


"""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   _a      (      SCREAMING_SNAKE_CASE_ ):
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		tempfile.mkdtemp()
          lowerCamelCase__           =		8

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

          lowerCamelCase__           =		os.path.join(self.tmpdirname							,					'bart_tokenizer'				)
          os.makedirs(SCREAMING_SNAKE_CASE__							,					exist_ok=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		os.path.join(SCREAMING_SNAKE_CASE__							,					BART_VOCAB_FILES_NAMES['vocab_file']				)
          lowerCamelCase__           =		os.path.join(SCREAMING_SNAKE_CASE__							,					BART_VOCAB_FILES_NAMES['merges_file']				)
          with open(self.vocab_file							,					'w'							,					encoding='utf-8'				) as fp:
                fp.write(json.dumps(SCREAMING_SNAKE_CASE__				) + '\n'				)
          with open(self.merges_file							,					'w'							,					encoding='utf-8'				) as fp:
                fp.write('\n'.join(SCREAMING_SNAKE_CASE__				)				)
    def      _UpperCamelCase       ( self							:					Dict				):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,					'dpr_tokenizer'				)				)
    def      _UpperCamelCase       ( self							:					str				):
          return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,					'dpr_tokenizer'				)				)
    def      _UpperCamelCase       ( self							:					Any				):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,					'bart_tokenizer'				)				)
    def      _UpperCamelCase       ( self							:					int				):
          shutil.rmtree(self.tmpdirname				)
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		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      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.get_dummy_dataset()
          lowerCamelCase__           =		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:
                lowerCamelCase__           =		dataset
                lowerCamelCase__           =		RagRetriever(
                    SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					)
          return retriever
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					bool				):
          lowerCamelCase__           =		self.get_dummy_dataset()
          lowerCamelCase__           =		RagConfig(
              retrieval_vector_size=self.retrieval_vector_size							,					question_encoder=DPRConfig().to_dict()							,					generator=BartConfig().to_dict()							,					index_name='custom'							,					)
          if from_disk:
                lowerCamelCase__           =		os.path.join(self.tmpdirname							,					'dataset'				)
                lowerCamelCase__           =		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
                lowerCamelCase__           =		RagRetriever(
                    SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					)
          else:
                lowerCamelCase__           =		RagRetriever(
                    SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					index=CustomHFIndex(config.retrieval_vector_size							,					SCREAMING_SNAKE_CASE__				)							,					)
          return retriever
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		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				)

          lowerCamelCase__           =		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'				)				)

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

          lowerCamelCase__           =		RagConfig(
              retrieval_vector_size=self.retrieval_vector_size							,					question_encoder=DPRConfig().to_dict()							,					generator=BartConfig().to_dict()							,					index_name='legacy'							,					index_path=self.tmpdirname							,					)
          lowerCamelCase__           =		RagRetriever(
              SCREAMING_SNAKE_CASE__							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()				)
          return retriever
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_canonical_hf_index_retriever()
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['embeddings', 'id', 'text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['id']				)							,					SCREAMING_SNAKE_CASE__				)
          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      _UpperCamelCase       ( self							:					Optional[int]				):
          lowerCamelCase__           =		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:
                      lowerCamelCase__           =		self.get_dummy_dataset()
                      retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                      self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                      lowerCamelCase__           =		np.array(
                          [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                      lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                      self.assertTrue(out is not None				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['embeddings', 'id', 'text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['id']				)							,					SCREAMING_SNAKE_CASE__				)
          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      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          with tempfile.TemporaryDirectory() as tmp_dirname:
                retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		np.array(
                    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                self.assertTrue(out is not None				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['embeddings', 'id', 'text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['id']				)							,					SCREAMING_SNAKE_CASE__				)
          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      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          with tempfile.TemporaryDirectory() as tmp_dirname:
                retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		np.array(
                    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                self.assertTrue(out is not None				)
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_legacy_index_retriever()
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=SCREAMING_SNAKE_CASE__				)
          self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(SCREAMING_SNAKE_CASE__				)							,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)							,					['text', 'title']				)
          self.assertEqual(len(doc_dicts[0]['text']				)							,					SCREAMING_SNAKE_CASE__				)
          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      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		self.get_dummy_legacy_index_retriever()
          with tempfile.TemporaryDirectory() as tmp_dirname:
                retriever.save_pretrained(SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE__				)
                self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
                lowerCamelCase__           =		np.array(
                    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
                lowerCamelCase__           =		retriever.retrieve(SCREAMING_SNAKE_CASE__							,					n_docs=1				)
                self.assertTrue(out is not None				)
    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      _UpperCamelCase       ( self							:					Dict				):
          import torch

          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_canonical_hf_index_retriever()
          lowerCamelCase__           =		[[5, 7], [10, 11]]
          lowerCamelCase__           =		np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]							,					dtype=np.floataa				)
          lowerCamelCase__           =		retriever(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					prefix=retriever.config.generator.prefix							,					n_docs=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		(
              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(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					np.ndarray				)

          lowerCamelCase__           =		retriever(
              SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					prefix=retriever.config.generator.prefix							,					n_docs=SCREAMING_SNAKE_CASE__							,					return_tensors='pt'							,					)
          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		(  # 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(SCREAMING_SNAKE_CASE__							,					torch.Tensor				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				)
          self.assertIsInstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				)


    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		self.get_dpr_ctx_encoder_tokenizer()
          lowerCamelCase__           =		1
          lowerCamelCase__           =		self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE__				)
          retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE__				)

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

          self.assertEqual(
              len(SCREAMING_SNAKE_CASE__				)							,					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')				)							,					SCREAMING_SNAKE_CASE__				)  # check for doc token related keys in dictionary.







"""simple docstring"""


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
   from .camera import create_pan_cameras
   from .pipeline_shap_e import ShapEPipeline
   from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
   from .renderer import (
       BoundingBoxVolume,
       ImportanceRaySampler,
       MLPNeRFModelOutput,
       MLPNeRSTFModel,
       ShapEParamsProjModel,
       ShapERenderer,
       StratifiedRaySampler,
       VoidNeRFModel,
   )



