




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
__SCREAMING_SNAKE_CASE     : int      ="""\
@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
__SCREAMING_SNAKE_CASE     : Optional[Any]      ="""\
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
__SCREAMING_SNAKE_CASE     : Union[str, Any]      =""""""

# TODO: Add the licence for the dataset here if you can find it
__SCREAMING_SNAKE_CASE     : Optional[int]      =""""""

# 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)
__SCREAMING_SNAKE_CASE     : Optional[int]      ={
    """first_domain""": """https://huggingface.co/great-new-dataset-first_domain.zip""",
    """second_domain""": """https://huggingface.co/great-new-dataset-second_domain.zip""",
}






class       A      (		datasets.GeneratorBasedBuilder		):
     """simple docstring"""

     _snake_case:						Optional[int]  =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')
     _snake_case:						Optional[Any]  =[
         datasets.BuilderConfig(name="first_domain"      ,    version=_lowerCAmelCase      ,    description="This part of my dataset covers a first domain"		),
         datasets.BuilderConfig(name="second_domain"      ,    version=_lowerCAmelCase      ,    description="This part of my dataset covers a second domain"		),
     ]

     _snake_case:						Union[str, 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
                 lowerCamelCase__   	=					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"
                 lowerCamelCase__   	=					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=A__						,	homepage=_HOMEPAGE						,	license=_LICENSE						,	citation=_CITATION						,	)




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




     def 			__snake_case (     self	: Any						,	A__	: Any						,	A__	: Dict      )					->							List[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(A__						,	encoding='utf-8'      ) as f:
                 for key, row in enumerate(A__      ):
                       lowerCamelCase__   	=					json.loads(A__      )
                       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 ...configuration_utils import PretrainedConfig
from ...utils import logging


__SCREAMING_SNAKE_CASE     : str      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Any      ={
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						str  ="tvlt"




     def __init__(     self	: List[Any]						,	A__	: List[str]=2_2_4						,	A__	: Optional[int]=2_0_4_8						,	A__	: str=1_2_8						,	A__	: Union[str, Any]=[1_6, 1_6]						,	A__	: List[Any]=[1_6, 1_6]						,	A__	: int=3						,	A__	: Optional[int]=1						,	A__	: Tuple=8						,	A__	: Tuple=7_6_8						,	A__	: List[str]=1_2						,	A__	: Tuple=1_2						,	A__	: int=3_0_7_2						,	A__	: Tuple="gelu"						,	A__	: str=0.0						,	A__	: Dict=0.0						,	A__	: Any=0.02						,	A__	: int=1E-6						,	A__	: List[str]=True						,	A__	: List[str]=False						,	A__	: Optional[Any]=1_6						,	A__	: Optional[Any]=5_1_2						,	A__	: List[Any]=8						,	A__	: Any=2_0_4_8						,	A__	: List[Any]=0.75						,	A__	: List[Any]=0.15						,	A__	: str="frame-level"						,	A__	: Union[str, Any]=True						,	A__	: Optional[int]=True						,	A__	: Optional[Any]="classification"						,	**A__	: int						,	)					->							Tuple:
           super().__init__(**A__      )

           if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
                     F"""got {audio_mask_type}"""      )

           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					spectrogram_length
           lowerCamelCase__   	=					frequency_length
           lowerCamelCase__   	=					image_patch_size
           lowerCamelCase__   	=					audio_patch_size
           lowerCamelCase__   	=					num_image_channels
           lowerCamelCase__   	=					num_audio_channels
           lowerCamelCase__   	=					num_frames

           lowerCamelCase__   	=					hidden_size
           lowerCamelCase__   	=					num_hidden_layers
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					intermediate_size
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					qkv_bias
           lowerCamelCase__   	=					use_mean_pooling

           lowerCamelCase__   	=					decoder_num_attention_heads
           lowerCamelCase__   	=					decoder_hidden_size
           lowerCamelCase__   	=					decoder_num_hidden_layers
           lowerCamelCase__   	=					decoder_intermediate_size
           lowerCamelCase__   	=					pixel_mask_ratio
           lowerCamelCase__   	=					audio_mask_ratio
           lowerCamelCase__   	=					audio_mask_type

           lowerCamelCase__   	=					task_matching
           lowerCamelCase__   	=					task_mae
           lowerCamelCase__   	=					loss_type