





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__A									=				{
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["LayoutLMv2TokenizerFast"]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["LayoutLMv2FeatureExtractor"]
     __A									=				["LayoutLMv2ImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMv2ForQuestionAnswering",
         "LayoutLMv2ForSequenceClassification",
         "LayoutLMv2ForTokenClassification",
         "LayoutLMv2Layer",
         "LayoutLMv2Model",
         "LayoutLMv2PreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
     from .processing_layoutlmva import LayoutLMvaProcessor
     from .tokenization_layoutlmva import LayoutLMvaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaLayer,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )
else:
     import sys

     __A									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





"""simple docstring"""







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
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.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
     from transformers.models.rag.configuration_rag import RagConfig
     from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							tempfile.mkdtemp()
         lowerCAmelCase     :     Optional[int]								=							8

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

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

       def        lowercase__							(						self						):






         """simple docstring"""





         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     "dpr_tokenizer"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,     "bart_tokenizer"						)						)

       def        lowercase__							(						self						):






         """simple docstring"""





         shutil.rmtree(self.tmpdirname						)

       @require_tokenizers
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							os.path.join(self.tmpdirname		,     "rag_tokenizer"						)
         lowerCAmelCase     :     List[Any]								=							RagConfig(question_encoder=DPRConfig().to_dict()		,     generator=BartConfig().to_dict()						)
         lowerCAmelCase     :     Optional[Any]								=							RagTokenizer(question_encoder=self.get_dpr_tokenizer()		,     generator=self.get_bart_tokenizer()						)
         rag_config.save_pretrained(snake_case__						)
         rag_tokenizer.save_pretrained(snake_case__						)
         lowerCAmelCase     :     List[str]								=							RagTokenizer.from_pretrained(snake_case__		,     config=snake_case__						)
         self.assertIsInstance(new_rag_tokenizer.question_encoder		,     snake_case__						)
         self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()		,     rag_tokenizer.question_encoder.get_vocab()						)
         self.assertIsInstance(new_rag_tokenizer.generator		,     snake_case__						)
         self.assertEqual(new_rag_tokenizer.generator.get_vocab()		,     rag_tokenizer.generator.get_vocab()						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							RagTokenizer.from_pretrained("facebook/rag-token-nq"						)
         lowerCAmelCase     :     Dict								=							[
             "who got the first nobel prize in physics",
             "when is the next deadpool movie being released",
             "which mode is used for short wave broadcast service",
             "who is the owner of reading football club",
             "when is the next scandal episode coming out",
             "when is the last time the philadelphia won the superbowl",
             "what is the most current adobe flash player version",
             "how many episodes are there in dragon ball z",
             "what is the first step in the evolution of the eye",
             "where is gall bladder situated in human body",
             "what is the main mineral in lithium batteries",
             "who is the president of usa right now",
             "where do the greasers live in the outsiders",
             "panda is a national animal of which country",
             "what is the name of manchester united stadium",
         ]
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer(snake_case__						)
         self.assertIsNotNone(snake_case__						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							RagTokenizer.from_pretrained("facebook/rag-sequence-nq"						)
         lowerCAmelCase     :     List[str]								=							[
             "who got the first nobel prize in physics",
             "when is the next deadpool movie being released",
             "which mode is used for short wave broadcast service",
             "who is the owner of reading football club",
             "when is the next scandal episode coming out",
             "when is the last time the philadelphia won the superbowl",
             "what is the most current adobe flash player version",
             "how many episodes are there in dragon ball z",
             "what is the first step in the evolution of the eye",
             "where is gall bladder situated in human body",
             "what is the main mineral in lithium batteries",
             "who is the president of usa right now",
             "where do the greasers live in the outsiders",
             "panda is a national animal of which country",
             "what is the name of manchester united stadium",
         ]
         lowerCAmelCase     :     str								=							tokenizer(snake_case__						)
         self.assertIsNotNone(snake_case__						)





