



'''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()
SCREAMING_SNAKE_CASE__     : Any				=   logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE__     : Any				=   {
    """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,
}

SCREAMING_SNAKE_CASE__     : Tuple				=   list("""ARNDCQEGHILKMFPSTWYV""")

SCREAMING_SNAKE_CASE__     : Dict				=   restypes + ["""X"""]
SCREAMING_SNAKE_CASE__     : int				=   restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]







def  SCREAMING_SNAKE_CASE__ (				):
		with TemporaryDirectory() as tempdir:
				SCREAMING_SNAKE_CASE_						:int										=      '\n'.join(SCREAMING_SNAKE_CASE     )
				SCREAMING_SNAKE_CASE_						:Tuple										=      Path(SCREAMING_SNAKE_CASE     ) / 'vocab.txt'
				vocab_file.write_text(SCREAMING_SNAKE_CASE     )
				SCREAMING_SNAKE_CASE_						:Dict										=      EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE     )     )
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      0  # Overlaps with 'A' but that seems to be what they want
		return hf_tokenizer







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Tuple										=      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		if model.startswith('esmfold'     ):
				SCREAMING_SNAKE_CASE_						:int										=      MODEL_MAPPING[model]()
		else:
				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

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

		if is_folding_model:
				SCREAMING_SNAKE_CASE_						:int										=      esm.esm.alphabet
		SCREAMING_SNAKE_CASE_						:List[Any]										=      tuple(alphabet.all_toks     )
		SCREAMING_SNAKE_CASE_						:Dict										=      alphabet.mask_idx
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      alphabet.padding_idx

		if is_folding_model:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm.esm
		else:
				SCREAMING_SNAKE_CASE_						:Tuple										=      esm

		SCREAMING_SNAKE_CASE_						:List[Any]										=      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:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      esm.classification_heads['mnli'].out_proj.weight.shape[0]
		print('Our ESM config:' ,		SCREAMING_SNAKE_CASE     )

		if model.startswith('esmfold'     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      EsmForProteinFolding
		elif classification_head:
				SCREAMING_SNAKE_CASE_						:Tuple										=      EsmForSequenceClassification
		else:
				SCREAMING_SNAKE_CASE_						:int										=      EsmForMaskedLM
		SCREAMING_SNAKE_CASE_						:List[Any]										=      model_class(SCREAMING_SNAKE_CASE     )
		model.eval()

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

		if config.emb_layer_norm_before:
				SCREAMING_SNAKE_CASE_						:Tuple										=      original_esm_model.emb_layer_norm_before.weight
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      original_esm_model.emb_layer_norm_before.bias

		SCREAMING_SNAKE_CASE_						:Optional[int]										=      original_esm_model.emb_layer_norm_after.weight
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      original_esm_model.emb_layer_norm_after.bias

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

				# self attention
				SCREAMING_SNAKE_CASE_						: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)     )
				)

				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      esm_layer.self_attn.q_proj.weight
				SCREAMING_SNAKE_CASE_						:str										=      esm_layer.self_attn.q_proj.bias
				SCREAMING_SNAKE_CASE_						:int										=      esm_layer.self_attn.k_proj.weight
				SCREAMING_SNAKE_CASE_						:int										=      esm_layer.self_attn.k_proj.bias
				SCREAMING_SNAKE_CASE_						:List[str]										=      esm_layer.self_attn.v_proj.weight
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      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.
						SCREAMING_SNAKE_CASE_						:Any										=      esm_layer.self_attn.rot_emb.inv_freq

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

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

				# intermediate
				SCREAMING_SNAKE_CASE_						:EsmIntermediate										=      layer.intermediate
				assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE_						:int										=      esm_layer.fca.weight
				SCREAMING_SNAKE_CASE_						:Optional[Any]										=      esm_layer.fca.bias

				# output
				SCREAMING_SNAKE_CASE_						:EsmOutput										=      layer.output
				assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE_						:List[Any]										=      esm_layer.fca.weight
				SCREAMING_SNAKE_CASE_						:Any										=      esm_layer.fca.bias
				# end of layer

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

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

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

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




from __future__ import annotations

from collections.abc import Callable
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Callable[[int | float], int | float]   ,    lowercase__	:int | float   ,    lowercase__	:int | float   ,    lowercase__	:int = 1_0_0   ,    )							->	float:
						__magic_name__					:				str				=   x_start
						__magic_name__					:				str				=   fnc(lowercase__					)
						__magic_name__					:				Union[str, Any]				=   0.0

						for _ in range(lowercase__					):
												# Approximates small segments of curve as linear and solve
												# for trapezoidal area
												__magic_name__					:				Optional[int]				=   (x_end - x_start) / steps + xa
												__magic_name__					:				List[Any]				=   fnc(lowercase__					)
												area += abs(fxa + fxa					) * (xa - xa) / 2

												# Increment step
												__magic_name__					:				Optional[Any]				=   xa
												__magic_name__					:				Union[str, Any]				=   fxa
						return area


if __name__ == "__main__":
			def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Dict					)							->	Optional[int]:
									return x**3

			print('f(x) = x^3')
			print('The area between the curve, x = -10, x = 10 and the x axis is:')
			snake_case__  				=			10
			while i <= 10_0000:
						snake_case__  				=			trapezoidal_area(f, -5, 5, i)
						print(F"""with {i} steps: {area}""")
						i *= 10


