

'''simple docstring'''




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			:	Tuple								=  logging.get_logger(__name__)

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

_a			:	Tuple								=  {
    '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			:	List[Any]								=  list('ARNDCQEGHILKMFPSTWYV')

_a			:	Tuple								=  restypes + ['X']
_a			:	int								=  restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']


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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Any    ,				SCREAMING_SNAKE_CASE :				Any  ):
  UpperCAmelCase  							=		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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				bool    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				str  ):
  if model.startswith('esmfold'  ):
    UpperCAmelCase  							=		MODEL_MAPPING[model]()
  else:
    UpperCAmelCase							, UpperCAmelCase  							=		MODEL_MAPPING[model]()
  esm.eval()  # disable dropout

  if model.startswith('esmfold'  ):
    UpperCAmelCase  							=		esm.esm.embed_dim
    UpperCAmelCase  							=		esm.esm.num_layers
    UpperCAmelCase  							=		esm.esm.attention_heads
    UpperCAmelCase  							=		4 * embed_dim
    UpperCAmelCase  							=		esm.esm.token_dropout
    UpperCAmelCase  							=		False  # This code path does not exist in ESM-2
    UpperCAmelCase  							=		'rotary'
    UpperCAmelCase  							=		True
    UpperCAmelCase  							=		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
    UpperCAmelCase  							=		esm.args.embed_dim
    UpperCAmelCase  							=		esm.args.layers
    UpperCAmelCase  							=		esm.args.attention_heads
    UpperCAmelCase  							=		esm.args.ffn_embed_dim
    UpperCAmelCase  							=		esm.args.token_dropout
    UpperCAmelCase  							=		True if esm.emb_layer_norm_before else False
    UpperCAmelCase  							=		'absolute'
    UpperCAmelCase  							=		False
    UpperCAmelCase  							=		None
  else:
    # Indicates an ESM-2 model
    UpperCAmelCase  							=		esm.embed_dim
    UpperCAmelCase  							=		esm.num_layers
    UpperCAmelCase  							=		esm.attention_heads
    UpperCAmelCase  							=		4 * embed_dim  # This is hardcoded in ESM-2
    UpperCAmelCase  							=		esm.token_dropout
    UpperCAmelCase  							=		False  # This code path does not exist in ESM-2
    UpperCAmelCase  							=		'rotary'
    UpperCAmelCase  							=		False
    UpperCAmelCase  							=		None

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

  if is_folding_model:
    UpperCAmelCase  							=		esm.esm
  else:
    UpperCAmelCase  							=		esm

  UpperCAmelCase  							=		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:
    UpperCAmelCase  							=		esm.classification_heads['mnli'].out_proj.weight.shape[0]
  print('Our ESM config:'    ,				SCREAMING_SNAKE_CASE  )

  if model.startswith('esmfold'  ):
    UpperCAmelCase  							=		EsmForProteinFolding
  elif classification_head:
    UpperCAmelCase  							=		EsmForSequenceClassification
  else:
    UpperCAmelCase  							=		EsmForMaskedLM
  UpperCAmelCase  							=		model_class(SCREAMING_SNAKE_CASE  )
  model.eval()

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

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

  UpperCAmelCase  							=		original_esm_model.emb_layer_norm_after.weight
  UpperCAmelCase  							=		original_esm_model.emb_layer_norm_after.bias

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

    # self attention
    UpperCAmelCase  							=		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)  )
    )

    UpperCAmelCase  							=		esm_layer.self_attn.q_proj.weight
    UpperCAmelCase  							=		esm_layer.self_attn.q_proj.bias
    UpperCAmelCase  							=		esm_layer.self_attn.k_proj.weight
    UpperCAmelCase  							=		esm_layer.self_attn.k_proj.bias
    UpperCAmelCase  							=		esm_layer.self_attn.v_proj.weight
    UpperCAmelCase  							=		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.
      UpperCAmelCase  							=		esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

