


import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
a_  :    List[str]      =			logging.get_logger(__name__)

a_  :    List[Any]      =			[
    (
        '''protein1''',
        '''MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA''',
    ),
    ('''protein2''', '''MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'''),
    ('''protein3''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'''),
    ('''protein4''', '''MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'''),
]

a_  :    List[str]      =			{
    '''esm1b_t33_650M_UR50S''': esm_module.pretrained.esm1b_t33_650M_UR50S,
    '''esm1v_t33_650M_UR90S_1''': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    '''esm1v_t33_650M_UR90S_2''': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    '''esm1v_t33_650M_UR90S_3''': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    '''esm1v_t33_650M_UR90S_4''': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    '''esm1v_t33_650M_UR90S_5''': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    '''esm2_t48_15B_UR50D''': esm_module.pretrained.esm2_t48_15B_UR50D,
    '''esm2_t36_3B_UR50D''': esm_module.pretrained.esm2_t36_3B_UR50D,
    '''esm2_t33_650M_UR50D''': esm_module.pretrained.esm2_t33_650M_UR50D,
    '''esm2_t30_150M_UR50D''': esm_module.pretrained.esm2_t30_150M_UR50D,
    '''esm2_t12_35M_UR50D''': esm_module.pretrained.esm2_t12_35M_UR50D,
    '''esm2_t6_8M_UR50D''': esm_module.pretrained.esm2_t6_8M_UR50D,
    '''esmfold_v1''': esmfold_va,
}

a_  :    Dict      =			list("""ARNDCQEGHILKMFPSTWYV""")

a_  :    Union[str, Any]      =			restypes + ['''X''']
a_  :    List[str]      =			restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']





def __snake_case (				):
 with TemporaryDirectory() as tempdir:
  lowerCamelCase_								=  "\n".join(SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_								=  Path(SCREAMING_SNAKE_CASE_		) / "vocab.txt"
  vocab_file.write_text(SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_								=  EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE_		)		)
 lowerCamelCase_								=  0  # Overlaps with 'A' but that seems to be what they want
 return hf_tokenizer





def __snake_case (				UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_								=  our_module.load_state_dict(original_module.state_dict()		)
 if status.missing_keys:
  raise ValueError(F'''Missing keys: {status.missing_keys}'''		)
 if status.unexpected_keys:
  raise ValueError(F'''Unexpected keys: {status.unexpected_keys}'''		)





def __snake_case (				UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						List[str] ,    UpperCAmelCase_						:						List[str]		):
 if model.startswith("esmfold"		):
  lowerCamelCase_								=  MODEL_MAPPING[model]()
 else:
  lowerCamelCase_								=  MODEL_MAPPING[model]()
 esm.eval()  # disable dropout

 if model.startswith("esmfold"		):
  lowerCamelCase_								=  esm.esm.embed_dim
  lowerCamelCase_								=  esm.esm.num_layers
  lowerCamelCase_								=  esm.esm.attention_heads
  lowerCamelCase_								=  4 * embed_dim
  lowerCamelCase_								=  esm.esm.token_dropout
  lowerCamelCase_								=  False  # This code path does not exist in ESM-2
  lowerCamelCase_								=  "rotary"
  lowerCamelCase_								=  True
  lowerCamelCase_								=  EsmFoldConfig()
  for key, val in esm.cfg.items():
   if hasattr(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_		) and key != "trunk":
    setattr(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_		)
  for key, val in esm.cfg.trunk.items():
   if hasattr(esmfold_config.trunk ,    SCREAMING_SNAKE_CASE_		) and key != "structure_module":
    setattr(esmfold_config.trunk ,    SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_		)
  for key, val in esm.cfg.trunk.structure_module.items():
   if hasattr(esmfold_config.trunk.structure_module ,    SCREAMING_SNAKE_CASE_		):
    setattr(esmfold_config.trunk.structure_module ,    SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_		)
 elif hasattr(SCREAMING_SNAKE_CASE_ ,    "args"		):
  # Indicates an ESM-1b or ESM-1v model
  lowerCamelCase_								=  esm.args.embed_dim
  lowerCamelCase_								=  esm.args.layers
  lowerCamelCase_								=  esm.args.attention_heads
  lowerCamelCase_								=  esm.args.ffn_embed_dim
  lowerCamelCase_								=  esm.args.token_dropout
  lowerCamelCase_								=  True if esm.emb_layer_norm_before else False
  lowerCamelCase_								=  "absolute"
  lowerCamelCase_								=  False
  lowerCamelCase_								=  None
 else:
  # Indicates an ESM-2 model
  lowerCamelCase_								=  esm.embed_dim
  lowerCamelCase_								=  esm.num_layers
  lowerCamelCase_								=  esm.attention_heads
  lowerCamelCase_								=  4 * embed_dim  # This is hardcoded in ESM-2
  lowerCamelCase_								=  esm.token_dropout
  lowerCamelCase_								=  False  # This code path does not exist in ESM-2
  lowerCamelCase_								=  "rotary"
  lowerCamelCase_								=  False
  lowerCamelCase_								=  None

