'''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    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					List[Any]							=["image_processor", "tokenizer"]
     lowerCamelCase  :					Union[str, Any]							="LayoutLMv2ImageProcessor"
     lowerCamelCase  :					List[Any]							=("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")






     def __init__(			self		:		Optional[int]  ,    a		:		Tuple=None  ,    a		:		Union[str, Any]=None  ,    **a		:		Dict   ):





         """simple docstring"""



         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''  ,    a  ,    )
             __lowerCamelCase          =  kwargs.pop('''feature_extractor'''   )

         __lowerCamelCase          =  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__(a  ,    a   )






     def __call__(			self		:		Optional[Any]  ,    a		:		str  ,    a		:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,    a		:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,    a		:		Union[List[List[int]], List[List[List[int]]]] = None  ,    a		:		Optional[Union[List[int], List[List[int]]]] = None  ,    a		:		bool = True  ,    a		:		Union[bool, str, PaddingStrategy] = False  ,    a		:		Union[bool, str, TruncationStrategy] = None  ,    a		:		Optional[int] = None  ,    a		:		int = 0  ,    a		:		Optional[int] = None  ,    a		:		Optional[bool] = None  ,    a		:		Optional[bool] = None  ,    a		:		bool = False  ,    a		:		bool = False  ,    a		:		bool = False  ,    a		:		bool = False  ,    a		:		bool = True  ,    a		:		Optional[Union[str, TensorType]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         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
         __lowerCamelCase          =  self.image_processor(images=a  ,    return_tensors=a   )

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

         __lowerCamelCase          =  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=a  ,    add_special_tokens=a  ,    padding=a  ,    truncation=a  ,    max_length=a  ,    stride=a  ,    pad_to_multiple_of=a  ,    return_token_type_ids=a  ,    return_attention_mask=a  ,    return_overflowing_tokens=a  ,    return_special_tokens_mask=a  ,    return_offsets_mapping=a  ,    return_length=a  ,    verbose=a  ,    return_tensors=a  ,    **a  ,    )

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

         return encoded_inputs






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]  ,    a		:		str  ,    a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  []
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]   )

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

         return images_with_overflow






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    *a		:		Dict  ,    **a		:		Dict   ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    *a		:		List[Any]  ,    **a		:		Dict   ):





         """simple docstring"""



         return self.tokenizer.decode(*a  ,    **a   )






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,    a  ,    )
         return self.image_processor_class






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,    a  ,    )
         return self.image_processor

'''simple docstring'''




import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer




class    a__				(       UpperCAmelCase__			):






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  tempfile.mkdtemp()
         __lowerCamelCase          =  5

         # Realm tok
         __lowerCamelCase          =  [
             '''[UNK]''',
             '''[CLS]''',
             '''[SEP]''',
             '''[PAD]''',
             '''[MASK]''',
             '''test''',
             '''question''',
             '''this''',
             '''is''',
             '''the''',
             '''first''',
             '''second''',
             '''third''',
             '''fourth''',
             '''fifth''',
             '''record''',
             '''want''',
             '''##want''',
             '''##ed''',
             '''wa''',
             '''un''',
             '''runn''',
             '''##ing''',
             ''',''',
             '''low''',
             '''lowest''',
         ]
         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    '''realm_tokenizer'''   )
         os.makedirs(a  ,    exist_ok=a   )
         __lowerCamelCase          =  os.path.join(a  ,    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]   )   )

         __lowerCamelCase          =  os.path.join(self.tmpdirname  ,    '''realm_block_records'''   )
         os.makedirs(a  ,    exist_ok=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,    '''realm_tokenizer'''   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         shutil.rmtree(self.tmpdirname   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  RealmConfig(num_block_records=self.num_block_records   )
         return config






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  Dataset.from_dict(
             {
                 '''id''': ['''0''', '''1'''],
                 '''question''': ['''foo''', '''bar'''],
                 '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
             }   )
         return dataset






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  np.array(
             [
                 b'''This is the first record''',
                 b'''This is the second record''',
                 b'''This is the third record''',
                 b'''This is the fourth record''',
                 b'''This is the fifth record''',
                 b'''This is a longer longer longer record''',
             ]  ,    dtype=a  ,    )
         return block_records






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         __lowerCamelCase          =  RealmRetriever(
             block_records=self.get_dummy_block_records()  ,    tokenizer=self.get_tokenizer()  ,    )
         return retriever






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_config()
         __lowerCamelCase          =  self.get_dummy_retriever()
         __lowerCamelCase          =  retriever.tokenizer

         __lowerCamelCase          =  np.array([0, 3]  ,    dtype='''long'''   )
         __lowerCamelCase          =  tokenizer(['''Test question''']   ).input_ids
         __lowerCamelCase          =  tokenizer(
             ['''the fourth''']  ,    add_special_tokens=a  ,    return_token_type_ids=a  ,    return_attention_mask=a  ,    ).input_ids
         __lowerCamelCase          =  config.reader_seq_len

         __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  retriever(
             a  ,    a  ,    answer_ids=a  ,    max_length=a  ,    return_tensors='''np'''   )

         self.assertEqual(len(a   )  ,    2   )
         self.assertEqual(len(a   )  ,    2   )
         self.assertEqual(len(a   )  ,    2   )
         self.assertEqual(concat_inputs.input_ids.shape  ,    (2, 10)   )
         self.assertEqual(concat_inputs.attention_mask.shape  ,    (2, 10)   )
         self.assertEqual(concat_inputs.token_type_ids.shape  ,    (2, 10)   )
         self.assertEqual(concat_inputs.special_tokens_mask.shape  ,    (2, 10)   )
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]   )  ,    ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]''']  ,    )
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]   )  ,    ['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]''']  ,    )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_config()
         __lowerCamelCase          =  self.get_dummy_retriever()
         __lowerCamelCase          =  retriever.tokenizer

         __lowerCamelCase          =  np.array([0, 3, 5]  ,    dtype='''long'''   )
         __lowerCamelCase          =  tokenizer(['''Test question''']   ).input_ids
         __lowerCamelCase          =  tokenizer(
             ['''the fourth''', '''longer longer''']  ,    add_special_tokens=a  ,    return_token_type_ids=a  ,    return_attention_mask=a  ,    ).input_ids
         __lowerCamelCase          =  config.reader_seq_len

         __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  retriever(
             a  ,    a  ,    answer_ids=a  ,    max_length=a  ,    return_tensors='''np'''   )

         self.assertEqual([False, True, True]  ,    a   )
         self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]  ,    a   )
         self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname  ,    '''realm_block_records'''   )   )

         # Test local path
         __lowerCamelCase          =  retriever.from_pretrained(os.path.join(self.tmpdirname  ,    '''realm_block_records'''   )   )
         self.assertEqual(retriever.block_records[0]  ,    b'''This is the first record'''   )

         # Test mocked remote path
         with patch('''transformers.models.realm.retrieval_realm.hf_hub_download'''   ) as mock_hf_hub_download:
             __lowerCamelCase          =  os.path.join(
                 os.path.join(self.tmpdirname  ,    '''realm_block_records'''   )  ,    _REALM_BLOCK_RECORDS_FILENAME   )
             __lowerCamelCase          =  RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa'''   )

         self.assertEqual(retriever.block_records[0]  ,    b'''This is the first record'''   )
