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
__lowerCamelCase     :					Union[str, Any]				  =						'\\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
__lowerCamelCase     :					Union[str, Any]				  =						'\\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
__lowerCamelCase     :					Optional[int]				  =						''

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

# 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)
__lowerCamelCase     :					List[Any]				  =						{
    'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
    'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
}







class __snake_case							(		datasets.GeneratorBasedBuilder						):
   lowerCAmelCase_								=				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_								=				[
       datasets.BuilderConfig(name="first_domain"  ,						version=__snake_case  ,						description="This part of my dataset covers a first domain"						),
       datasets.BuilderConfig(name="second_domain"  ,						version=__snake_case  ,						description="This part of my dataset covers a second domain"						),
   ]

   lowerCAmelCase_								=				"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
       SCREAMING_SNAKE_CASE__     				= 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"
       SCREAMING_SNAKE_CASE__     				= 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=__UpperCamelCase			,      homepage=_HOMEPAGE			,      license=_LICENSE			,      citation=_CITATION			,      )




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= _URLS[self.config.name]
     SCREAMING_SNAKE_CASE__     				= dl_manager.download_and_extract(__UpperCamelCase )
     return [
         datasets.SplitGenerator(
             name=datasets.Split.TRAIN			,      gen_kwargs={
                 """filepath""": os.path.join(__UpperCamelCase			,      """train.jsonl""" ),
                 """split""": """train""",
             }			,      ),
         datasets.SplitGenerator(
             name=datasets.Split.VALIDATION			,      gen_kwargs={
                 """filepath""": os.path.join(__UpperCamelCase			,      """dev.jsonl""" ),
                 """split""": """dev""",
             }			,      ),
         datasets.SplitGenerator(
             name=datasets.Split.TEST			,      gen_kwargs={
                 """filepath""": os.path.join(__UpperCamelCase			,      """test.jsonl""" ),
                 """split""": """test"""
             }			,      ),
     ]




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							int			,      _lowercase     :							Optional[int] ):



     """simple docstring"""






     with open(__UpperCamelCase			,      encoding="""utf-8""" ) as f:
       for key, row in enumerate(__UpperCamelCase ):
         SCREAMING_SNAKE_CASE__     				= json.loads(__UpperCamelCase )
         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 argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			List[str]:
    A_								=					[
        "encoder.version",
        "decoder.version",
        "model.encoder.version",
        "model.decoder.version",
        "_float_tensor",
        "decoder.output_projection.weight",
    ]
    for k in ignore_keys:
        state_dict.pop(snake_case__				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Dict )->			str:
    A_	,	A_								=					emb.weight.shape
    A_								=					nn.Linear(snake_case__				,					snake_case__				,					bias=snake_case__ )
    A_								=					emb.weight.data
    return lin_layer





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      Optional[int]="uclanlp/plbart-base"				,					snake_case__   :      Any=False				,					snake_case__   :      str=False )->			str:
    A_								=					torch.load(snake_case__				,					map_location="cpu" )["model"]
    remove_ignore_keys_(snake_case__ )
    A_								=					state_dict["encoder.embed_tokens.weight"].shape[0]

    A_								=					PLBartConfig.from_pretrained(snake_case__				,					vocab_size=snake_case__ )

    A_								=					state_dict["decoder.embed_tokens.weight"]
    if not classification:
        A_								=					PLBartForConditionalGeneration(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        if finetuned:
            A_								=					make_linear_from_emb(model.model.shared )

    else:
        A_								=					{}
        for key, value in state_dict.copy().items():
            if key.startswith("classification_heads.sentence_classification_head" ):
                A_								=					value
                state_dict.pop(snake_case__ )
        A_								=					PLBartForSequenceClassification(snake_case__ )
        model.model.load_state_dict(snake_case__ )
        model.classification_head.load_state_dict(snake_case__ )

    return model


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('fairseq_path', type=str, help='model.pt on local filesystem.')
     parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument(
         '--hf_config',
         default='uclanlp/plbart-base',
         type=str,
         help='Which huggingface architecture to use: plbart-base',
     )
     parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
     parser.add_argument(
         '--classification', action='store_true', help='whether the model is a classification checkpoint'
     )
     __magic_name__  :  Union[str, Any]     			=   parser.parse_args()
     __magic_name__  :  List[str]     			=   convert_fairseq_plbart_checkpoint_from_disk(
         args.fairseq_path,
         hf_config_path=args.hf_config,
         finetuned=args.finetuned,
         classification=args.classification,
     )
     model.save_pretrained(args.pytorch_dump_folder_path)






