


'''simple docstring'''



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

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
	import faiss






@require_faiss
class  UpperCamelCase__(     lowerCAmelCase ):




				def   a__(	self    :				Tuple   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			tempfile.mkdtemp()
										UpperCAmelCase         =			8

										# DPR tok
										UpperCAmelCase         =			[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''[PAD]''',
										    '''[MASK]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										UpperCAmelCase         =			os.path.join(self.tmpdirname   ,					'''dpr_tokenizer'''   )
										os.makedirs(lowerCAmelCase   ,					exist_ok=lowerCAmelCase   )
										UpperCAmelCase         =			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         =			[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										UpperCAmelCase         =			dict(zip(lowerCAmelCase   ,					range(len(lowerCAmelCase   )   )   )   )
										UpperCAmelCase         =			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										UpperCAmelCase         =			{'''unk_token''': '''<unk>'''}

										UpperCAmelCase         =			os.path.join(self.tmpdirname   ,					'''bart_tokenizer'''   )
										os.makedirs(lowerCAmelCase   ,					exist_ok=lowerCAmelCase   )
										UpperCAmelCase         =			os.path.join(lowerCAmelCase   ,					BART_VOCAB_FILES_NAMES['''vocab_file''']   )
										UpperCAmelCase         =			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   a__(	self    :				List[Any]   )->  DPRQuestionEncoderTokenizer:

										"""simple docstring"""




										return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,					'''dpr_tokenizer'''   )   )




				def   a__(	self    :				int   )->  DPRContextEncoderTokenizer:

										"""simple docstring"""




										return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,					'''dpr_tokenizer'''   )   )




				def   a__(	self    :				str   )->  BartTokenizer:

										"""simple docstring"""




										return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,					'''bart_tokenizer'''   )   )




				def   a__(	self    :				Union[str, Any]   )->  Any:

										"""simple docstring"""




										shutil.rmtree(self.tmpdirname   )




				def   a__(	self    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			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   a__(	self    :				str   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dummy_dataset()
										UpperCAmelCase         =			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         =			dataset
																UpperCAmelCase         =			RagRetriever(
																    lowerCAmelCase   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					)
										return retriever




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				bool   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dummy_dataset()
										UpperCAmelCase         =			RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size   ,					question_encoder=DPRConfig().to_dict()   ,					generator=BartConfig().to_dict()   ,					index_name='''custom'''   ,					)
										if from_disk:
																UpperCAmelCase         =			os.path.join(self.tmpdirname   ,					'''dataset'''   )
																UpperCAmelCase         =			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         =			RagRetriever(
																    lowerCAmelCase   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					)
										else:
																UpperCAmelCase         =			RagRetriever(
																    lowerCAmelCase   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   ,					index=CustomHFIndex(config.retrieval_vector_size   ,					lowerCAmelCase   )   ,					)
										return retriever




				def   a__(	self    :				Dict   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			Dataset.from_dict(
										    {
										        '''id''': ['''0''', '''1'''],
										        '''text''': ['''foo''', '''bar'''],
										        '''title''': ['''Foo''', '''Bar'''],
										        '''embeddings''': [np.ones(self.retrieval_vector_size + 1   ), 2 * np.ones(self.retrieval_vector_size + 1   )],
										    }   )
										dataset.add_faiss_index('''embeddings'''   ,					string_factory='''Flat'''   ,					metric_type=faiss.METRIC_INNER_PRODUCT   )

										UpperCAmelCase         =			os.path.join(self.tmpdirname   ,					'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''   )
										dataset.save_faiss_index('''embeddings'''   ,					index_file_name + '''.index.dpr'''   )
										pickle.dump(dataset['''id''']   ,					open(index_file_name + '''.index_meta.dpr'''   ,					'''wb'''   )   )

										UpperCAmelCase         =			os.path.join(self.tmpdirname   ,					'''psgs_w100.tsv.pkl'''   )
										UpperCAmelCase         =			{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
										pickle.dump(lowerCAmelCase   ,					open(lowerCAmelCase   ,					'''wb'''   )   )

										UpperCAmelCase         =			RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size   ,					question_encoder=DPRConfig().to_dict()   ,					generator=BartConfig().to_dict()   ,					index_name='''legacy'''   ,					index_path=self.tmpdirname   ,					)
										UpperCAmelCase         =			RagRetriever(
										    lowerCAmelCase   ,					question_encoder_tokenizer=self.get_dpr_tokenizer()   ,					generator_tokenizer=self.get_bart_tokenizer()   )
										return retriever




				def   a__(	self    :				Optional[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			1
										UpperCAmelCase         =			self.get_dummy_canonical_hf_index_retriever()
										UpperCAmelCase         =			np.array(
										    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			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   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dummy_canonical_hf_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
																with patch('''transformers.models.rag.retrieval_rag.load_dataset'''   ) as mock_load_dataset:
																						UpperCAmelCase         =			self.get_dummy_dataset()
																						retriever.save_pretrained(lowerCAmelCase   )
																						UpperCAmelCase         =			RagRetriever.from_pretrained(lowerCAmelCase   )
																						self.assertIsInstance(lowerCAmelCase   ,					lowerCAmelCase   )
																						UpperCAmelCase         =			np.array(
																						    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
																						UpperCAmelCase         =			retriever.retrieve(lowerCAmelCase   ,					n_docs=1   )
																						self.assertTrue(out is not None   )




				def   a__(	self    :				Union[str, Any]   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			1
										UpperCAmelCase         =			self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase   )
										UpperCAmelCase         =			np.array(
										    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			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   a__(	self    :				List[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase   )
										with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(lowerCAmelCase   )
																UpperCAmelCase         =			RagRetriever.from_pretrained(lowerCAmelCase   )
																self.assertIsInstance(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			np.array(
																    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
																UpperCAmelCase         =			retriever.retrieve(lowerCAmelCase   ,					n_docs=1   )
																self.assertTrue(out is not None   )




