


'''simple docstring'''






import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
_A      =     logging.get_logger(__name__)

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

_A      =     {
    """esm1b_t33_650M_UR50S""": esm_module.pretrained.esm1b_t33_650M_UR50S,
    """esm1v_t33_650M_UR90S_1""": esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    """esm1v_t33_650M_UR90S_2""": esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    """esm1v_t33_650M_UR90S_3""": esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    """esm1v_t33_650M_UR90S_4""": esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    """esm1v_t33_650M_UR90S_5""": esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    """esm2_t48_15B_UR50D""": esm_module.pretrained.esm2_t48_15B_UR50D,
    """esm2_t36_3B_UR50D""": esm_module.pretrained.esm2_t36_3B_UR50D,
    """esm2_t33_650M_UR50D""": esm_module.pretrained.esm2_t33_650M_UR50D,
    """esm2_t30_150M_UR50D""": esm_module.pretrained.esm2_t30_150M_UR50D,
    """esm2_t12_35M_UR50D""": esm_module.pretrained.esm2_t12_35M_UR50D,
    """esm2_t6_8M_UR50D""": esm_module.pretrained.esm2_t6_8M_UR50D,
    """esmfold_v1""": esmfold_va,
}

_A      =     list("""ARNDCQEGHILKMFPSTWYV""")

_A      =     restypes + ["""X"""]
_A      =     restypes_with_x + ["""<pad>""", """<mask>""", """<cls>""", """<sep>""", """<eos>"""]


def A_					(				)     ->  str:
				with TemporaryDirectory() as tempdir:
								__SCREAMING_SNAKE_CASE					:							Dict			   = '''\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					:							int			   = EsmTokenizer(vocab_file=str(__SCREAMING_SNAKE_CASE       )       )
				__SCREAMING_SNAKE_CASE					:							List[str]			   = 0  # Overlaps with 'A' but that seems to be what they want
				return hf_tokenizer


def A_					(				__SCREAMING_SNAKE_CASE :					Union[str, Any]	,   __SCREAMING_SNAKE_CASE :					Dict       )     ->  List[Any]:
				__SCREAMING_SNAKE_CASE					:							List[Any]			   = 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_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					bool	,   __SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					str       )     ->  List[str]:
				if model.startswith('''esmfold'''       ):
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = MODEL_MAPPING[model]()
				else:
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

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

				if is_folding_model:
								__SCREAMING_SNAKE_CASE					:							int			   = esm.esm.alphabet
				__SCREAMING_SNAKE_CASE					:							Dict			   = tuple(alphabet.all_toks       )
				__SCREAMING_SNAKE_CASE					:							str			   = 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					:							Any			   = esm

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

				if model.startswith('''esmfold'''       ):
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = EsmForProteinFolding
				elif classification_head:
								__SCREAMING_SNAKE_CASE					:							Tuple			   = EsmForSequenceClassification
				else:
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = EsmForMaskedLM
				__SCREAMING_SNAKE_CASE					:							Union[str, 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					:							int			   = original_esm_model.embed_positions.weight

				if config.emb_layer_norm_before:
								__SCREAMING_SNAKE_CASE					:							Any			   = original_esm_model.emb_layer_norm_before.weight
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = original_esm_model.emb_layer_norm_before.bias

				__SCREAMING_SNAKE_CASE					:							str			   = original_esm_model.emb_layer_norm_after.weight
				__SCREAMING_SNAKE_CASE					:							List[str]			   = 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					:							str			   = 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					:							Tuple			   = esm_layer.self_attn.q_proj.weight
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = esm_layer.self_attn.q_proj.bias
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = esm_layer.self_attn.k_proj.weight
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = esm_layer.self_attn.k_proj.bias
								__SCREAMING_SNAKE_CASE					:							Dict			   = esm_layer.self_attn.v_proj.weight
								__SCREAMING_SNAKE_CASE					:							Any			   = 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					:							Union[str, Any]			   = esm_layer.self_attn.rot_emb.inv_freq

								# LayerNorm changes for pre-activation
								__SCREAMING_SNAKE_CASE					:							Tuple			   = esm_layer.self_attn_layer_norm.weight
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = esm_layer.self_attn_layer_norm.bias
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = esm_layer.final_layer_norm.weight
								__SCREAMING_SNAKE_CASE					:							Dict			   = 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					:							int			   = 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					:							List[Any]			   = esm_layer.fca.weight
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = 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					:							str			   = esm_layer.fca.weight
								__SCREAMING_SNAKE_CASE					:							List[str]			   = esm_layer.fca.bias
								# end of layer

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

				if is_folding_model:
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = get_esmfold_tokenizer()
								__SCREAMING_SNAKE_CASE					:							Dict			   = 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					:							str			   = esmfold_encode_sequences([row[1] for row in sample_data]       )
								__SCREAMING_SNAKE_CASE					:							int			   = 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					:							Tuple			   = alphabet.get_batch_converter()
								__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					:							Optional[Any]			   = Path(__SCREAMING_SNAKE_CASE       ) / '''vocab.txt'''
												vocab_file.write_text(__SCREAMING_SNAKE_CASE       )
												__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = EsmTokenizer(vocab_file=str(__SCREAMING_SNAKE_CASE       )       )

