





'''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
__lowerCamelCase : str											=			pytest.mark.integration


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

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

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







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

				return wrapper







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

				return wrapper







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				@wraps(lowerCAmelCase_							)
				def wrapper(self		,							lowerCAmelCase_							):
								if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
												self.skipTest("\"test not supported on Windows\""							)
								else:
												test_case(self		,							lowerCAmelCase_							)

				return wrapper







def 							UpperCAmelCase_    (	):

				"""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(
    UpperCamelCase_			,       UpperCamelCase_			,       UpperCamelCase_  )
@local
class       UpperCAmelCase (						parameterized.TestCase  ):
		UpperCAmelCase					:	str 			=		{}
		UpperCAmelCase					:	Tuple 			=		None




		@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"				)
		@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"				)
		def 				lowerCamelCase__   (self      :	Optional[Any]							,			A__      :	Optional[int]				)					-> int:
						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 				lowerCamelCase__   (self      :	int							,			A__      :	List[Any]				)					-> List[str]:
						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 				lowerCamelCase__   (self      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	List[Any]				)					-> List[Any]:






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




		@contextmanager
		def 				lowerCamelCase__   (self      :	Dict				)					-> Dict:






						def load_local_metric(A__      :	Optional[Any]							,			*A__      :	Optional[int]							,			**A__      :	Optional[int]				):
										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 				lowerCamelCase__   (cls      :	List[str]							,			A__      :	Dict				)					-> Any:






						def wrapper(A__      :	Optional[Any]				):
										lowercase         = contextmanager(__a				)
										lowercase         = patcher
										return patcher

						return wrapper







@LocalMetricTest.register_intensive_calls_patcher("bleurt"							)
def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

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

				class       UpperCAmelCase (						UpperCamelCase_  ):




						def 				lowerCamelCase__   (self      :	List[Any]							,			A__      :	Optional[int]				)					-> List[Any]:
										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         = MockedPredictor()
								yield







@LocalMetricTest.register_intensive_calls_patcher("bertscore"							)
def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				import torch

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

				# 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 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				def load_from_checkpoint(lowerCAmelCase_							):

								class       UpperCAmelCase :




										def 				lowerCamelCase__   (self      :	Optional[Any]							,			A__      :	List[str]							,			*A__      :	Tuple							,			**A__      :	List[str]				)					-> Any:
														assert len(__a				) == 2
														lowercase         = [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         = None
								with patch("comet.load_from_checkpoint"							) as mock_load_from_checkpoint:
												lowercase         = load_from_checkpoint
												yield







def 							UpperCAmelCase_    (	):

				"""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(lowerCAmelCase_		,							match=re.escape(lowerCAmelCase_							)							):
								metric.compute(predictions=[]		,							references=[]		,							scheme=lowerCAmelCase_							)











'''simple docstring'''

import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__lowerCamelCase : Any											=			{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

class       UpperCAmelCase (						nn.Module  ):




		def __init__(self      :	Tuple							,			A__      :	Any				)					-> str:
						super().__init__()
						lowercase         = torchvision.models.resnetaaa(pretrained=A__				)
						lowercase         = list(model.children()				)[:-2]
						lowercase         = nn.Sequential(*A__				)
						lowercase         = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





		def 				UpperCAmelCase__   (self      :	List[str]							,			A__      :	Optional[Any]				)					-> Optional[int]:
						# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
						lowercase         = self.pool(self.model(A__				)				)
						lowercase         = torch.flatten(A__							,			start_dim=2				)
						lowercase         = out.transpose(1							,			2				).contiguous()
						return out  # BxNx2048




class       UpperCAmelCase (						_lowercase  ):




		def __init__(self      :	int							,			A__      :	Optional[int]							,			A__      :	Optional[Any]							,			A__      :	List[Any]							,			A__      :	Tuple							,			A__      :	Optional[int]				)					-> Union[str, Any]:
						lowercase         = [json.loads(A__				) for l in open(A__				)]
						lowercase         = os.path.dirname(A__				)
						lowercase         = tokenizer
						lowercase         = labels
						lowercase         = len(A__				)
						lowercase         = max_seq_length

						lowercase         = transforms




		def __len__(self      :	List[str]				)					-> Dict:
						return len(self.data				)




		def __getitem__(self      :	int							,			A__      :	Union[str, Any]				)					-> List[str]:
						lowercase         = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]							,			add_special_tokens=A__				)				)
						lowercase   ,							lowercase   ,							lowercase         = sentence[0], sentence[1:-1], sentence[-1]
						lowercase         = sentence[: self.max_seq_length]

						lowercase         = torch.zeros(self.n_classes				)
						lowercase         = 1

						lowercase         = Image.open(os.path.join(self.data_dir							,			self.data[index]["img"]				)				).convert("RGB"				)
						lowercase         = self.transforms(A__				)

						return {
						    "image_start_token": start_token,
						    "image_end_token": end_token,
						    "sentence": sentence,
						    "image": image,
						    "label": label,
						}





		def 				UpperCAmelCase__   (self      :	str				)					-> str:
						lowercase         = Counter()
						for row in self.data:
										label_freqs.update(row["label"]				)
						return label_freqs







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = [len(row["sentence"]							) for row in batch]
				lowercase   ,							lowercase         = len(lowerCAmelCase_							), max(lowerCAmelCase_							)

				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)
				lowercase         = torch.zeros(lowerCAmelCase_		,							lowerCAmelCase_		,							dtype=torch.long							)

				for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase_		,							lowerCAmelCase_							)							):
								lowercase         = input_row["sentence"]
								lowercase         = 1

				lowercase         = torch.stack([row["image"] for row in batch]							)
				lowercase         = torch.stack([row["label"] for row in batch]							)
				lowercase         = torch.stack([row["image_start_token"] for row in batch]							)
				lowercase         = torch.stack([row["image_end_token"] for row in batch]							)

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				return transforms.Compose(
				    [
				        transforms.Resize(256							),
				        transforms.CenterCrop(224							),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]		,							std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]		,							),
				    ]							)




