



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
UpperCAmelCase__       =	pytest.mark.integration


UpperCAmelCase__       =	{"comet"}
UpperCAmelCase__       =	importlib.util.find_spec("fairseq") is not None

UpperCAmelCase__       =	{"code_eval"}
UpperCAmelCase__       =	os.name == "nt"

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





def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			str:







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

			return wrapper





def 							A     (					_UpperCAmelCase    :       Optional[Any]   )    ->			List[Any]:







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

			return wrapper





def 							A     (					_UpperCAmelCase    :       Dict   )    ->			int:







			'''simple docstring'''
			@wraps(_UpperCAmelCase   )
			def wrapper(self    :       Tuple							,       _UpperCAmelCase    :       Optional[Any]   ):
						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 							A     (					)    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       [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       __lowerCAmelCase   (      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       :  Union[str, Any]			,		A       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       '[...]'
						_UpperCAmelCase			=       importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'			,		A)).module_path)
						_UpperCAmelCase			=       datasets.load.import_main_class(metric_module.__name__			,		dataset=A)
						# check parameters
						_UpperCAmelCase			=       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:
															_UpperCAmelCase			=       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       :  List[Any]			,		A       :  Optional[int])       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       '[...]'
						_UpperCAmelCase			=       importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join('metrics'			,		A)).module_path)
						# run doctest
						with self.use_local_metrics():
									_UpperCAmelCase			=       doctest.testmod(A			,		verbose=A			,		raise_on_error=A)
						self.assertEqual(results.failed			,		0)
						self.assertGreater(results.attempted			,		1)



			@contextmanager
			def    _lowerCamelCase	(						self       :  str			,		A       :  List[Any]			,		A       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






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



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



						"""simple docstring"""






						def load_local_metric(A       :  str			,		*A       :  Union[str, Any]			,		**A       :  List[Any]):
									return load_metric(os.path.join('metrics'			,		A)			,		*A			,		**A)

						with patch('datasets.load_metric') as mock_load_metric:
									_UpperCAmelCase			=       load_local_metric
									yield



			@classmethod
			def    _lowerCamelCase	(						cls       :  List[Any]			,		A       :  str)       ->   str:



						"""simple docstring"""






						def wrapper(A       :  Union[str, Any]):
									_UpperCAmelCase			=       contextmanager(A)
									_UpperCAmelCase			=       patcher
									return patcher

						return wrapper





@LocalMetricTest.register_intensive_calls_patcher('bleurt'   )
def 							A     (					_UpperCAmelCase    :       List[str]   )    ->			List[Any]:







			'''simple docstring'''
			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

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







			class       __lowerCAmelCase   (      A						):



						def    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  Union[str, Any])       ->   Tuple:



									"""simple docstring"""






									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:
						_UpperCAmelCase			=       MockedPredictor()
						yield





@LocalMetricTest.register_intensive_calls_patcher('bertscore'   )
def 							A     (					_UpperCAmelCase    :       Optional[int]   )    ->			Dict:







			'''simple docstring'''
			import torch

			def bert_cos_score_idf(_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict							,       *_UpperCAmelCase    :       Union[str, Any]							,       **_UpperCAmelCase    :       str   ):
						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:
						_UpperCAmelCase			=       bert_cos_score_idf
						yield





@LocalMetricTest.register_intensive_calls_patcher('comet'   )
def 							A     (					_UpperCAmelCase    :       Tuple   )    ->			Optional[int]:







			'''simple docstring'''
			def load_from_checkpoint(_UpperCAmelCase    :       Any   ):







						class       __lowerCAmelCase   :



									def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  List[str]			,		*A       :  Dict			,		**A       :  Optional[Any])       ->   Union[str, Any]:



