import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
 from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
 UpperCAmelCase     :   str					=				None

if is_ray_available():
 import ray  # noqa: E402 # isort:skip
 from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
 UpperCAmelCase     :   Optional[int]					=				None
 UpperCAmelCase     :   Optional[int]					=				None
 UpperCAmelCase     :   Optional[int]					=				None


def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[Any]				):
       if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
              _UpperCamelCase     :       List[Any]  						=  unittest.skip('test requires Datasets, Faiss, psutil'				)(lowercase_				)
       return test_case





@require_distributed_retrieval
class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''

 def 					__snake_case					(			self       :		str  ) ->							str:
        _UpperCamelCase     :       Tuple  						=  tempfile.mkdtemp()
        _UpperCamelCase     :       int  						=  8

        # DPR tok
        _UpperCamelCase     :       Any  						=  [
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            'want',
            '##want',
            '##ed',
            'wa',
            'un',
            'runn',
            '##ing',
            ',',
            'low',
            'lowest',
        ]
        _UpperCamelCase     :       List[Any]  						=  os.path.join(self.tmpdirname       ,			'dpr_tokenizer'  )
        os.makedirs(lowerCamelCase       ,			exist_ok=lowerCamelCase  )
        _UpperCamelCase     :       Dict  						=  os.path.join(lowerCamelCase       ,			DPR_VOCAB_FILES_NAMES['vocab_file']  )
        with open(self.vocab_file       ,			'w'       ,			encoding='utf-8'  ) as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]  )  )

        # BART tok
        _UpperCamelCase     :       int  						=  [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        _UpperCamelCase     :       Tuple  						=  dict(zip(lowerCamelCase       ,			range(len(lowerCamelCase  )  )  )  )
        _UpperCamelCase     :       str  						=  ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        _UpperCamelCase     :       List[Any]  						=  {'unk_token': '<unk>'}

        _UpperCamelCase     :       str  						=  os.path.join(self.tmpdirname       ,			'bart_tokenizer'  )
        os.makedirs(lowerCamelCase       ,			exist_ok=lowerCamelCase  )
        _UpperCamelCase     :       Optional[int]  						=  os.path.join(lowerCamelCase       ,			BART_VOCAB_FILES_NAMES['vocab_file']  )
        _UpperCamelCase     :       List[Any]  						=  os.path.join(lowerCamelCase       ,			BART_VOCAB_FILES_NAMES['merges_file']  )
        with open(self.vocab_file       ,			'w'       ,			encoding='utf-8'  ) as fp:
               fp.write(json.dumps(lowerCamelCase  ) + '\n'  )
        with open(self.merges_file       ,			'w'       ,			encoding='utf-8'  ) as fp:
               fp.write('\n'.join(lowerCamelCase  )  )

 def 					__snake_case					(			self       :		List[str]  ) ->							DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,			'dpr_tokenizer'  )  )

 def 					__snake_case					(			self       :		str  ) ->							BartTokenizer:
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,			'bart_tokenizer'  )  )

 def 					__snake_case					(			self       :		Union[str, Any]  ) ->							Tuple:
        shutil.rmtree(self.tmpdirname  )

 def 					__snake_case					(			self       :		int  ) ->							Tuple:
        _UpperCamelCase     :       Union[str, Any]  						=  Dataset.from_dict(
            {
                'id': ['0', '1'],
                'text': ['foo', 'bar'],
                'title': ['Foo', 'Bar'],
                'embeddings': [np.ones(self.retrieval_vector_size  ), 2 * np.ones(self.retrieval_vector_size  )],
            }  )
        dataset.add_faiss_index('embeddings'       ,			string_factory='Flat'       ,			metric_type=faiss.METRIC_INNER_PRODUCT  )
        return dataset

 def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		Any=1_2_3_4_5  ) ->							RagPyTorchDistributedRetriever:
        _UpperCamelCase     :       Dict  						=  self.get_dummy_dataset()
        _UpperCamelCase     :       str  						=  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			)
        with patch('transformers.models.rag.retrieval_rag.load_dataset'  ) as mock_load_dataset:
               _UpperCamelCase     :       Any  						=  dataset
               _UpperCamelCase     :       List[Any]  						=  RagPyTorchDistributedRetriever(
                   lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			)
               if init_retrieval:
                      retriever.init_retrieval(lowerCamelCase  )
        return retriever

 def 					__snake_case					(			self       :		Tuple       ,			lowerCamelCase       :		bool  ) ->							RagRayDistributedRetriever:
        # Have to run in local mode because sys.path modifications at top of
        # file are not propogated to remote workers.
        # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
        ray.init(local_mode=lowerCamelCase  )
        _UpperCamelCase     :       Tuple  						=  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			)
        _UpperCamelCase     :       Tuple  						=  ray.remote(lowerCamelCase  )
        _UpperCamelCase     :       Any  						=  [remote_cls.remote() for _ in range(1  )]
        with patch('transformers.models.rag.retrieval_rag.load_dataset'  ) as mock_load_dataset:
               _UpperCamelCase     :       Any  						=  self.get_dummy_dataset()
               _UpperCamelCase     :       List[Any]  						=  RagRayDistributedRetriever(
                   lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			retrieval_workers=lowerCamelCase       ,			)
               if init_retrieval:
                      retriever.init_retrieval()
        return retriever