 if is_folding_model:
  lowerCamelCase_								=  esm.esm.alphabet
 lowerCamelCase_								=  tuple(alphabet.all_toks		)
 lowerCamelCase_								=  alphabet.mask_idx
 lowerCamelCase_								=  alphabet.padding_idx

 if is_folding_model:
  lowerCamelCase_								=  esm.esm
 else:
  lowerCamelCase_								=  esm

 lowerCamelCase_								=  EsmConfig(
     vocab_size=original_esm_model.embed_tokens.num_embeddings ,    mask_token_id=SCREAMING_SNAKE_CASE_ ,    hidden_size=SCREAMING_SNAKE_CASE_ ,    num_hidden_layers=SCREAMING_SNAKE_CASE_ ,    num_attention_heads=SCREAMING_SNAKE_CASE_ ,    intermediate_size=SCREAMING_SNAKE_CASE_ ,    max_position_embeddings=1026 ,    layer_norm_eps=1E-5 ,    attention_probs_dropout_prob=0.0 ,    hidden_dropout_prob=0.0 ,    pad_token_id=SCREAMING_SNAKE_CASE_ ,    emb_layer_norm_before=SCREAMING_SNAKE_CASE_ ,    token_dropout=SCREAMING_SNAKE_CASE_ ,    position_embedding_type=SCREAMING_SNAKE_CASE_ ,    is_folding_model=SCREAMING_SNAKE_CASE_ ,    esmfold_config=SCREAMING_SNAKE_CASE_ ,    vocab_list=SCREAMING_SNAKE_CASE_ ,    )
 if classification_head:
  lowerCamelCase_								=  esm.classification_heads["mnli"].out_proj.weight.shape[0]
 print("Our ESM config:" ,    SCREAMING_SNAKE_CASE_		)

 if model.startswith("esmfold"		):
  lowerCamelCase_								=  EsmForProteinFolding
 elif classification_head:
  lowerCamelCase_								=  EsmForSequenceClassification
 else:
  lowerCamelCase_								=  EsmForMaskedLM
 lowerCamelCase_								=  model_class(SCREAMING_SNAKE_CASE_		)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 lowerCamelCase_								=  original_esm_model.embed_tokens.weight
 if position_embedding_type == "absolute":
  lowerCamelCase_								=  original_esm_model.embed_positions.weight

 if config.emb_layer_norm_before:
  lowerCamelCase_								=  original_esm_model.emb_layer_norm_before.weight
  lowerCamelCase_								=  original_esm_model.emb_layer_norm_before.bias

 lowerCamelCase_								=  original_esm_model.emb_layer_norm_after.weight
 lowerCamelCase_								=  original_esm_model.emb_layer_norm_after.bias

 for i in range(config.num_hidden_layers		):
  # Encoder: start of layer
  lowerCamelCase_								=  model.esm.encoder.layer[i]
  # esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
  lowerCamelCase_								=  original_esm_model.layers[i]

  # self attention
  lowerCamelCase_								=  layer.attention.self
  assert (
      esm_layer.self_attn.k_proj.weight.data.shape
      == esm_layer.self_attn.q_proj.weight.data.shape
      == esm_layer.self_attn.v_proj.weight.data.shape
      == torch.Size((config.hidden_size, config.hidden_size)		)
  )

  lowerCamelCase_								=  esm_layer.self_attn.q_proj.weight
  lowerCamelCase_								=  esm_layer.self_attn.q_proj.bias
  lowerCamelCase_								=  esm_layer.self_attn.k_proj.weight
  lowerCamelCase_								=  esm_layer.self_attn.k_proj.bias
  lowerCamelCase_								=  esm_layer.self_attn.v_proj.weight
  lowerCamelCase_								=  esm_layer.self_attn.v_proj.bias

  if getattr(esm_layer.self_attn ,    "rot_emb" ,    SCREAMING_SNAKE_CASE_		) is not None:
   # Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
   # During the training of ESM-2 the model was converted to float16 precision, which also converts
   # the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
   # If we recompute inv_freq without this loss of precision then we will get subtly different rotary
   # embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
   # we make sure the new model copies the data from the old inv_freq.
   lowerCamelCase_								=  esm_layer.self_attn.rot_emb.inv_freq