												"""simple docstring"""






												assert len(A) == 2
												_UpperCAmelCase			=       [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:
						_UpperCAmelCase			=       None
						with patch('comet.load_from_checkpoint'   ) as mock_load_from_checkpoint:
									_UpperCAmelCase			=       load_from_checkpoint
									yield





def 							A     (					)    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       load_metric(os.path.join('metrics'							,       'seqeval'   )   )
			_UpperCAmelCase			=       'ERROR'
			_UpperCAmelCase			=       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   )





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


UpperCAmelCase__       =	r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"







@add_start_docstrings(A						)
class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''rag'''
			UpperCamelCase        =					True



			def __init__(						self       :  str			,		A       :  int=None			,		A       :  Dict=True			,		A       :  int=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  Any=None			,		A       :  int=None			,		A       :  str=" / "			,		A       :  int=" // "			,		A       :  Any=5			,		A       :  Optional[Any]=3_00			,		A       :  Optional[Any]=7_68			,		A       :  Optional[int]=8			,		A       :  Union[str, Any]="wiki_dpr"			,		A       :  int="train"			,		A       :  Tuple="compressed"			,		A       :  str=None			,		A       :  Any=None			,		A       :  Tuple=False			,		A       :  List[str]=False			,		A       :  Tuple=0.0			,		A       :  Any=True			,		A       :  int=False			,		A       :  Dict=False			,		A       :  List[str]=False			,		A       :  Optional[Any]=True			,		A       :  Tuple=None			,		**A       :  Optional[Any]			,		)       ->   Optional[Any]:



						"""simple docstring"""






						super().__init__(
						    bos_token_id=A			,		pad_token_id=A			,		eos_token_id=A			,		decoder_start_token_id=A			,		forced_eos_token_id=A			,		is_encoder_decoder=A			,		prefix=A			,		vocab_size=A			,		**A			,		)
						assert (
						    "question_encoder" in kwargs and "generator" in kwargs
						), "Config has to be initialized with question_encoder and generator config"
						_UpperCAmelCase			=       kwargs.pop('question_encoder')
						_UpperCAmelCase			=       question_encoder_config.pop('model_type')
						_UpperCAmelCase			=       kwargs.pop('generator')
						_UpperCAmelCase			=       decoder_config.pop('model_type')

						from ..auto.configuration_auto import AutoConfig

						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)
						_UpperCAmelCase			=       AutoConfig.for_model(A			,		**A)

						_UpperCAmelCase			=       reduce_loss
						_UpperCAmelCase			=       label_smoothing
						_UpperCAmelCase			=       exclude_bos_score
						_UpperCAmelCase			=       do_marginalize

						_UpperCAmelCase			=       title_sep
						_UpperCAmelCase			=       doc_sep
						_UpperCAmelCase			=       n_docs
						_UpperCAmelCase			=       max_combined_length

						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       dataset_split
						_UpperCAmelCase			=       index_name

						_UpperCAmelCase			=       retrieval_vector_size
						_UpperCAmelCase			=       retrieval_batch_size
						_UpperCAmelCase			=       passages_path
						_UpperCAmelCase			=       index_path
						_UpperCAmelCase			=       use_dummy_dataset

						_UpperCAmelCase			=       output_retrieved

						_UpperCAmelCase			=       do_deduplication

						_UpperCAmelCase			=       use_cache

						if self.forced_eos_token_id is None:
									_UpperCAmelCase			=       getattr(self.generator			,		'forced_eos_token_id'			,		A)



			@classmethod
			def    _lowerCamelCase	(						cls       :  Union[str, Any]			,		A       :  PretrainedConfig			,		A       :  PretrainedConfig			,		**A       :  Optional[Any])       ->   PretrainedConfig:



						"""simple docstring"""






						return cls(question_encoder=question_encoder_config.to_dict()			,		generator=generator_config.to_dict()			,		**A)



			def    _lowerCamelCase	(						self       :  Dict)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       copy.deepcopy(self.__dict__)
						_UpperCAmelCase			=       self.question_encoder.to_dict()
						_UpperCAmelCase			=       self.generator.to_dict()
						_UpperCAmelCase			=       self.__class__.model_type
						return output