 def 					__snake_case					(			self       :		Dict       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		Union[str, Any]=1_2_3_4_5  ) ->							Optional[Any]:
        _UpperCamelCase     :       Tuple  						=  self.get_dummy_dataset()
        _UpperCamelCase     :       int  						=  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			index_name='custom'       ,			)
        if from_disk:
               _UpperCamelCase     :       Optional[Any]  						=  os.path.join(self.tmpdirname       ,			'dataset'  )
               _UpperCamelCase     :       Any  						=  os.path.join(self.tmpdirname       ,			'index.faiss'  )
               dataset.get_index('embeddings'  ).save(os.path.join(self.tmpdirname       ,			'index.faiss'  )  )
               dataset.drop_index('embeddings'  )
               dataset.save_to_disk(os.path.join(self.tmpdirname       ,			'dataset'  )  )
               del dataset
               _UpperCamelCase     :       Dict  						=  RagPyTorchDistributedRetriever(
                   lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			)
        else:
               _UpperCamelCase     :       Optional[Any]  						=  RagPyTorchDistributedRetriever(
                   lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			index=CustomHFIndex(config.retrieval_vector_size       ,			lowerCamelCase  )       ,			)
        if init_retrieval:
               retriever.init_retrieval(lowerCamelCase  )
        return retriever

 def 					__snake_case					(			self       :		List[Any]       ,			lowerCamelCase       :		bool       ,			lowerCamelCase       :		bool  ) ->							Any:
        # Have to run in local mode because sys.path modifications at top of
        # file are not propogated to remote workers.
        # https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
        ray.init(local_mode=lowerCamelCase  )
        _UpperCamelCase     :       List[Any]  						=  self.get_dummy_dataset()
        _UpperCamelCase     :       int  						=  RagConfig(
            retrieval_vector_size=self.retrieval_vector_size       ,			question_encoder=DPRConfig().to_dict()       ,			generator=BartConfig().to_dict()       ,			index_name='custom'       ,			)
        _UpperCamelCase     :       Dict  						=  ray.remote(lowerCamelCase  )
        _UpperCamelCase     :       List[Any]  						=  [remote_cls.remote() for _ in range(1  )]
        if from_disk:
               _UpperCamelCase     :       Union[str, Any]  						=  os.path.join(self.tmpdirname       ,			'dataset'  )
               _UpperCamelCase     :       List[str]  						=  os.path.join(self.tmpdirname       ,			'index.faiss'  )
               dataset.get_index('embeddings'  ).save(os.path.join(self.tmpdirname       ,			'index.faiss'  )  )
               dataset.drop_index('embeddings'  )
               dataset.save_to_disk(os.path.join(self.tmpdirname       ,			'dataset'  )  )
               del dataset
               _UpperCamelCase     :       Union[str, Any]  						=  RagRayDistributedRetriever(
                   lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			retrieval_workers=lowerCamelCase       ,			index=CustomHFIndex.load_from_disk(
                       vector_size=config.retrieval_vector_size       ,			dataset_path=config.passages_path       ,			index_path=config.index_path       ,			)       ,			)
        else:
               _UpperCamelCase     :       List[str]  						=  RagRayDistributedRetriever(
                   lowerCamelCase       ,			question_encoder_tokenizer=self.get_dpr_tokenizer()       ,			generator_tokenizer=self.get_bart_tokenizer()       ,			retrieval_workers=lowerCamelCase       ,			index=CustomHFIndex(config.retrieval_vector_size       ,			lowerCamelCase  )       ,			)
        if init_retrieval:
               retriever.init_retrieval()
        return retriever

 def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		RagRetriever       ,			lowerCamelCase       :		np.array       ,			lowerCamelCase       :		int  ) ->							None:
        _UpperCamelCase     :       Any  						=  retriever.retrieve(lowerCamelCase       ,			n_docs=lowerCamelCase  )
        self.assertEqual(retrieved_doc_embeds.shape       ,			(2, n_docs, self.retrieval_vector_size)  )
        self.assertEqual(len(lowerCamelCase  )       ,			2  )
        self.assertEqual(sorted(doc_dicts[0]  )       ,			['embeddings', 'id', 'text', 'title']  )
        self.assertEqual(len(doc_dicts[0]['id']  )       ,			lowerCamelCase  )
        self.assertEqual(doc_dicts[0]['id'][0]       ,			'1'  )  # max inner product is reached with second doc
        self.assertEqual(doc_dicts[1]['id'][0]       ,			'0'  )  # max inner product is reached with first doc
        self.assertListEqual(doc_ids.tolist()       ,			[[1], [0]]  )

 def 					__snake_case					(			self       :		str  ) ->							List[str]:
        _UpperCamelCase     :       str  						=  1
        _UpperCamelCase     :       Any  						=  np.array(
            [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )

        self.distributed_retriever_check(
            self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase  )

 def 					__snake_case					(			self       :		List[str]  ) ->							Optional[int]:
        _UpperCamelCase     :       Any  						=  1
        _UpperCamelCase     :       str  						=  np.array(
            [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )

        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase       ,			)

 def 					__snake_case					(			self       :		Dict  ) ->							Tuple:
        _UpperCamelCase     :       Union[str, Any]  						=  1
        _UpperCamelCase     :       Dict  						=  np.array(
            [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )

        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase       ,			)

 @require_ray
 def 					__snake_case					(			self       :		Dict  ) ->							List[Any]:
        _UpperCamelCase     :       Optional[Any]  						=  1
        _UpperCamelCase     :       Tuple  						=  np.array(
            [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )

        self.distributed_retriever_check(
            self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase  )
        ray.shutdown()

 @require_ray
 def 					__snake_case					(			self       :		int  ) ->							int:
        _UpperCamelCase     :       str  						=  1
        _UpperCamelCase     :       List[Any]  						=  np.array(
            [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )
        with self.assertRaises(lowerCamelCase  ):
               self.distributed_retriever_check(
                   self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase       ,			)
        ray.shutdown()

 @require_ray
 def 					__snake_case					(			self       :		List[str]  ) ->							Tuple:
        _UpperCamelCase     :       Tuple  						=  1
        _UpperCamelCase     :       List[Any]  						=  np.array(
            [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]       ,			dtype=np.floataa  )

        self.distributed_retriever_check(
            self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase       ,			from_disk=lowerCamelCase  )       ,			lowerCamelCase       ,			lowerCamelCase  )
        ray.shutdown()




import argparse
import json
import re
import string
import sys
from collections import Counter


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):

       def remove_articles(lowercase_:	Dict				):
              return re.sub(R'\b(a|an|the)\b'		,						' '		,						lowercase_				)

       def white_space_fix(lowercase_:	Optional[Any]				):
              return " ".join(text.split()				)

       def remove_punc(lowercase_:	List[str]				):
              _UpperCamelCase     :       str  						=  set(string.punctuation				)
              return "".join(ch for ch in text if ch not in exclude				)

       def lower(lowercase_:	List[str]				):
              return text.lower()

       return white_space_fix(remove_articles(remove_punc(lower(lowercase_				)				)				)				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]		,						lowercase_:	Tuple				):
       _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
       _UpperCamelCase     :       int  						=  normalize_answer(lowercase_				).split()
       _UpperCamelCase     :       Union[str, Any]  						=  Counter(lowercase_				) & Counter(lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  sum(common.values()				)
       if num_same == 0:
              return 0
       _UpperCamelCase     :       Optional[Any]  						=  1.0 * num_same / len(lowercase_				)
       _UpperCamelCase     :       Optional[int]  						=  1.0 * num_same / len(lowercase_				)
       _UpperCamelCase     :       List[str]  						=  (2 * precision * recall) / (precision + recall)
       return fa


def SCREAMING_SNAKE_CASE				(     lowercase_:	str		,						lowercase_:	Union[str, Any]				):
       return normalize_answer(lowercase_				) == normalize_answer(lowercase_				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	Optional[int]		,						lowercase_:	int		,						lowercase_:	Union[str, Any]				):
       _UpperCamelCase     :       Any  						=  []
       for ground_truth in ground_truths:
              _UpperCamelCase     :       Optional[Any]  						=  metric_fn(lowercase_		,						lowercase_				)
              scores_for_ground_truths.append(lowercase_				)
       return max(lowercase_				)


def SCREAMING_SNAKE_CASE				(     lowercase_:	Union[str, Any]		,						lowercase_:	List[Any]				):
       _UpperCamelCase     :       List[Any]  						=  0
       for article in dataset:
              for paragraph in article["paragraphs"]:
                     for qa in paragraph["qas"]:
                            total += 1
                            if qa["id"] not in predictions:
                                   _UpperCamelCase     :       Union[str, Any]  						=  'Unanswered question ' + qa['id'] + ' will receive score 0.'
                                   print(lowercase_		,						file=sys.stderr				)
                                   continue
                            _UpperCamelCase     :       Dict  						=  [x['text'] for x in qa['answers']]
                            _UpperCamelCase     :       List[str]  						=  predictions[qa['id']]
                            exact_match += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)
                            fa += metric_max_over_ground_truths(lowercase_		,						lowercase_		,						lowercase_				)

       _UpperCamelCase     :       Dict  						=  100.0 * exact_match / total
       _UpperCamelCase     :       Optional[Any]  						=  100.0 * fa / total

       return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
 UpperCAmelCase					=				'''1.1'''
 UpperCAmelCase					=				argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
 parser.add_argument('''dataset_file''', help='''Dataset file''')
 parser.add_argument('''prediction_file''', help='''Prediction File''')
 UpperCAmelCase					=				parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  UpperCAmelCase					=				json.load(dataset_file)
  if dataset_json["version"] != expected_version:
   print(
       '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
       file=sys.stderr,
   )
  UpperCAmelCase					=				dataset_json['''data''']
 with open(args.prediction_file) as prediction_file:
  UpperCAmelCase					=				json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))