  # LayerNorm changes for pre-activation
  lowerCamelCase_								=  esm_layer.self_attn_layer_norm.weight
  lowerCamelCase_								=  esm_layer.self_attn_layer_norm.bias
  lowerCamelCase_								=  esm_layer.final_layer_norm.weight
  lowerCamelCase_								=  esm_layer.final_layer_norm.bias

  # self-attention output
  lowerCamelCase_								=  layer.attention.output
  assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
  lowerCamelCase_								=  esm_layer.self_attn.out_proj.weight
  lowerCamelCase_								=  esm_layer.self_attn.out_proj.bias

  # intermediate
  lowerCamelCase_								=  layer.intermediate
  assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
  lowerCamelCase_								=  esm_layer.fca.weight
  lowerCamelCase_								=  esm_layer.fca.bias

  # output
  lowerCamelCase_								=  layer.output
  assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
  lowerCamelCase_								=  esm_layer.fca.weight
  lowerCamelCase_								=  esm_layer.fca.bias
  # end of layer

 if is_folding_model:
  lowerCamelCase_								=  esm.esm_s_combine.data
  lowerCamelCase_								=  esm.afa_to_esm.data
  transfer_and_check_weights(esm.embedding ,    model.embedding		)
  transfer_and_check_weights(esm.esm_s_mlp ,    model.esm_s_mlp		)
  transfer_and_check_weights(esm.trunk ,    model.trunk		)
  transfer_and_check_weights(esm.distogram_head ,    model.distogram_head		)
  transfer_and_check_weights(esm.ptm_head ,    model.ptm_head		)
  transfer_and_check_weights(esm.lm_head ,    model.lm_head		)
  transfer_and_check_weights(esm.lddt_head ,    model.lddt_head		)

 elif classification_head:
  lowerCamelCase_								=  esm.esm.classification_heads["mnli"].dense.weight
  lowerCamelCase_								=  esm.classification_heads["mnli"].dense.bias
  lowerCamelCase_								=  esm.classification_heads["mnli"].out_proj.weight
  lowerCamelCase_								=  esm.classification_heads["mnli"].out_proj.bias
 else:
  # LM Head
  lowerCamelCase_								=  esm.lm_head.dense.weight
  lowerCamelCase_								=  esm.lm_head.dense.bias
  lowerCamelCase_								=  esm.lm_head.layer_norm.weight
  lowerCamelCase_								=  esm.lm_head.layer_norm.bias
  lowerCamelCase_								=  esm.lm_head.weight
  lowerCamelCase_								=  esm.lm_head.bias

 # Contact prediction head
 transfer_and_check_weights(esm.contact_head ,    model.esm.contact_head		)

 # Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
 if is_folding_model:
  # Folding models aren't trained on masked inputs and don't like mask tokens.
  lowerCamelCase_								=  SAMPLE_DATA[:2]
 else:
  lowerCamelCase_								=  SAMPLE_DATA

 if is_folding_model:
  lowerCamelCase_								=  get_esmfold_tokenizer()
  lowerCamelCase_								=  hf_tokenizer(
      [row[1] for row in sample_data] ,    return_tensors="pt" ,    padding=SCREAMING_SNAKE_CASE_ ,    add_special_tokens=SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_								=  esmfold_encode_sequences([row[1] for row in sample_data]		)
  lowerCamelCase_								=  torch.all(hf_tokens["input_ids"] == esmfold_aas		) and torch.all(
      hf_tokens["attention_mask"] == esmfold_mask		)
 else:
  # Let's check that we get the same results.
  lowerCamelCase_								=  alphabet.get_batch_converter()
  lowerCamelCase_								=  batch_converter(SCREAMING_SNAKE_CASE_		)
  # Prepare tokenizer and make sure it matches
  with TemporaryDirectory() as tempdir:
   lowerCamelCase_								=  "\n".join(alphabet.all_toks		)
   lowerCamelCase_								=  Path(SCREAMING_SNAKE_CASE_		) / "vocab.txt"
   vocab_file.write_text(SCREAMING_SNAKE_CASE_		)
   lowerCamelCase_								=  EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE_		)		)

  lowerCamelCase_								=  hf_tokenizer([row[1] for row in sample_data] ,    return_tensors="pt" ,    padding=SCREAMING_SNAKE_CASE_		)
  lowerCamelCase_								=  torch.all(hf_tokens["input_ids"] == batch_tokens		)

