
import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
__a        = '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'

# TODO: Add description of the dataset here
# You can copy an official description
__a        = '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'

# TODO: Add a link to an official homepage for the dataset here
__a        = ''

# TODO: Add the licence for the dataset here if you can find it
__a        = ''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
__a        = {
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}
class 			lowercase__(    datasets.GeneratorBasedBuilder							):



	"""simple docstring"""




	a      :Optional[Any]   					=     datasets.Version('1.1.0'							)

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	a      :Optional[int]   					=     [
	    datasets.BuilderConfig(name='first_domain'			,						version=UpperCAmelCase			,						description='This part of my dataset covers a first domain'							),
	    datasets.BuilderConfig(name='second_domain'			,						version=UpperCAmelCase			,						description='This part of my dataset covers a second domain'							),
	]

	a      :Dict   					=     'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.
	def 						_lowercase  (	self  :      List[Any]	)	->					List[Any]:
					# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
					if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
									lowercase_					  = datasets.Features(
									    {
									        '''sentence''': datasets.Value('''string'''	),
									        '''option1''': datasets.Value('''string'''	),
									        '''answer''': datasets.Value('''string'''	)
									        # These are the features of your dataset like images, labels ...
									    }	)
					else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
									lowercase_					  = datasets.Features(
									    {
									        '''sentence''': datasets.Value('''string'''	),
									        '''option2''': datasets.Value('''string'''	),
									        '''second_domain_answer''': datasets.Value('''string'''	)
									        # These are the features of your dataset like images, labels ...
									    }	)
					return datasets.DatasetInfo(
					    # This is the description that will appear on the datasets page.
					    description=_DESCRIPTION	,							features=SCREAMING_SNAKE_CASE_	,							homepage=_HOMEPAGE	,							license=_LICENSE	,							citation=_CITATION	,							)
	def 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					Union[str, Any]:
					# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
					# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

					# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
					# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
					# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
					lowercase_					  = _URLS[self.config.name]
					lowercase_					  = dl_manager.download_and_extract(SCREAMING_SNAKE_CASE_	)
					return [
					    datasets.SplitGenerator(
					        name=datasets.Split.TRAIN	,							gen_kwargs={
					            '''filepath''': os.path.join(SCREAMING_SNAKE_CASE_	,							'''train.jsonl'''	),
					            '''split''': '''train''',
					        }	,							),
					    datasets.SplitGenerator(
					        name=datasets.Split.VALIDATION	,							gen_kwargs={
					            '''filepath''': os.path.join(SCREAMING_SNAKE_CASE_	,							'''dev.jsonl'''	),
					            '''split''': '''dev''',
					        }	,							),
					    datasets.SplitGenerator(
					        name=datasets.Split.TEST	,							gen_kwargs={
					            '''filepath''': os.path.join(SCREAMING_SNAKE_CASE_	,							'''test.jsonl'''	),
					            '''split''': '''test'''
					        }	,							),
					]




	def 						_lowercase  (	self  :      Dict	,							SCREAMING_SNAKE_CASE_  :      Dict	,							SCREAMING_SNAKE_CASE_  :      int	)	->					Any:
					# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
					# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
					with open(SCREAMING_SNAKE_CASE_	,							encoding='''utf-8'''	) as f:
									for key, row in enumerate(SCREAMING_SNAKE_CASE_	):
													lowercase_					  = json.loads(SCREAMING_SNAKE_CASE_	)
													if self.config.name == "first_domain":
																	# Yields examples as (key, example) tuples
																	yield key, {
																	    "sentence": data["sentence"],
																	    "option1": data["option1"],
																	    "answer": "" if split == "test" else data["answer"],
																	}
													else:
																	yield key, {
																	    "sentence": data["sentence"],
																	    "option2": data["option2"],
																	    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
																	}






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 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	a      :Any   					=     ['image_processor', 'tokenizer']
	a      :Tuple   					=     'LayoutLMv2ImageProcessor'
	a      :List[Any]   					=     ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')
	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							SCREAMING_SNAKE_CASE_  :      Any=None	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = 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_	,							)
									lowercase_					  = kwargs.pop('''feature_extractor'''	)

					lowercase_					  = 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_	)
	def __call__(	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,							SCREAMING_SNAKE_CASE_  :      Union[List[List[int]], List[List[List[int]]]] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[List[int], List[List[int]]]] = None	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							SCREAMING_SNAKE_CASE_  :      Union[bool, str, PaddingStrategy] = False	,							SCREAMING_SNAKE_CASE_  :      Union[bool, str, TruncationStrategy] = False	,							SCREAMING_SNAKE_CASE_  :      Optional[int] = None	,							SCREAMING_SNAKE_CASE_  :      int = 0	,							SCREAMING_SNAKE_CASE_  :      Optional[int] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[bool] = None	,							SCREAMING_SNAKE_CASE_  :      Optional[bool] = None	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = False	,							SCREAMING_SNAKE_CASE_  :      bool = True	,							SCREAMING_SNAKE_CASE_  :      Optional[Union[str, TensorType]] = None	,							**SCREAMING_SNAKE_CASE_  :      List[Any]	,							)	->					BatchEncoding:
					# 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
					lowercase_					  = self.image_processor(images=SCREAMING_SNAKE_CASE_	,							return_tensors=SCREAMING_SNAKE_CASE_	)

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

					lowercase_					  = 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=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
					lowercase_					  = features.pop('''pixel_values'''	)
					if return_overflowing_tokens is True:
									lowercase_					  = self.get_overflowing_images(SCREAMING_SNAKE_CASE_	,							encoded_inputs['''overflow_to_sample_mapping''']	)
					lowercase_					  = images

					return encoded_inputs
	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]	)	->					List[str]:
					# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
					lowercase_					  = []
					for sample_idx in overflow_to_sample_mapping:
									images_with_overflow.append(images[sample_idx]	)

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

					return images_with_overflow
	def 						_lowercase  (	self  :      List[Any]	,							*SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							**SCREAMING_SNAKE_CASE_  :      Tuple	)	->					List[str]:
					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      Optional[Any]	,							*SCREAMING_SNAKE_CASE_  :      Dict	,							**SCREAMING_SNAKE_CASE_  :      List[str]	)	->					List[Any]:
					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_	,							**SCREAMING_SNAKE_CASE_	)
	@property
	def 						_lowercase  (	self  :      List[Any]	)	->					List[Any]:
					return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
	@property
	def 						_lowercase  (	self  :      Dict	)	->					str:
					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 						_lowercase  (	self  :      int	)	->					List[str]:
					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,							SCREAMING_SNAKE_CASE_	,							)
					return self.image_processor




