



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
lowercase_  =   """\\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
lowercase_  =   """\\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
lowercase_  =   """"""

# TODO: Add the licence for the dataset here if you can find it
lowercase_  =   """"""

# 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)
lowercase_  =   {
    """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):
				UpperCamelCase__ :    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')
				UpperCamelCase__ :    List[str]   						=[
				    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"""),
				]

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




				def 							A__					(  self    :  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
										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=__a,    homepage=_HOMEPAGE,    license=_LICENSE,    citation=_CITATION,    )




				def 							A__					(  self    :  Dict,    __lowercase    :  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(__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 							A__					(  self    :  Union[str, Any],    __lowercase    :  Any,    __lowercase    :  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(__a,    encoding="utf-8"  ) as f:
										for key, row in enumerate(__a  ):
													lowercase__         = 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"],
																}











"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )
