





"""simple docstring"""
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:
		_snake_case					:		Union[str, Any]							=			None

if is_ray_available():
		import ray  # noqa: E402 # isort:skip
		from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
		_snake_case					:		Any							=			None
		_snake_case					:		List[str]							=			None
		_snake_case					:		List[Any]							=			None






def  A__    (      UpperCamelCase	):
							if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
														A		=  unittest.skip("test requires Datasets, Faiss, psutil"	)(UpperCamelCase	)
							return test_case






@require_distributed_retrieval
class  _UpperCAmelCase      (     lowercase_			):


					def      lowerCamelCase	( self      :Optional[Any]		):
												A		=  tempfile.mkdtemp()
												A		=  8

												# DPR tok
												A		=  [
												    "[UNK]",
												    "[CLS]",
												    "[SEP]",
												    "[PAD]",
												    "[MASK]",
												    "want",
												    "##want",
												    "##ed",
												    "wa",
												    "un",
												    "runn",
												    "##ing",
												    ",",
												    "low",
												    "lowest",
												]
												A		=  os.path.join(self.tmpdirname					,	"dpr_tokenizer"		)
												os.makedirs(__UpperCamelCase					,	exist_ok=__UpperCamelCase		)
												A		=  os.path.join(__UpperCamelCase					,	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
												A		=  [
												    "l",
												    "o",
												    "w",
												    "e",
												    "r",
												    "s",
												    "t",
												    "i",
												    "d",
												    "n",
												    "\u0120",
												    "\u0120l",
												    "\u0120n",
												    "\u0120lo",
												    "\u0120low",
												    "er",
												    "\u0120lowest",
												    "\u0120newer",
												    "\u0120wider",
												    "<unk>",
												]
												A		=  dict(zip(__UpperCamelCase					,	range(len(__UpperCamelCase		)		)		)		)
												A		=  ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
												A		=  {"unk_token": "<unk>"}

												A		=  os.path.join(self.tmpdirname					,	"bart_tokenizer"		)
												os.makedirs(__UpperCamelCase					,	exist_ok=__UpperCamelCase		)
												A		=  os.path.join(__UpperCamelCase					,	BART_VOCAB_FILES_NAMES["vocab_file"]		)
												A		=  os.path.join(__UpperCamelCase					,	BART_VOCAB_FILES_NAMES["merges_file"]		)
												with open(self.vocab_file					,	"w"					,	encoding="utf-8"		) as fp:
																			fp.write(json.dumps(__UpperCamelCase		) + "\n"		)
												with open(self.merges_file					,	"w"					,	encoding="utf-8"		) as fp:
																			fp.write("\n".join(__UpperCamelCase		)		)


					def      lowerCamelCase	( self      :Union[str, Any]		):
												return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,	"dpr_tokenizer"		)		)


					def      lowerCamelCase	( self      :Tuple		):
												return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,	"bart_tokenizer"		)		)


					def      lowerCamelCase	( self      :Tuple		):
												shutil.rmtree(self.tmpdirname		)


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  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      lowerCamelCase	( self      :int					,	__UpperCamelCase      :bool					,	__UpperCamelCase      :Dict=1_23_45		):
												A		=  self.get_dummy_dataset()
												A		=  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:
																			A		=  dataset
																			A		=  RagPyTorchDistributedRetriever(
																			    __UpperCamelCase					,	question_encoder_tokenizer=self.get_dpr_tokenizer()					,	generator_tokenizer=self.get_bart_tokenizer()					,	)
																			if init_retrieval:
																										retriever.init_retrieval(__UpperCamelCase		)
												return retriever


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :bool		):
												# 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=__UpperCamelCase		)
												A		=  RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size					,	question_encoder=DPRConfig().to_dict()					,	generator=BartConfig().to_dict()					,	)
												A		=  ray.remote(__UpperCamelCase		)
												A		=  [remote_cls.remote() for _ in range(1		)]
												with patch("transformers.models.rag.retrieval_rag.load_dataset"		) as mock_load_dataset:
																			A		=  self.get_dummy_dataset()
																			A		=  RagRayDistributedRetriever(
																			    __UpperCamelCase					,	question_encoder_tokenizer=self.get_dpr_tokenizer()					,	generator_tokenizer=self.get_bart_tokenizer()					,	retrieval_workers=__UpperCamelCase					,	)
																			if init_retrieval:
																										retriever.init_retrieval()
												return retriever


