path.join('''metrics'''							,		_a    )    ).module_path    )
					__lowercase       :   Union[str, Any]        =    datasets.load.import_main_class(metric_module.__name__							,		dataset=_a    )
					# check parameters
					__lowercase       :   List[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(_a							,		metric_module.__name__    ):
								with self.use_local_metrics():
											try:
														__lowercase       :   Union[str, Any]        =    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      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Tuple:
					__lowercase       :   Optional[Any]        =    "[...]"
					__lowercase       :   int        =    importlib.import_module(
					    datasets.load.metric_module_factory(os.path.join('''metrics'''							,		_a    )    ).module_path    )
					# run doctest
					with self.use_local_metrics():
								__lowercase       :   Any        =    doctest.testmod(_a							,		verbose=_a							,		raise_on_error=_a    )
					self.assertEqual(results.failed							,		0    )
					self.assertGreater(results.attempted							,		1    )
		@contextmanager
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Dict:
					if metric_name in self.INTENSIVE_CALLS_PATCHER:
								with self.INTENSIVE_CALLS_PATCHER[metric_name](_a    ):
											yield
					else:
								yield
		@contextmanager
		def      _lowerCamelCase				(		self    )		->    int:
					def load_local_metric(UpperCamelCase_							,		*UpperCamelCase_							,		**UpperCamelCase_    ):
								return load_metric(os.path.join('''metrics'''							,		_a    )							,		*_a							,		**_a    )

					with patch('''datasets.load_metric'''    ) as mock_load_metric:
								__lowercase       :   Any        =    load_local_metric
								yield
		@classmethod
		def      _lowerCamelCase				(		cls							,		UpperCamelCase_    )		->    Optional[Any]:
					def wrapper(UpperCamelCase_    ):
								__lowercase       :   int        =    contextmanager(_a    )
								__lowercase       :   Optional[int]        =    patcher
								return patcher

					return wrapper







@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''     )
def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

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

			class    UpperCAmelCase_  (       a_						):
					def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Dict:
								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:
						__lowercase       :   str        =    MockedPredictor()
						yield







@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''     )
def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			import torch

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

			# 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       :   Any        =    bert_cos_score_idf
						yield







@LocalMetricTest.register_intensive_calls_patcher('''comet'''     )
def 			__UpperCAmelCase			(      __UpperCamelCase     ):
			def load_from_checkpoint(__UpperCamelCase     ):

						class    UpperCAmelCase_  :
								def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		*UpperCamelCase_							,		**UpperCamelCase_    )		->    Tuple:
											assert len(_a    ) == 2
											__lowercase       :   str        =    [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:
						__lowercase       :   Optional[Any]        =    None
						with patch('''comet.load_from_checkpoint'''     ) as mock_load_from_checkpoint:
									__lowercase       :   int        =    load_from_checkpoint
									yield







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





"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


