
"""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()
UpperCamelCase      :							Dict          =					logging.get_logger(__name__)

UpperCamelCase      :							str          =					[
    (
        "protein1",
        "MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA",
    ),
    ("protein2", "MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA"),
    ("protein3", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG"),
    ("protein4", "MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA"),
]

UpperCamelCase      :							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,
}

UpperCamelCase      :							Any          =					list("ARNDCQEGHILKMFPSTWYV")

UpperCamelCase      :							List[str]          =					restypes + ["X"]
UpperCamelCase      :							List[Any]          =					restypes_with_x + ["<pad>", "<mask>", "<cls>", "<sep>", "<eos>"]






def 				A		(							) ->      Optional[Any]:
 with TemporaryDirectory() as tempdir:
  __UpperCamelCase							    =			'\n'.join(lowerCamelCase_						)
  __UpperCamelCase							    =			Path(lowerCamelCase_						) / 'vocab.txt'
  vocab_file.write_text(lowerCamelCase_						)
  __UpperCamelCase							    =			EsmTokenizer(vocab_file=str(lowerCamelCase_						)						)
 __UpperCamelCase							    =			0  # Overlaps with 'A' but that seems to be what they want
 return hf_tokenizer






def 				A		(							snake_case :List[Any]							,   snake_case :List[Any]						) ->      Optional[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 				A		(							snake_case :str							,   snake_case :str							,   snake_case :bool							,   snake_case :str							,   snake_case :str						) ->      Optional[Any]:
 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(lowerCamelCase_							,   lowerCamelCase_						) and key != "trunk":
    setattr(lowerCamelCase_							,   lowerCamelCase_							,   lowerCamelCase_						)
  for key, val in esm.cfg.trunk.items():
   if hasattr(esmfold_config.trunk							,   lowerCamelCase_						) and key != "structure_module":
    setattr(esmfold_config.trunk							,   lowerCamelCase_							,   lowerCamelCase_						)
  for key, val in esm.cfg.trunk.structure_module.items():
   if hasattr(esmfold_config.trunk.structure_module							,   lowerCamelCase_						):
    setattr(esmfold_config.trunk.structure_module							,   lowerCamelCase_							,   lowerCamelCase_						)
 elif hasattr(lowerCamelCase_							,   '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=lowerCamelCase_							,   hidden_size=lowerCamelCase_							,   num_hidden_layers=lowerCamelCase_							,   num_attention_heads=lowerCamelCase_							,   intermediate_size=lowerCamelCase_							,   max_position_embeddings=1_0_2_6							,   layer_norm_eps=1e-5							,   attention_probs_dropout_prob=0.0							,   hidden_dropout_prob=0.0							,   pad_token_id=lowerCamelCase_							,   emb_layer_norm_before=lowerCamelCase_							,   token_dropout=lowerCamelCase_							,   position_embedding_type=lowerCamelCase_							,   is_folding_model=lowerCamelCase_							,   esmfold_config=lowerCamelCase_							,   vocab_list=lowerCamelCase_							,   )
 if classification_head:
  __UpperCamelCase							    =			esm.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our ESM config:'							,   lowerCamelCase_						)

 if model.startswith('esmfold'						):
  __UpperCamelCase							    =			EsmForProteinFolding
 elif classification_head:
  __UpperCamelCase							    =			EsmForSequenceClassification
 else:
  __UpperCamelCase							    =			EsmForMaskedLM
 __UpperCamelCase							    =			model_class(lowerCamelCase_						)
 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'							,   lowerCamelCase_						) 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=lowerCamelCase_							,   add_special_tokens=lowerCamelCase_						)
  __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(lowerCamelCase_						)
  # Prepare tokenizer and make sure it matches
  with TemporaryDirectory() as tempdir:
   __UpperCamelCase							    =			'\n'.join(alphabet.all_toks						)
   __UpperCamelCase							    =			Path(lowerCamelCase_						) / 'vocab.txt'
   vocab_file.write_text(lowerCamelCase_						)
   __UpperCamelCase							    =			EsmTokenizer(vocab_file=str(lowerCamelCase_						)						)

  __UpperCamelCase							    =			hf_tokenizer([row[1] for row in sample_data]							,   return_tensors='pt'							,   padding=lowerCamelCase_						)
  __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(**lowerCamelCase_							,   output_hidden_states=lowerCamelCase_						)
   __UpperCamelCase							    =			our_output['logits']
   if classification_head:
    __UpperCamelCase							    =			esm.model.classification_heads['mnli'](esm.extract_features(lowerCamelCase_						)						)
   else:
    __UpperCamelCase							    =			esm(hf_tokens['input_ids']							,   repr_layers=list(range(9_9_9						)						)						)
    __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(lowerCamelCase_							,   lowerCamelCase_							,   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(lowerCamelCase_							,   lowerCamelCase_							,   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(lowerCamelCase_						).mkdir(parents=lowerCamelCase_							,   exist_ok=lowerCamelCase_						)
  print(f'Saving model to {pytorch_dump_folder_path}'						)
  model.save_pretrained(lowerCamelCase_						)

  del esm  # Free up some memory before continuing

 print(f'Saving tokenizer to {pytorch_dump_folder_path}'						)
 hf_tokenizer.save_pretrained(lowerCamelCase_						)

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


if __name__ == "__main__":
      UpperCamelCase      :							Optional[int]          =					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.")
      UpperCamelCase      :							Any          =					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 torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)

								else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."  )

								return d






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )  )

												output.sort(key=lambda UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]  )
																				temp.append((decoded, output[k][2] * output[k][3])  )

												final_outputs.append(UpperCAmelCase__  )

								return final_outputs