				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			1
										UpperCAmelCase         =			self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase   )
										UpperCAmelCase         =			np.array(
										    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			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   a__(	self    :				Union[str, Any]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase   )
										with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(lowerCAmelCase   )
																UpperCAmelCase         =			RagRetriever.from_pretrained(lowerCAmelCase   )
																self.assertIsInstance(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			np.array(
																    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
																UpperCAmelCase         =			retriever.retrieve(lowerCAmelCase   ,					n_docs=1   )
																self.assertTrue(out is not None   )




				def   a__(	self    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			1
										UpperCAmelCase         =			self.get_dummy_legacy_index_retriever()
										UpperCAmelCase         =			np.array(
										    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			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]   )   ,					['''text''', '''title''']   )
										self.assertEqual(len(doc_dicts[0]['''text''']   )   ,					lowerCAmelCase   )
										self.assertEqual(doc_dicts[0]['''text'''][0]   ,					'''bar'''   )  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]['''text'''][0]   ,					'''foo'''   )  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()   ,					[[1], [0]]   )




				def   a__(	self    :				Tuple   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dummy_legacy_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(lowerCAmelCase   )
																UpperCAmelCase         =			RagRetriever.from_pretrained(lowerCAmelCase   )
																self.assertIsInstance(lowerCAmelCase   ,					lowerCAmelCase   )
																UpperCAmelCase         =			np.array(
																    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
																UpperCAmelCase         =			retriever.retrieve(lowerCAmelCase   ,					n_docs=1   )
																self.assertTrue(out is not None   )




				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def   a__(	self    :				Tuple   )->  List[Any]:

										"""simple docstring"""




										import torch

										UpperCAmelCase         =			1
										UpperCAmelCase         =			self.get_dummy_canonical_hf_index_retriever()
										UpperCAmelCase         =			[[5, 7], [10, 11]]
										UpperCAmelCase         =			np.array(
										    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
										UpperCAmelCase         =			retriever(lowerCAmelCase   ,					lowerCAmelCase   ,					prefix=retriever.config.generator.prefix   ,					n_docs=lowerCAmelCase   )
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			(
										    out['''context_input_ids'''],
										    out['''context_attention_mask'''],
										    out['''retrieved_doc_embeds'''],
										)
										self.assertEqual(retrieved_doc_embeds.shape   ,					(2, n_docs, self.retrieval_vector_size)   )
										self.assertIsInstance(lowerCAmelCase   ,					lowerCAmelCase   )
										self.assertIsInstance(lowerCAmelCase   ,					lowerCAmelCase   )
										self.assertIsInstance(lowerCAmelCase   ,					np.ndarray   )

										UpperCAmelCase         =			retriever(
										    lowerCAmelCase   ,					lowerCAmelCase   ,					prefix=retriever.config.generator.prefix   ,					n_docs=lowerCAmelCase   ,					return_tensors='''pt'''   ,					)
										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			(  # noqa: F841
										    out['''context_input_ids'''],
										    out['''context_attention_mask'''],
										    out['''retrieved_doc_embeds'''],
										    out['''doc_ids'''],
										)
										self.assertEqual(retrieved_doc_embeds.shape   ,					(2, n_docs, self.retrieval_vector_size)   )
										self.assertIsInstance(lowerCAmelCase   ,					torch.Tensor   )
										self.assertIsInstance(lowerCAmelCase   ,					torch.Tensor   )
										self.assertIsInstance(lowerCAmelCase   ,					torch.Tensor   )





				@require_torch
				@require_tokenizers
				@require_sentencepiece
				def   a__(	self    :				Tuple   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_dpr_ctx_encoder_tokenizer()
										UpperCAmelCase         =			1
										UpperCAmelCase         =			self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase   )
										retriever.set_ctx_encoder_tokenizer(lowerCAmelCase   )

										UpperCAmelCase         =			[[5, 7], [10, 11]]
										UpperCAmelCase         =			np.array(
										    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]   ,					dtype=np.floataa   )
										UpperCAmelCase         =			retriever(lowerCAmelCase   ,					lowerCAmelCase   ,					prefix=retriever.config.generator.prefix   ,					n_docs=lowerCAmelCase   )

										self.assertEqual(
										    len(lowerCAmelCase   )   ,					6   )  # check whether the retriever output consist of 6 attributes including tokenized docs
										self.assertEqual(
										    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')   )   ,					lowerCAmelCase   )  # check for doc token related keys in dictionary.









'''simple docstring'''



# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


_lowercase    :    Union[str, Any]			     =  """3"""

print("""Python version:""", sys.version)
print("""transformers version:""", transformers.__version__)

try:
	import torch

	print("""Torch version:""", torch.__version__)
	print("""Cuda available:""", torch.cuda.is_available())
	print("""Cuda version:""", torch.version.cuda)
	print("""CuDNN version:""", torch.backends.cudnn.version())
	print("""Number of GPUs available:""", torch.cuda.device_count())
	print("""NCCL version:""", torch.cuda.nccl.version())
except ImportError:
	print("""Torch version:""", None)

try:
	import deepspeed

	print("""DeepSpeed version:""", deepspeed.__version__)
except ImportError:
	print("""DeepSpeed version:""", None)

try:
	import tensorflow as tf

	print("""TensorFlow version:""", tf.__version__)
	print("""TF GPUs available:""", bool(tf.config.list_physical_devices("""GPU""")))
	print("""Number of TF GPUs available:""", len(tf.config.list_physical_devices("""GPU""")))
except ImportError:
	print("""TensorFlow version:""", None)





