




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_					(		)      ->			int:
							with TemporaryDirectory() as tempdir:
														lowercase__  :	List[str]        =							"\n".join(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							Path(SCREAMING_SNAKE_CASE_    ) / "vocab.txt"
														vocab_file.write_text(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	str        =							EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE_    )    )
							lowercase__  :	Optional[Any]        =							0  # Overlaps with 'A' but that seems to be what they want
							return hf_tokenizer
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			str:
							lowercase__  :	int        =							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_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			Any:
							if model.startswith("esmfold"    ):
														lowercase__  :	List[str]        =							MODEL_MAPPING[model]()
							else:
														lowercase__ ,       lowercase__  :	List[Any]        =							MODEL_MAPPING[model]()
							esm.eval()  # disable dropout

							if model.startswith("esmfold"    ):
														lowercase__  :	Dict        =							esm.esm.embed_dim
														lowercase__  :	Optional[int]        =							esm.esm.num_layers
														lowercase__  :	Union[str, Any]        =							esm.esm.attention_heads
														lowercase__  :	str        =							4 * embed_dim
														lowercase__  :	Tuple        =							esm.esm.token_dropout
														lowercase__  :	str        =							False  # This code path does not exist in ESM-2
														lowercase__  :	Any        =							"rotary"
														lowercase__  :	Optional[Any]        =							True
														lowercase__  :	str        =							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
														lowercase__  :	Dict        =							esm.args.embed_dim
														lowercase__  :	Any        =							esm.args.layers
														lowercase__  :	int        =							esm.args.attention_heads
														lowercase__  :	Optional[Any]        =							esm.args.ffn_embed_dim
														lowercase__  :	Optional[Any]        =							esm.args.token_dropout
														lowercase__  :	List[str]        =							True if esm.emb_layer_norm_before else False
														lowercase__  :	Union[str, Any]        =							"absolute"
														lowercase__  :	Optional[int]        =							False
														lowercase__  :	List[Any]        =							None
							else:
														# Indicates an ESM-2 model
														lowercase__  :	int        =							esm.embed_dim
														lowercase__  :	List[Any]        =							esm.num_layers
														lowercase__  :	Dict        =							esm.attention_heads
														lowercase__  :	Dict        =							4 * embed_dim  # This is hardcoded in ESM-2
														lowercase__  :	Tuple        =							esm.token_dropout
														lowercase__  :	Dict        =							False  # This code path does not exist in ESM-2
														lowercase__  :	List[Any]        =							"rotary"
														lowercase__  :	int        =							False
														lowercase__  :	str        =							None

							if is_folding_model:
														lowercase__  :	List[str]        =							esm.esm.alphabet
							lowercase__  :	Dict        =							tuple(alphabet.all_toks    )
							lowercase__  :	str        =							alphabet.mask_idx
							lowercase__  :	Tuple        =							alphabet.padding_idx

							if is_folding_model:
														lowercase__  :	List[str]        =							esm.esm
							else:
														lowercase__  :	Dict        =							esm

							lowercase__  :	Optional[int]        =							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=10_26    ,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:
														lowercase__  :	Union[str, Any]        =							esm.classification_heads["mnli"].out_proj.weight.shape[0]
							print("Our ESM config:"    ,SCREAMING_SNAKE_CASE_    )

							if model.startswith("esmfold"    ):
														lowercase__  :	Optional[int]        =							EsmForProteinFolding
							elif classification_head:
														lowercase__  :	Optional[Any]        =							EsmForSequenceClassification
							else:
														lowercase__  :	List[str]        =							EsmForMaskedLM
							lowercase__  :	List[str]        =							model_class(SCREAMING_SNAKE_CASE_    )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							lowercase__  :	Optional[int]        =							original_esm_model.embed_tokens.weight
							if position_embedding_type == "absolute":
														lowercase__  :	str        =							original_esm_model.embed_positions.weight

							if config.emb_layer_norm_before:
														lowercase__  :	Dict        =							original_esm_model.emb_layer_norm_before.weight
														lowercase__  :	List[str]        =							original_esm_model.emb_layer_norm_before.bias

							lowercase__  :	Union[str, Any]        =							original_esm_model.emb_layer_norm_after.weight
							lowercase__  :	List[str]        =							original_esm_model.emb_layer_norm_after.bias

							for i in range(config.num_hidden_layers    ):
														# Encoder: start of layer
														lowercase__  :	EsmLayer        =							model.esm.encoder.layer[i]
														# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
														lowercase__  :	Union[str, Any]        =							original_esm_model.layers[i]

