






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_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							Optional[int]						 =       tempfile.mkdtemp()
								snake_case_				:							Optional[int]						 =       8

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

								snake_case_				:							List[str]						 =       os.path.join(self.tmpdirname		,				"bart_tokenizer"				)
								os.makedirs(_SCREAMING_SNAKE_CASE		,				exist_ok=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							List[str]						 =       os.path.join(_SCREAMING_SNAKE_CASE		,				BART_VOCAB_FILES_NAMES["vocab_file"]				)
								snake_case_				:							str						 =       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       _lowerCAmelCase       (	self				) ->				DPRQuestionEncoderTokenizer:
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"dpr_tokenizer"				)				)






						def       _lowerCAmelCase       (	self				) ->				DPRContextEncoderTokenizer:
								return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"dpr_tokenizer"				)				)






						def       _lowerCAmelCase       (	self				) ->				BartTokenizer:
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,				"bart_tokenizer"				)				)






						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								shutil.rmtree(self.tmpdirname				)






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Optional[Any]						 =       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				) ->				Any:
								snake_case_				:							Optional[int]						 =       self.get_dummy_dataset()
								snake_case_				:							List[Any]						 =       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:
										snake_case_				:							List[Any]						 =       dataset
										snake_case_				:							int						 =       RagRetriever(
										    _SCREAMING_SNAKE_CASE		,				question_encoder_tokenizer=self.get_dpr_tokenizer()		,				generator_tokenizer=self.get_bart_tokenizer()		,				)
								return retriever






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							Dict						 =       self.get_dummy_dataset()
								snake_case_				:							int						 =       RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,				question_encoder=DPRConfig().to_dict()		,				generator=BartConfig().to_dict()		,				index_name="custom"		,				)
								if from_disk:
										snake_case_				:							Optional[Any]						 =       os.path.join(self.tmpdirname		,				"dataset"				)
										snake_case_				:							Tuple						 =       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
										snake_case_				:							int						 =       RagRetriever(
										    _SCREAMING_SNAKE_CASE		,				question_encoder_tokenizer=self.get_dpr_tokenizer()		,				generator_tokenizer=self.get_bart_tokenizer()		,				)
								else:
										snake_case_				:							Optional[int]						 =       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       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							List[str]						 =       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				)

								snake_case_				:							Optional[int]						 =       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"				)				)

								snake_case_				:							Union[str, Any]						 =       os.path.join(self.tmpdirname		,				"psgs_w100.tsv.pkl"				)
								snake_case_				:							Tuple						 =       {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
								pickle.dump(_SCREAMING_SNAKE_CASE		,				open(_SCREAMING_SNAKE_CASE		,				"wb"				)				)

								snake_case_				:							str						 =       RagConfig(
								    retrieval_vector_size=self.retrieval_vector_size		,				question_encoder=DPRConfig().to_dict()		,				generator=BartConfig().to_dict()		,				index_name="legacy"		,				index_path=self.tmpdirname		,				)
								snake_case_				:							Tuple						 =       RagRetriever(
								    _SCREAMING_SNAKE_CASE		,				question_encoder_tokenizer=self.get_dpr_tokenizer()		,				generator_tokenizer=self.get_bart_tokenizer()				)
								return retriever






						def       _lowerCAmelCase       (	self				) ->				int:
								snake_case_				:							Optional[Any]						 =       1
								snake_case_				:							Optional[Any]						 =       self.get_dummy_canonical_hf_index_retriever()
								snake_case_				:							List[Any]						 =       np.array(
								    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
								snake_case_ ,       snake_case_ ,       snake_case_				:							Union[str, Any]						 =       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       _lowerCAmelCase       (	self				) ->				List[str]:
								snake_case_				:							Any						 =       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:
												snake_case_				:							List[str]						 =       self.get_dummy_dataset()
												retriever.save_pretrained(_SCREAMING_SNAKE_CASE				)
												snake_case_				:							Union[str, Any]						 =       RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE				)
												self.assertIsInstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
												snake_case_				:							Optional[int]						 =       np.array(
												    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
												snake_case_				:							str						 =       retriever.retrieve(_SCREAMING_SNAKE_CASE		,				n_docs=1				)
												self.assertTrue(out is not None				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							List[Any]						 =       1
								snake_case_				:							Any						 =       self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Tuple						 =       np.array(
								    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
								snake_case_ ,       snake_case_ ,       snake_case_				:							int						 =       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       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Union[str, Any]						 =       self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE				)
								with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[str]						 =       RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsInstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
										snake_case_				:							Dict						 =       retriever.retrieve(_SCREAMING_SNAKE_CASE		,				n_docs=1				)
										self.assertTrue(out is not None				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Optional[int]						 =       1
								snake_case_				:							int						 =       self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Union[str, Any]						 =       np.array(
								    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
								snake_case_ ,       snake_case_ ,       snake_case_				:							int						 =       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       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							str						 =       self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE				)
								with tempfile.TemporaryDirectory() as tmp_dirname:
										retriever.save_pretrained(_SCREAMING_SNAKE_CASE				)
										snake_case_				:							List[Any]						 =       RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE				)
										self.assertIsInstance(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
										snake_case_				:							Union[str, Any]						 =       np.array(
										    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
										snake_case_				:							Union[str, Any]						 =       retriever.retrieve(_SCREAMING_SNAKE_CASE		,				n_docs=1				)
										self.assertTrue(out is not None				)






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






						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								import torch

								snake_case_				:							str						 =       1
								snake_case_				:							List[str]						 =       self.get_dummy_canonical_hf_index_retriever()
								snake_case_				:							Dict						 =       [[5, 7], [10, 11]]
								snake_case_				:							List[Any]						 =       np.array(
								    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
								snake_case_				:							Any						 =       retriever(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				prefix=retriever.config.generator.prefix		,				n_docs=_SCREAMING_SNAKE_CASE				)
								snake_case_ ,       snake_case_ ,       snake_case_				:							List[Any]						 =       (
								    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				)

								snake_case_				:							int						 =       retriever(
								    _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				prefix=retriever.config.generator.prefix		,				n_docs=_SCREAMING_SNAKE_CASE		,				return_tensors="pt"		,				)
								snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_				:							Union[str, Any]						 =       (  # 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       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							List[Any]						 =       self.get_dpr_ctx_encoder_tokenizer()
								snake_case_				:							int						 =       1
								snake_case_				:							str						 =       self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE				)
								retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE				)

								snake_case_				:							int						 =       [[5, 7], [10, 11]]
								snake_case_				:							Any						 =       np.array(
								    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]		,				dtype=np.floataa				)
								snake_case_				:							Tuple						 =       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.








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_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   List[Any]						=					'ViltImageProcessor'
						A     :   Optional[Any]						=					('BertTokenizer', 'BertTokenizerFast')






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							str						 =       None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."		,				_SCREAMING_SNAKE_CASE		,				)
										snake_case_				:							str						 =       kwargs.pop("feature_extractor"				)

								snake_case_				:							Tuple						 =       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__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       self.image_processor






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchEncoding:
								snake_case_				:							List[Any]						 =       self.tokenizer(
								    text=_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,				return_special_tokens_mask=_SCREAMING_SNAKE_CASE		,				return_offsets_mapping=_SCREAMING_SNAKE_CASE		,				return_length=_SCREAMING_SNAKE_CASE		,				verbose=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								# add pixel_values + pixel_mask
								snake_case_				:							str						 =       self.image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE				)
								encoding.update(_SCREAMING_SNAKE_CASE				)

								return encoding






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.tokenizer.model_input_names
								snake_case_				:							Tuple						 =       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor_class






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor