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :bool					,	__UpperCamelCase      :bool					,	__UpperCamelCase      :Tuple=1_23_45		):
												A		=  self.get_dummy_dataset()
												A		=  RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size					,	question_encoder=DPRConfig().to_dict()					,	generator=BartConfig().to_dict()					,	index_name="custom"					,	)
												if from_disk:
																			A		=  os.path.join(self.tmpdirname					,	"dataset"		)
																			A		=  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
																			A		=  RagPyTorchDistributedRetriever(
																			    __UpperCamelCase					,	question_encoder_tokenizer=self.get_dpr_tokenizer()					,	generator_tokenizer=self.get_bart_tokenizer()					,	)
												else:
																			A		=  RagPyTorchDistributedRetriever(
																			    __UpperCamelCase					,	question_encoder_tokenizer=self.get_dpr_tokenizer()					,	generator_tokenizer=self.get_bart_tokenizer()					,	index=CustomHFIndex(config.retrieval_vector_size					,	__UpperCamelCase		)					,	)
												if init_retrieval:
																			retriever.init_retrieval(__UpperCamelCase		)
												return retriever


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :bool					,	__UpperCamelCase      :bool		):
												# 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=__UpperCamelCase		)
												A		=  self.get_dummy_dataset()
												A		=  RagConfig(
												    retrieval_vector_size=self.retrieval_vector_size					,	question_encoder=DPRConfig().to_dict()					,	generator=BartConfig().to_dict()					,	index_name="custom"					,	)
												A		=  ray.remote(__UpperCamelCase		)
												A		=  [remote_cls.remote() for _ in range(1		)]
												if from_disk:
																			A		=  os.path.join(self.tmpdirname					,	"dataset"		)
																			A		=  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
																			A		=  RagRayDistributedRetriever(
																			    __UpperCamelCase					,	question_encoder_tokenizer=self.get_dpr_tokenizer()					,	generator_tokenizer=self.get_bart_tokenizer()					,	retrieval_workers=__UpperCamelCase					,	index=CustomHFIndex.load_from_disk(
																			        vector_size=config.retrieval_vector_size					,	dataset_path=config.passages_path					,	index_path=config.index_path					,	)					,	)
												else:
																			A		=  RagRayDistributedRetriever(
																			    __UpperCamelCase					,	question_encoder_tokenizer=self.get_dpr_tokenizer()					,	generator_tokenizer=self.get_bart_tokenizer()					,	retrieval_workers=__UpperCamelCase					,	index=CustomHFIndex(config.retrieval_vector_size					,	__UpperCamelCase		)					,	)
												if init_retrieval:
																			retriever.init_retrieval()
												return retriever


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :RagRetriever					,	__UpperCamelCase      :np.array					,	__UpperCamelCase      :int		):
												A,     A,     A		=  retriever.retrieve(__UpperCamelCase					,	n_docs=__UpperCamelCase		)
												self.assertEqual(retrieved_doc_embeds.shape					,	(2, n_docs, self.retrieval_vector_size)		)
												self.assertEqual(len(__UpperCamelCase		)					,	2		)
												self.assertEqual(sorted(doc_dicts[0]		)					,	["embeddings", "id", "text", "title"]		)
												self.assertEqual(len(doc_dicts[0]["id"]		)					,	__UpperCamelCase		)
												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      lowerCamelCase	( self      :Optional[int]		):
												A		=  1
												A		=  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=__UpperCamelCase		)					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  1
												A		=  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=__UpperCamelCase					,	from_disk=__UpperCamelCase		)					,	__UpperCamelCase					,	__UpperCamelCase					,	)


					def      lowerCamelCase	( self      :Tuple		):
												A		=  1
												A		=  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=__UpperCamelCase					,	from_disk=__UpperCamelCase		)					,	__UpperCamelCase					,	__UpperCamelCase					,	)


					@require_ray
					def      lowerCamelCase	( self      :Tuple		):
												A		=  1
												A		=  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=__UpperCamelCase		)					,	__UpperCamelCase					,	__UpperCamelCase		)
												ray.shutdown()


					@require_ray
					def      lowerCamelCase	( self      :str		):
												A		=  1
												A		=  np.array(
												    [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]					,	dtype=np.floataa		)
												with self.assertRaises(__UpperCamelCase		):
																			self.distributed_retriever_check(
																			    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__UpperCamelCase					,	from_disk=__UpperCamelCase		)					,	__UpperCamelCase					,	__UpperCamelCase					,	)
												ray.shutdown()


					@require_ray
					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  1
												A		=  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=__UpperCamelCase					,	from_disk=__UpperCamelCase		)					,	__UpperCamelCase					,	__UpperCamelCase		)
												ray.shutdown()









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


