





"""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"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							['''image_processor''', '''tokenizer''']
					UpperCamelCase								=							'''LayoutLMv2ImageProcessor'''
					UpperCamelCase								=							('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')


					def __init__( self      :List[Any]					,	__UpperCamelCase      :List[Any]=None					,	__UpperCamelCase      :Tuple=None					,	**__UpperCamelCase      :int		):
												A		=  None
												if "feature_extractor" in kwargs:
																			warnings.warn(
																			    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																			    " instead."					,	__UpperCamelCase					,	)
																			A		=  kwargs.pop("feature_extractor"		)

												A		=  image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																			raise ValueError("You need to specify an `image_processor`."		)
												if tokenizer is None:
																			raise ValueError("You need to specify a `tokenizer`."		)

												super().__init__(__UpperCamelCase					,	__UpperCamelCase		)


					def __call__( self      :Optional[Any]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,	__UpperCamelCase      :Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,	__UpperCamelCase      :Union[List[List[int]], List[List[List[int]]]] = None					,	__UpperCamelCase      :Optional[Union[List[int], List[List[int]]]] = None					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Union[bool, str, PaddingStrategy] = False					,	__UpperCamelCase      :Union[bool, str, TruncationStrategy] = False					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :int = 0					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :Optional[bool] = None					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = False					,	__UpperCamelCase      :bool = True					,	__UpperCamelCase      :Optional[Union[str, TensorType]] = None					,	**__UpperCamelCase      :int					,	):
												# verify input
												if self.image_processor.apply_ocr and (boxes is not None):
																			raise ValueError(
																			    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."		)

												if self.image_processor.apply_ocr and (word_labels is not None):
																			raise ValueError(
																			    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

												if return_overflowing_tokens is True and return_offsets_mapping is False:
																			raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

												# first, apply the image processor
												A		=  self.image_processor(images=__UpperCamelCase					,	return_tensors=__UpperCamelCase		)

												# second, apply the tokenizer
												if text is not None and self.image_processor.apply_ocr and text_pair is None:
																			if isinstance(__UpperCamelCase					,	__UpperCamelCase		):
																										A		=  [text]  # add batch dimension (as the image processor always adds a batch dimension)
																			A		=  features["words"]

												A		=  self.tokenizer(
												    text=text if text is not None else features["words"]					,	text_pair=text_pair if text_pair is not None else None					,	boxes=boxes if boxes is not None else features["boxes"]					,	word_labels=__UpperCamelCase					,	add_special_tokens=__UpperCamelCase					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase					,	max_length=__UpperCamelCase					,	stride=__UpperCamelCase					,	pad_to_multiple_of=__UpperCamelCase					,	return_token_type_ids=__UpperCamelCase					,	return_attention_mask=__UpperCamelCase					,	return_overflowing_tokens=__UpperCamelCase					,	return_special_tokens_mask=__UpperCamelCase					,	return_offsets_mapping=__UpperCamelCase					,	return_length=__UpperCamelCase					,	verbose=__UpperCamelCase					,	return_tensors=__UpperCamelCase					,	**__UpperCamelCase					,	)

												# add pixel values
												A		=  features.pop("pixel_values"		)
												if return_overflowing_tokens is True:
																			A		=  self.get_overflowing_images(__UpperCamelCase					,	encoded_inputs["overflow_to_sample_mapping"]		)
												A		=  images

												return encoded_inputs


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[str]		):
												# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
												A		=  []
												for sample_idx in overflow_to_sample_mapping:
																			images_with_overflow.append(images[sample_idx]		)

												if len(__UpperCamelCase		) != len(__UpperCamelCase		):
																			raise ValueError(
																			    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
																			    f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

												return images_with_overflow


					def      lowerCamelCase	( self      :Tuple					,	*__UpperCamelCase      :Union[str, Any]					,	**__UpperCamelCase      :Tuple		):
												return self.tokenizer.batch_decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	*__UpperCamelCase      :str					,	**__UpperCamelCase      :List[str]		):
												return self.tokenizer.decode(*__UpperCamelCase					,	**__UpperCamelCase		)


					@property
					def      lowerCamelCase	( self      :str		):
												return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]


					@property
					def      lowerCamelCase	( self      :str		):
												warnings.warn(
												    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,	__UpperCamelCase					,	)
												return self.image_processor_class


					@property
					def      lowerCamelCase	( self      :int		):
												warnings.warn(
												    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,	__UpperCamelCase					,	)
												return self.image_processor


