






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												=      logging.get_logger(__name__)

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

UpperCamelCase												=      {
    '''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												=      list('''ARNDCQEGHILKMFPSTWYV''')

UpperCamelCase												=      restypes + ['''X''']
UpperCamelCase												=      restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']

def 			__lowerCamelCase     (						)				->    int:






						"""simple docstring"""





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

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Any:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    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     (						snake_case__			,snake_case__			,snake_case__			,snake_case__			,snake_case__						)				->    Optional[int]:






						"""simple docstring"""





						if model.startswith("""esmfold"""						):
												_SCREAMING_SNAKE_CASE       =    MODEL_MAPPING[model]()
						else:
												_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    MODEL_MAPPING[model]()
						esm.eval()  # disable dropout

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

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

						if is_folding_model:
												_SCREAMING_SNAKE_CASE       =    esm.esm
						else:
												_SCREAMING_SNAKE_CASE       =    esm

						_SCREAMING_SNAKE_CASE       =    EsmConfig(
						    vocab_size=original_esm_model.embed_tokens.num_embeddings			,mask_token_id=snake_case__			,hidden_size=snake_case__			,num_hidden_layers=snake_case__			,num_attention_heads=snake_case__			,intermediate_size=snake_case__			,max_position_embeddings=10_26			,layer_norm_eps=1e-5			,attention_probs_dropout_prob=0.0			,hidden_dropout_prob=0.0			,pad_token_id=snake_case__			,emb_layer_norm_before=snake_case__			,token_dropout=snake_case__			,position_embedding_type=snake_case__			,is_folding_model=snake_case__			,esmfold_config=snake_case__			,vocab_list=snake_case__			,)
						if classification_head:
												_SCREAMING_SNAKE_CASE       =    esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
						print("""Our ESM config:"""			,snake_case__						)

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

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

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

						_SCREAMING_SNAKE_CASE       =    original_esm_model.emb_layer_norm_after.weight
						_SCREAMING_SNAKE_CASE       =    original_esm_model.emb_layer_norm_after.bias

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

												# self attention
												_SCREAMING_SNAKE_CASE       =    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)						)
												)

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

												if getattr(esm_layer.self_attn			,"""rot_emb"""			,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.
																		_SCREAMING_SNAKE_CASE       =    esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

												_SCREAMING_SNAKE_CASE       =    hf_tokenizer([row[1] for row in sample_data]			,return_tensors="""pt"""			,padding=snake_case__						)
												_SCREAMING_SNAKE_CASE       =    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.
																		_SCREAMING_SNAKE_CASE       =    esm.cuda().infer([row[1] for row in sample_data]						)
																		_SCREAMING_SNAKE_CASE       =    model.cuda()(
																		    input_ids=hf_tokens["""input_ids"""].cuda()			,attention_mask=hf_tokens["""attention_mask"""].cuda()						)
												else:
																		_SCREAMING_SNAKE_CASE       =    model(**snake_case__			,output_hidden_states=snake_case__						)
																		_SCREAMING_SNAKE_CASE       =    our_output["""logits"""]
																		if classification_head:
																								_SCREAMING_SNAKE_CASE       =    esm.model.classification_heads["""mnli"""](esm.extract_features(snake_case__						)						)
																		else:
																								_SCREAMING_SNAKE_CASE       =    esm(hf_tokens["""input_ids"""]			,repr_layers=list(range(9_99						)						)						)
																								_SCREAMING_SNAKE_CASE       =    their_output["""logits"""]

												if is_folding_model:
																		_SCREAMING_SNAKE_CASE       =    torch.max(torch.abs(our_output["""positions"""] - their_output["""positions"""]						)						).item()
																		_SCREAMING_SNAKE_CASE       =    torch.allclose(our_output["""positions"""]			,their_output["""positions"""]			,atol=1e-5						)
												else:
																		_SCREAMING_SNAKE_CASE       =    torch.max(torch.abs(our_output - their_output						)						).item()
																		_SCREAMING_SNAKE_CASE       =    torch.allclose(snake_case__			,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
																		_SCREAMING_SNAKE_CASE       =    model.predict_contacts(hf_tokens["""input_ids"""]			,hf_tokens["""attention_mask"""]						)
																		_SCREAMING_SNAKE_CASE       =    esm.predict_contacts(hf_tokens["""input_ids"""]						)
																		_SCREAMING_SNAKE_CASE       =    torch.max(torch.abs(our_output - their_output						)						).item()
																		_SCREAMING_SNAKE_CASE       =    torch.allclose(snake_case__			,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(snake_case__						).mkdir(parents=snake_case__			,exist_ok=snake_case__						)
												print(F'Saving model to {pytorch_dump_folder_path}'						)
												model.save_pretrained(snake_case__						)

												del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
		UpperCamelCase												=      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												=      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
		)









from __future__ import annotations

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    len(snake_case__						) - 1
						while left <= right:
												_SCREAMING_SNAKE_CASE       =    (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								_SCREAMING_SNAKE_CASE       =    middle + 1
																		break
												elif sorted_list[middle] < item:
																		_SCREAMING_SNAKE_CASE       =    middle + 1
												else:
																		_SCREAMING_SNAKE_CASE       =    middle - 1
						sorted_list.insert(snake_case__			,snake_case__						)
						return sorted_list

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0						)						)
												else:
																		result.append(right.pop(0						)						)
						return result + left + right

def 			__lowerCamelCase     (						snake_case__						)				->    List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    len(snake_case__						)
						if length <= 1:
												return list_ad
						_SCREAMING_SNAKE_CASE       =    length // 2
						return merge(sortlist_ad(list_ad[:middle]						)			,sortlist_ad(list_ad[middle:]						)						)

def 			__lowerCamelCase     (						snake_case__						)				->    list[int]:






						"""simple docstring"""





						if len(snake_case__						) <= 1:
												return collection
						_SCREAMING_SNAKE_CASE       =    []
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(0			,len(snake_case__						)			,2						):
												if i == len(snake_case__						) - 1:
																		_SCREAMING_SNAKE_CASE       =    True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]						)
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]						)
						_SCREAMING_SNAKE_CASE       =    sortlist_ad(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]						)
						if has_last_odd_item:
												_SCREAMING_SNAKE_CASE       =    collection[-1]
												_SCREAMING_SNAKE_CASE       =    binary_search_insertion(snake_case__			,snake_case__						)
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(len(snake_case__						) - 1						):
												if result[i] == collection[-1] and has_last_odd_item:
																		_SCREAMING_SNAKE_CASE       =    True
												_SCREAMING_SNAKE_CASE       =    sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 2] + binary_search_insertion(result[i + 2 :]			,snake_case__						)
												else:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 1] + binary_search_insertion(result[i + 1 :]			,snake_case__						)

						return result


if __name__ == "__main__":
		UpperCamelCase												=      input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase												=      [int(item) for item in user_input.split(''',''')]
		print(merge_insertion_sort(unsorted))

