




"""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       SCREAMING_SNAKE_CASE__					(					_a					):


						def   __lowercase (							self				:			str					):
										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(lowerCAmelCase						,						exist_ok=lowerCAmelCase					)
										lowerCAmelCase  =				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
										lowerCAmelCase  =				[
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										lowerCAmelCase  =				dict(zip(lowerCAmelCase						,						range(len(lowerCAmelCase					)					)					)					)
										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(lowerCAmelCase						,						exist_ok=lowerCAmelCase					)
										lowerCAmelCase  =				os.path.join(lowerCAmelCase						,						BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
										lowerCAmelCase  =				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   __lowercase (							self				:			Tuple					):
										return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,						"""dpr_tokenizer"""					)					)


						def   __lowercase (							self				:			List[str]					):
										return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname						,						"""dpr_tokenizer"""					)					)


						def   __lowercase (							self				:			Union[str, Any]					):
										return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname						,						"""bart_tokenizer"""					)					)


						def   __lowercase (							self				:			Any					):
										shutil.rmtree(self.tmpdirname					)


						def   __lowercase (							self				:			Tuple					):
										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   __lowercase (							self				:			Union[str, Any]					):
										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(
														    lowerCAmelCase						,						question_encoder_tokenizer=self.get_dpr_tokenizer()						,						generator_tokenizer=self.get_bart_tokenizer()						,						)
										return retriever


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			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(
														    lowerCAmelCase						,						question_encoder_tokenizer=self.get_dpr_tokenizer()						,						generator_tokenizer=self.get_bart_tokenizer()						,						)
										else:
														lowerCAmelCase  =				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   __lowercase (							self				:			Any					):
										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(lowerCAmelCase						,						open(lowerCAmelCase						,						"""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(
										    lowerCAmelCase						,						question_encoder_tokenizer=self.get_dpr_tokenizer()						,						generator_tokenizer=self.get_bart_tokenizer()					)
										return retriever


						def   __lowercase (							self				:			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(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   __lowercase (							self				:			List[str]					):
										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(lowerCAmelCase					)
																		lowerCAmelCase  =				RagRetriever.from_pretrained(lowerCAmelCase					)
																		self.assertIsInstance(lowerCAmelCase						,						lowerCAmelCase					)
																		lowerCAmelCase  =				np.array(
																		    [np.ones(self.retrieval_vector_size					), -np.ones(self.retrieval_vector_size					)]						,						dtype=np.floataa					)
																		lowerCAmelCase  =				retriever.retrieve(lowerCAmelCase						,						n_docs=1					)
																		self.assertTrue(out is not None					)


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


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


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


						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def   __lowercase (							self				:			List[str]					):
										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(lowerCAmelCase						,						lowerCAmelCase						,						prefix=retriever.config.generator.prefix						,						n_docs=lowerCAmelCase					)
										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(lowerCAmelCase						,						lowerCAmelCase					)
										self.assertIsInstance(lowerCAmelCase						,						lowerCAmelCase					)
										self.assertIsInstance(lowerCAmelCase						,						np.ndarray					)

										lowerCAmelCase  =				retriever(
										    lowerCAmelCase						,						lowerCAmelCase						,						prefix=retriever.config.generator.prefix						,						n_docs=lowerCAmelCase						,						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(lowerCAmelCase						,						torch.Tensor					)
										self.assertIsInstance(lowerCAmelCase						,						torch.Tensor					)
										self.assertIsInstance(lowerCAmelCase						,						torch.Tensor					)




						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.get_dpr_ctx_encoder_tokenizer()
										lowerCAmelCase  =				1
										lowerCAmelCase  =				self.get_dummy_custom_hf_index_retriever(from_disk=lowerCAmelCase					)
										retriever.set_ctx_encoder_tokenizer(lowerCAmelCase					)

										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(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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





