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
UpperCAmelCase							= '''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

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

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

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

# 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)
UpperCAmelCase							= {
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}




class       __lowerCAmelCase     (				datasets.GeneratorBasedBuilder  ):



 '''simple docstring'''
 _lowerCamelCase  :		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')
 _lowerCamelCase  :		Tuple 			=       [
     datasets.BuilderConfig(name='first_domain',		version=a_,		description='This part of my dataset covers a first domain'  ),
     datasets.BuilderConfig(name='second_domain',		version=a_,		description='This part of my dataset covers a second domain'  ),
 ]

 _lowerCamelCase  :		List[Any] 			=       'first_domain'  # It's not mandatory to have a default configuration. Just use one if it make sense.

 def     __snake_case    (	self				:						Optional[Any]     )       ->							Dict:
        # 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
               _UpperCamelCase   :				Dict       =     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"
               _UpperCamelCase   :				Optional[int]       =     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=lowerCamelCase				,	homepage=_HOMEPAGE				,	license=_LICENSE				,	citation=_CITATION				,	)

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						List[str]     )       ->							List[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
        _UpperCamelCase   :				Optional[int]       =     _URLS[self.config.name]
        _UpperCamelCase   :				Union[str, Any]       =     dl_manager.download_and_extract(lowerCamelCase     )
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN				,	gen_kwargs={
                    'filepath': os.path.join(lowerCamelCase				,	'train.jsonl'     ),
                    'split': 'train',
                }				,	),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION				,	gen_kwargs={
                    'filepath': os.path.join(lowerCamelCase				,	'dev.jsonl'     ),
                    'split': 'dev',
                }				,	),
            datasets.SplitGenerator(
                name=datasets.Split.TEST				,	gen_kwargs={
                    'filepath': os.path.join(lowerCamelCase				,	'test.jsonl'     ),
                    'split': 'test'
                }				,	),
        ]

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						List[str]     )       ->							Union[str, 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(lowerCamelCase				,	encoding='utf-8'     ) as f:
               for key, row in enumerate(lowerCamelCase     ):
                      _UpperCamelCase   :				str       =     json.loads(lowerCamelCase     )
                      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"],
                             }




from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							Any:
        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`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

        _UpperCamelCase  ,					_UpperCamelCase   :				List[str]       =     tokens['attention_mask'], tokens['input_ids']

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							List[Any]:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				str       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				Optional[Any]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				Dict       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Optional[Any]       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Optional[int]       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        if task_inputs is None:
               raise ValueError('You have to specify the task_input. Found None.'     )
        elif images is None:
               raise ValueError('You have to specify the image. Found None.'     )

        if not all(task in ['semantic', 'instance', 'panoptic'] for task in task_inputs     ):
               raise ValueError('task_inputs must be semantic, instance, or panoptic.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				int       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				List[str]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				int       =     self._preprocess_text(lowerCamelCase				,	max_length=self.task_seq_length     )
        else:
               raise TypeError('Task Inputs should be a string or a list of strings.'     )

        if hasattr(lowerCamelCase				,	'text_inputs'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

               _UpperCamelCase   :				Any       =     []
               for texts in texts_list:
                      _UpperCamelCase   :				Any       =     self._preprocess_text(lowerCamelCase				,	max_length=self.max_seq_length     )
                      text_inputs.append(text_input_list.unsqueeze(0     )     )

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