  if is_folding_model:
    UpperCAmelCase  							=		esm.esm_s_combine.data
    UpperCAmelCase  							=		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:
    UpperCAmelCase  							=		esm.esm.classification_heads['mnli'].dense.weight
    UpperCAmelCase  							=		esm.classification_heads['mnli'].dense.bias
    UpperCAmelCase  							=		esm.classification_heads['mnli'].out_proj.weight
    UpperCAmelCase  							=		esm.classification_heads['mnli'].out_proj.bias
  else:
    # LM Head
    UpperCAmelCase  							=		esm.lm_head.dense.weight
    UpperCAmelCase  							=		esm.lm_head.dense.bias
    UpperCAmelCase  							=		esm.lm_head.layer_norm.weight
    UpperCAmelCase  							=		esm.lm_head.layer_norm.bias
    UpperCAmelCase  							=		esm.lm_head.weight
    UpperCAmelCase  							=		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.
    UpperCAmelCase  							=		SAMPLE_DATA[:2]
  else:
    UpperCAmelCase  							=		SAMPLE_DATA

  if is_folding_model:
    UpperCAmelCase  							=		get_esmfold_tokenizer()
    UpperCAmelCase  							=		hf_tokenizer(
        [row[1] for row in sample_data]    ,				return_tensors='pt'    ,				padding=SCREAMING_SNAKE_CASE    ,				add_special_tokens=SCREAMING_SNAKE_CASE  )
    UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		esmfold_encode_sequences([row[1] for row in sample_data]  )
    UpperCAmelCase  							=		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.
    UpperCAmelCase  							=		alphabet.get_batch_converter()
    UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		batch_converter(SCREAMING_SNAKE_CASE  )
    # Prepare tokenizer and make sure it matches
    with TemporaryDirectory() as tempdir:
      UpperCAmelCase  							=		'\n'.join(alphabet.all_toks  )
      UpperCAmelCase  							=		Path(SCREAMING_SNAKE_CASE  ) / 'vocab.txt'
      vocab_file.write_text(SCREAMING_SNAKE_CASE  )
      UpperCAmelCase  							=		EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE  )  )

    UpperCAmelCase  							=		hf_tokenizer([row[1] for row in sample_data]    ,				return_tensors='pt'    ,				padding=SCREAMING_SNAKE_CASE  )
    UpperCAmelCase  							=		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.
      UpperCAmelCase  							=		esm.cuda().infer([row[1] for row in sample_data]  )
      UpperCAmelCase  							=		model.cuda()(
          input_ids=hf_tokens['input_ids'].cuda()    ,				attention_mask=hf_tokens['attention_mask'].cuda()  )
    else:
      UpperCAmelCase  							=		model(**SCREAMING_SNAKE_CASE    ,				output_hidden_states=SCREAMING_SNAKE_CASE  )
      UpperCAmelCase  							=		our_output['logits']
      if classification_head:
        UpperCAmelCase  							=		esm.model.classification_heads['mnli'](esm.extract_features(SCREAMING_SNAKE_CASE  )  )
      else:
        UpperCAmelCase  							=		esm(hf_tokens['input_ids']    ,				repr_layers=list(range(999  )  )  )
        UpperCAmelCase  							=		their_output['logits']

    if is_folding_model:
      UpperCAmelCase  							=		torch.max(torch.abs(our_output['positions'] - their_output['positions']  )  ).item()
      UpperCAmelCase  							=		torch.allclose(our_output['positions']    ,				their_output['positions']    ,				atol=1E-5  )
    else:
      UpperCAmelCase  							=		torch.max(torch.abs(our_output - their_output  )  ).item()
      UpperCAmelCase  							=		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
      UpperCAmelCase  							=		model.predict_contacts(hf_tokens['input_ids']    ,				hf_tokens['attention_mask']  )
      UpperCAmelCase  							=		esm.predict_contacts(hf_tokens['input_ids']  )
      UpperCAmelCase  							=		torch.max(torch.abs(our_output - their_output  )  ).item()
      UpperCAmelCase  							=		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			:	List[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 getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_a			:	Any								=  False
_a			:	Union[str, Any]								=  Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_a			:	Dict								=  datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_=sys.argv[1:]       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		1
    UpperCAmelCase							, UpperCAmelCase  							=		getopt.getopt(a_				,	'i:o:b:s'				,	['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']       )
    for opt, arg in opts:
      if opt in ("-i", "--inputdir"):
        UpperCAmelCase  							=		arg
      elif opt in ("-o", "--outfile"):
        UpperCAmelCase  							=		arg
      elif opt in ("-b", "--batch_size"):
        UpperCAmelCase  							=		int(a_       )
      elif opt in ("-s", "--subset_list"):
        UpperCAmelCase  							=		arg

    assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
    assert outputfile is not None and not os.path.isfile(a_       ), F'''{outputfile}'''
    if subset_list is not None:
      with open(os.path.realpath(a_       )       ) as f:
        UpperCAmelCase  							=		{self._vqa_file_split()[0] for x in tryload(a_       )}
    else:
      UpperCAmelCase  							=		None

    UpperCAmelCase  							=		CONFIG
    if torch.cuda.is_available():
      UpperCAmelCase  							=		'cuda'
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		os.path.realpath(a_       )
    UpperCAmelCase  							=		Preprocess(self.config       )
    UpperCAmelCase  							=		GeneralizedRCNN.from_pretrained('unc-nlp/frcnn-vg-finetuned'				,	config=self.config       )
    UpperCAmelCase  							=		batch_size if batch_size != 0 else 1
    UpperCAmelCase  							=		DEFAULT_SCHEMA
  def   snake_case_     (       self				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		int(file.split('.'       )[0].split('_'       )[-1]       )
    UpperCAmelCase  							=		os.path.join(self.inputdir				,	a_       )
    return (img_id, filepath)
  @property
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		[]
    for i, file in enumerate(os.listdir(self.inputdir       )       ):
      if self.subset_list is not None and i not in self.subset_list:
        continue
      batch.append(self._vqa_file_split(a_       )       )
      if len(a_       ) == self.batch:
        UpperCAmelCase  							=		batch
        UpperCAmelCase  							=		[]
        yield list(map(a_				,	zip(*a_       )       )       )

    for i in range(1       ):
      yield list(map(a_				,	zip(*a_       )       )       )





  def __call__(       self       )							->			str:


    """simple docstring"""
    # make writer
    if not TEST:
      UpperCAmelCase  							=		datasets.ArrowWriter(features=self.schema				,	path=self.outputfile       )
    # do file generator
    for i, (img_ids, filepaths) in enumerate(self.file_generator       ):
      UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		self.preprocess(a_       )
      UpperCAmelCase  							=		self.model(
          a_				,	a_				,	scales_yx=a_				,	padding='max_detections'				,	max_detections=self.config.MAX_DETECTIONS				,	pad_value=0				,	return_tensors='np'				,	location='cpu'				,	)
      UpperCAmelCase  							=		output_dict.pop('normalized_boxes'       )
      if not TEST:
        UpperCAmelCase  							=		np.array(a_       )
        UpperCAmelCase  							=		self.schema.encode_batch(a_       )
        writer.write_batch(a_       )
      if TEST:
        break
      # finalizer the writer
    if not TEST:
      UpperCAmelCase							, UpperCAmelCase  							=		writer.finalize()
      print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''       )




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				int  ):
  try:
    UpperCAmelCase  							=		json.load(SCREAMING_SNAKE_CASE  )
    try:
      UpperCAmelCase  							=		list(data.keys()  )
    except Exception:
      UpperCAmelCase  							=		[d['img_id'] for d in data]
  except Exception:
    try:
      UpperCAmelCase  							=		eval(stream.read()  )
    except Exception:
      UpperCAmelCase  							=		stream.read().split('\n'  )
  return data


if __name__ == "__main__":
  _a			:	Optional[Any]								=  Extract(sys.argv[1:])
  extract()
  if not TEST:
    _a			:	Tuple								=  datasets.Dataset.from_file(extract.outputfile)
    # wala!
    # print(np.array(dataset[0:2]["roi_features"]).shape)