														# self attention
														lowercase__  :	EsmSelfAttention        =							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)    )
														)

														lowercase__  :	str        =							esm_layer.self_attn.q_proj.weight
														lowercase__  :	List[str]        =							esm_layer.self_attn.q_proj.bias
														lowercase__  :	str        =							esm_layer.self_attn.k_proj.weight
														lowercase__  :	List[str]        =							esm_layer.self_attn.k_proj.bias
														lowercase__  :	Union[str, Any]        =							esm_layer.self_attn.v_proj.weight
														lowercase__  :	Dict        =							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.
																					lowercase__  :	List[Any]        =							esm_layer.self_attn.rot_emb.inv_freq

														# LayerNorm changes for pre-activation
														lowercase__  :	Optional[Any]        =							esm_layer.self_attn_layer_norm.weight
														lowercase__  :	int        =							esm_layer.self_attn_layer_norm.bias
														lowercase__  :	str        =							esm_layer.final_layer_norm.weight
														lowercase__  :	List[Any]        =							esm_layer.final_layer_norm.bias

														# self-attention output
														lowercase__  :	EsmSelfOutput        =							layer.attention.output
														assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
														lowercase__  :	List[str]        =							esm_layer.self_attn.out_proj.weight
														lowercase__  :	Union[str, Any]        =							esm_layer.self_attn.out_proj.bias

														# intermediate
														lowercase__  :	EsmIntermediate        =							layer.intermediate
														assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
														lowercase__  :	Union[str, Any]        =							esm_layer.fca.weight
														lowercase__  :	Any        =							esm_layer.fca.bias

														# output
														lowercase__  :	EsmOutput        =							layer.output
														assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
														lowercase__  :	Tuple        =							esm_layer.fca.weight
														lowercase__  :	Optional[int]        =							esm_layer.fca.bias
														# end of layer

							if is_folding_model:
														lowercase__  :	str        =							esm.esm_s_combine.data
														lowercase__  :	int        =							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:
														lowercase__  :	Optional[Any]        =							esm.esm.classification_heads["mnli"].dense.weight
														lowercase__  :	List[str]        =							esm.classification_heads["mnli"].dense.bias
														lowercase__  :	str        =							esm.classification_heads["mnli"].out_proj.weight
														lowercase__  :	Optional[Any]        =							esm.classification_heads["mnli"].out_proj.bias
							else:
														# LM Head
														lowercase__  :	List[str]        =							esm.lm_head.dense.weight
														lowercase__  :	Dict        =							esm.lm_head.dense.bias
														lowercase__  :	str        =							esm.lm_head.layer_norm.weight
														lowercase__  :	Union[str, Any]        =							esm.lm_head.layer_norm.bias
														lowercase__  :	Dict        =							esm.lm_head.weight
														lowercase__  :	Optional[Any]        =							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.
														lowercase__  :	Optional[Any]        =							SAMPLE_DATA[:2]
							else:
														lowercase__  :	Tuple        =							SAMPLE_DATA

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

														lowercase__  :	Any        =							hf_tokenizer([row[1] for row in sample_data]    ,return_tensors="pt"    ,padding=SCREAMING_SNAKE_CASE_    )
														lowercase__  :	List[Any]        =							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.
																					lowercase__  :	Dict        =							esm.cuda().infer([row[1] for row in sample_data]    )
																					lowercase__  :	str        =							model.cuda()(
																					    input_ids=hf_tokens["input_ids"].cuda()    ,attention_mask=hf_tokens["attention_mask"].cuda()    )
														else:
																					lowercase__  :	str        =							model(**SCREAMING_SNAKE_CASE_    ,output_hidden_states=SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Optional[Any]        =							our_output["logits"]
																					if classification_head:
																												lowercase__  :	Optional[Any]        =							esm.model.classification_heads["mnli"](esm.extract_features(SCREAMING_SNAKE_CASE_    )    )
																					else:
																												lowercase__  :	int        =							esm(hf_tokens["input_ids"]    ,repr_layers=list(range(9_99    )    )    )
																												lowercase__  :	Tuple        =							their_output["logits"]