 print("Do both models tokenizers output the same tokens?" ,    "🔥" if success else "💩"		)
 if not success:
  raise Exception("Tokenization does not match!"		)

 with torch.no_grad():
  if is_folding_model:
   # Let's test the model in parts
   # ESMFold always converts the ESM stem to float16, which requires float16 ops
   # that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
   # ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
   # original and the converted model on the GPU at the same time.
   lowerCamelCase_								=  esm.cuda().infer([row[1] for row in sample_data]		)
   lowerCamelCase_								=  model.cuda()(
       input_ids=hf_tokens["input_ids"].cuda() ,    attention_mask=hf_tokens["attention_mask"].cuda()		)
  else:
   lowerCamelCase_								=  model(**SCREAMING_SNAKE_CASE_ ,    output_hidden_states=SCREAMING_SNAKE_CASE_		)
   lowerCamelCase_								=  our_output["logits"]
   if classification_head:
    lowerCamelCase_								=  esm.model.classification_heads["mnli"](esm.extract_features(SCREAMING_SNAKE_CASE_		)		)
   else:
    lowerCamelCase_								=  esm(hf_tokens["input_ids"] ,    repr_layers=list(range(999		)		)		)
    lowerCamelCase_								=  their_output["logits"]

  if is_folding_model:
   lowerCamelCase_								=  torch.max(torch.abs(our_output["positions"] - their_output["positions"]		)		).item()
   lowerCamelCase_								=  torch.allclose(our_output["positions"] ,    their_output["positions"] ,    atol=1E-5		)
  else:
   lowerCamelCase_								=  torch.max(torch.abs(our_output - their_output		)		).item()
   lowerCamelCase_								=  torch.allclose(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ,    atol=1E-5		)

  print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-5
  print("Do both models output the same tensors?" ,    "🔥" if success else "💩"		)

  if not success:
   raise Exception("Something went wRoNg"		)

  if not is_folding_model:
   # Let's check contact prediction too
   lowerCamelCase_								=  model.predict_contacts(hf_tokens["input_ids"] ,    hf_tokens["attention_mask"]		)
   lowerCamelCase_								=  esm.predict_contacts(hf_tokens["input_ids"]		)
   lowerCamelCase_								=  torch.max(torch.abs(our_output - their_output		)		).item()
   lowerCamelCase_								=  torch.allclose(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ,    atol=1E-5		)

   print("Contact prediction testing:"		)
   print(F'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-5
   print("Do both models output the same tensors?" ,    "🔥" if success else "💩"		)

   if not success:
    raise Exception("Something went wRoNg"		)

  pathlib.Path(SCREAMING_SNAKE_CASE_		).mkdir(parents=SCREAMING_SNAKE_CASE_ ,    exist_ok=SCREAMING_SNAKE_CASE_		)
  print(F'''Saving model to {pytorch_dump_folder_path}'''		)
  model.save_pretrained(SCREAMING_SNAKE_CASE_		)

  del esm  # Free up some memory before continuing

 print(F'''Saving tokenizer to {pytorch_dump_folder_path}'''		)
 hf_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_		)

 if push_to_repo:
  model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE_ ,    use_auth_token=SCREAMING_SNAKE_CASE_		)
  hf_tokenizer.push_to_hub(repo_id=SCREAMING_SNAKE_CASE_ ,    use_auth_token=SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
     a_  :    Union[str, Any]      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--pytorch_dump_folder_path""", type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
     )
     parser.add_argument("""--model""", default=None, type=str, required=True, help="""Name of model to convert.""")
     parser.add_argument("""--push_to_repo""", type=str, help="""Repo to upload to (including username!).""")
     parser.add_argument("""--auth_token""", type=str, help="""HuggingFace auth token.""")
     a_  :    Tuple      =			parser.parse_args()
     convert_esm_checkpoint_to_pytorch(
         args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
     )







'''simple docstring'''

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_  :    Any      =			"""\
@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
a_  :    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
a_  :    List[str]      =			""""""

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

# 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_  :    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 					snake_case   (   datasets.GeneratorBasedBuilder      ):





  """simple docstring"""




  _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=lowercase      ,						description="This part of my dataset covers a first domain"      ),
      datasets.BuilderConfig(name="second_domain"      ,						version=lowercase      ,						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   snake_case		(   self					):







   """simple docstring"""
   # 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=UpperCamelCase      ,  homepage=_HOMEPAGE      ,  license=_LICENSE      ,  citation=_CITATION      ,  )






  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   # 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(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   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # 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(UpperCamelCase      ,  encoding="utf-8"					) as f:
    for key, row in enumerate(UpperCamelCase					):
     lowerCamelCase_								=  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"],
      }



