


"""simple docstring"""




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		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Union[str, Any]					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							Union[str, Any]					  =  "BridgeTowerImageProcessor"
							__UpperCAmelCase   :							Union[str, Any]					  =  ("RobertaTokenizer", "RobertaTokenizerFast")

							def __init__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Optional[int]    )     ->   Dict:
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )

							def __call__(							self	:  Dict   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Union[bool, str, PaddingStrategy] = False   ,     lowerCamelCase	:  Union[bool, str, TruncationStrategy] = None   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  int = 0   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  Optional[bool] = None   ,     lowerCamelCase	:  Optional[bool] = None   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  Optional[Union[str, TensorType]] = None   ,     **lowerCamelCase	:  Any   ,     )     ->   BatchEncoding:
														__snake_case		:      List[Any]										=       self.tokenizer(
														    text=lowerCamelCase   ,     add_special_tokens=lowerCamelCase   ,     padding=lowerCamelCase   ,     truncation=lowerCamelCase   ,     max_length=lowerCamelCase   ,     stride=lowerCamelCase   ,     pad_to_multiple_of=lowerCamelCase   ,     return_token_type_ids=lowerCamelCase   ,     return_attention_mask=lowerCamelCase   ,     return_overflowing_tokens=lowerCamelCase   ,     return_special_tokens_mask=lowerCamelCase   ,     return_offsets_mapping=lowerCamelCase   ,     return_length=lowerCamelCase   ,     verbose=lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase   ,     )
														# add pixel_values + pixel_mask
														__snake_case		:      int										=       self.image_processor(
														    lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     do_normalize=lowerCamelCase   ,     do_center_crop=lowerCamelCase   ,     **lowerCamelCase    )
														encoding.update(lowerCamelCase    )

														return encoding

							def    __snake_case (							self	:  Optional[Any]   ,     *lowerCamelCase	:  str   ,     **lowerCamelCase	:  Union[str, Any]    )     ->   List[str]:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  List[Any]   ,     *lowerCamelCase	:  Union[str, Any]   ,     **lowerCamelCase	:  Tuple    )     ->   Optional[int]:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )






							@property
							def    __snake_case (							self	:  Optional[int]    )     ->   int:
														__snake_case		:      List[Any]										=       self.tokenizer.model_input_names
														__snake_case		:      List[str]										=       self.image_processor.model_input_names
														return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_snake_case					:		List[str]      =    namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (abs(source - target     ) / target) < 0.0_1







@pytest.mark.integration
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       _TestCommandArgs(dataset=__lowerCamelCase							,     all_configs=__lowerCamelCase							,     save_infos=__lowerCamelCase     )
							__snake_case		:      List[Any]										=       TestCommand(*__lowerCamelCase     )
							test_command.run()
							__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     "README.md"     )
							assert os.path.exists(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       DatasetInfosDict.from_directory(__lowerCamelCase     )
							__snake_case		:      List[str]										=       DatasetInfosDict(
							    {
							        "default": DatasetInfo(
							            features=Features(
							                {
							                    "tokens": Sequence(Value("string"     )     ),
							                    "ner_tags": Sequence(
							                        ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]     )     ),
							                    "langs": Sequence(Value("string"     )     ),
							                    "spans": Sequence(Value("string"     )     ),
							                }     )							,     splits=[
							                {
							                    "name": "train",
							                    "num_bytes": 2_3_5_1_5_6_3,
							                    "num_examples": 1_0_0_0_0,
							                },
							                {
							                    "name": "validation",
							                    "num_bytes": 2_3_8_4_1_8,
							                    "num_examples": 1_0_0_0,
							                },
							            ]							,     download_size=3_9_4_0_6_8_0							,     dataset_size=2_5_8_9_9_8_1							,     )
							    }     )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														__snake_case			,      __snake_case		:      Tuple										=       getattr(dataset_infos["default"]							,     __lowerCamelCase     ), getattr(expected_dataset_infos["default"]							,     __lowerCamelCase     )
														if key == "num_bytes":
																					assert is_apercent_close(__lowerCamelCase							,     __lowerCamelCase     )
														elif key == "splits":
																					assert list(__lowerCamelCase     ) == list(__lowerCamelCase     )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes							,     expected[split].num_bytes     )
														else:
																					result == expected