														if is_folding_model:
																					lowercase__  :	Any        =							torch.max(torch.abs(our_output["positions"] - their_output["positions"]    )    ).item()
																					lowercase__  :	Any        =							torch.allclose(our_output["positions"]    ,their_output["positions"]    ,atol=1E-5    )
														else:
																					lowercase__  :	str        =							torch.max(torch.abs(our_output - their_output    )    ).item()
																					lowercase__  :	Optional[int]        =							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
																					lowercase__  :	Union[str, Any]        =							model.predict_contacts(hf_tokens["input_ids"]    ,hf_tokens["attention_mask"]    )
																					lowercase__  :	Tuple        =							esm.predict_contacts(hf_tokens["input_ids"]    )
																					lowercase__  :	Dict        =							torch.max(torch.abs(our_output - their_output    )    ).item()
																					lowercase__  :	List[str]        =							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
						)





import numpy as np
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			int:
							lowercase__  :	str        =							n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							lowercase__  :	Union[str, Any]        =							np.floor(SCREAMING_SNAKE_CASE_    )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							lowercase__  :	Tuple        =							int(n_draws - floored.sum()    )
							if need_to_add > 0:
														lowercase__  :	Union[str, Any]        =							continuous - floored
														lowercase__  :	Optional[Any]        =							np.sort(np.unique(SCREAMING_SNAKE_CASE_    )    )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((lowercase__) ,       )  :	Optional[Any]        =							np.where(remainder == value    )
																					# if we need_to_add less than what's in inds
																					# we draw randomly from them.
																					# if we need to add more, we add them all and
																					# go to the next value
																					lowercase__  :	Dict        =							min(len(SCREAMING_SNAKE_CASE_    )    ,SCREAMING_SNAKE_CASE_    )
																					lowercase__  :	Any        =							rng.choice(SCREAMING_SNAKE_CASE_    ,size=SCREAMING_SNAKE_CASE_    ,replace=SCREAMING_SNAKE_CASE_    )
																					floored[inds] += 1
																					need_to_add -= add_now
																					if need_to_add == 0:
																												break
							return floored.astype(np.intaa    )




def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_=10    )      ->			List[str]:
							lowercase__ ,       lowercase__  :	int        =							np.unique(SCREAMING_SNAKE_CASE_    ,return_inverse=SCREAMING_SNAKE_CASE_    )
							lowercase__  :	int        =							classes.shape[0]
							lowercase__  :	Union[str, Any]        =							np.bincount(SCREAMING_SNAKE_CASE_    )
							if np.min(SCREAMING_SNAKE_CASE_    ) < 2:
														raise ValueError("Minimum class count error"    )
							if n_train < n_classes:
														raise ValueError(
														    "The train_size = %d should be greater or " "equal to the number of classes = %d" % (n_train, n_classes)    )
							if n_test < n_classes:
														raise ValueError(
														    "The test_size = %d should be greater or " "equal to the number of classes = %d" % (n_test, n_classes)    )
							lowercase__  :	List[Any]        =							np.split(np.argsort(SCREAMING_SNAKE_CASE_    ,kind="mergesort"    )    ,np.cumsum(SCREAMING_SNAKE_CASE_    )[:-1]    )
							for _ in range(SCREAMING_SNAKE_CASE_    ):
														lowercase__  :	str        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )
														lowercase__  :	int        =							class_counts - n_i
														lowercase__  :	int        =							approximate_mode(SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )

														lowercase__  :	Tuple        =							[]
														lowercase__  :	int        =							[]

														for i in range(SCREAMING_SNAKE_CASE_    ):
																					lowercase__  :	Union[str, Any]        =							rng.permutation(class_counts[i]    )
																					lowercase__  :	List[Any]        =							class_indices[i].take(SCREAMING_SNAKE_CASE_    ,mode="clip"    )
																					train.extend(perm_indices_class_i[: n_i[i]]    )
																					test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]    )
														lowercase__  :	Tuple        =							rng.permutation(SCREAMING_SNAKE_CASE_    )
														lowercase__  :	Dict        =							rng.permutation(SCREAMING_SNAKE_CASE_    )

														yield train, test