								__SCREAMING_SNAKE_CASE					:							Dict			   = hf_tokenizer([row[1] for row in sample_data]	,   return_tensors='''pt'''	,   padding=__SCREAMING_SNAKE_CASE       )
								__SCREAMING_SNAKE_CASE					:							Optional[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					:							List[str]			   = esm.cuda().infer([row[1] for row in sample_data]       )
												__SCREAMING_SNAKE_CASE					:							int			   = model.cuda()(
												    input_ids=hf_tokens['''input_ids'''].cuda()	,   attention_mask=hf_tokens['''attention_mask'''].cuda()       )
								else:
												__SCREAMING_SNAKE_CASE					:							List[Any]			   = model(**__SCREAMING_SNAKE_CASE	,   output_hidden_states=__SCREAMING_SNAKE_CASE       )
												__SCREAMING_SNAKE_CASE					:							Dict			   = 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					:							Optional[Any]			   = esm(hf_tokens['''input_ids''']	,   repr_layers=list(range(9_99       )       )       )
																__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = their_output['''logits''']

								if is_folding_model:
												__SCREAMING_SNAKE_CASE					:							int			   = torch.max(torch.abs(our_output['''positions'''] - their_output['''positions''']       )       ).item()
												__SCREAMING_SNAKE_CASE					:							List[str]			   = torch.allclose(our_output['''positions''']	,   their_output['''positions''']	,   atol=1E-5       )
								else:
												__SCREAMING_SNAKE_CASE					:							List[Any]			   = torch.max(torch.abs(our_output - their_output       )       ).item()
												__SCREAMING_SNAKE_CASE					:							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
												__SCREAMING_SNAKE_CASE					:							Dict			   = model.predict_contacts(hf_tokens['''input_ids''']	,   hf_tokens['''attention_mask''']       )
												__SCREAMING_SNAKE_CASE					:							List[str]			   = esm.predict_contacts(hf_tokens['''input_ids''']       )
												__SCREAMING_SNAKE_CASE					:							List[Any]			   = torch.max(torch.abs(our_output - their_output       )       ).item()
												__SCREAMING_SNAKE_CASE					:							Any			   = 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      =     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      =     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 argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_A      =     {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = search.split()
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = dict((g.split('''='''       ) for g in groups)       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(entries.keys()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [[f"""--{k} {v}""" for v in vs.split(''':'''       )] for k, vs in entries.items()]
					__SCREAMING_SNAKE_CASE					:							int			   = [list(__SCREAMING_SNAKE_CASE       ) for x in itertools.product(*__SCREAMING_SNAKE_CASE       )]
					return matrix, entry_names


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = argparse.ArgumentParser(
					    usage=(
					        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
					        ''' refer to `run_eval.py -h` for the complete list.'''
					    )       )
					parser.add_argument(
					    '''--search'''	,   type=__SCREAMING_SNAKE_CASE	,   required=__SCREAMING_SNAKE_CASE	,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''	,   )
					parser.add_argument(
					    '''--bs'''	,   type=__SCREAMING_SNAKE_CASE	,   default=8	,   required=__SCREAMING_SNAKE_CASE	,   help='''initial batch size (may get reduced if it\'s too big)'''       )
					parser.add_argument('''--task'''	,   type=__SCREAMING_SNAKE_CASE	,   help='''used for task_specific_params + metrics'''       )
					parser.add_argument(
					    '''--info'''	,   nargs='''?'''	,   type=__SCREAMING_SNAKE_CASE	,   const=datetime_now()	,   help=(
					        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
					        ''' string will be used.'''
					    )	,   )
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = parser.parse_known_args()
					# we share some of the args
					args_main.extend(['''--task''', args.task]       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = [prog] + args_main

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = parse_search_arg(args.search       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = task_score_names[task]  # score cols first
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {col: len(str(__SCREAMING_SNAKE_CASE       )       ) for col in col_names}
					__SCREAMING_SNAKE_CASE					:							Dict			   = []
					for r in matrix:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = dict((x.replace('''--'''	,   ''''''       ).split() for x in r)       )
										__SCREAMING_SNAKE_CASE					:							Tuple			   = ''' '''.join(__SCREAMING_SNAKE_CASE       ).split()
										args_exp.extend(['''--bs''', str(args.bs       )]       )  # in case we need to reduce its size due to CUDA OOM
										__SCREAMING_SNAKE_CASE					:							Dict			   = args_normal + args_exp

										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = run_generate(verbose=__SCREAMING_SNAKE_CASE       )
										# make sure scores are first in the table
										__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict()
										for score in task_score_names[task]:
															__SCREAMING_SNAKE_CASE					:							int			   = scores[score]
										result.update(__SCREAMING_SNAKE_CASE       )
										results.append(__SCREAMING_SNAKE_CASE       )

										# find widest entries
										for k, v in result.items():
															__SCREAMING_SNAKE_CASE					:							List[Any]			   = len(str(__SCREAMING_SNAKE_CASE       )       )
															if l > col_widths[k]:
																				__SCREAMING_SNAKE_CASE					:							Any			   = l

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					print(''' | '''.join([f"""{col:{col_widths[col]}}""" for col in col_names]       )       )
					print(''' | '''.join([f"""{"-"*col_widths[col]}""" for col in col_names]       )       )
					for row in results_sorted:
										print(''' | '''.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--bs''', str(args.bs       )]
					if args.info:
										print(f"""\nInfo: {args.info}"""       )
					print('''\nBest score args:'''       )
					print(''' '''.join(args_main + best_args + dyn_args       )       )

					return results_sorted


if __name__ == "__main__":
						# Usage:
						# [normal-run_eval_search.py cmd plus] \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						#
						# Example:
						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						run_search()


