'''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		:			str	      =logging.get_logger(__name__)

_A		:			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		:			List[str]	      =list('''ARNDCQEGHILKMFPSTWYV''')

_A		:			str	      =restypes + ['''X''']
_A		:			int	      =restypes_with_x + ['''<pad>''', '''<mask>''', '''<cls>''', '''<sep>''', '''<eos>''']







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







def 							__UpperCamelCase   (				__UpperCAmelCase     :				List[Any]   ,      __UpperCAmelCase     :				int )       ->  List[Any]:
		lowercase							:       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 							__UpperCamelCase   (				__UpperCAmelCase     :				str   ,      __UpperCAmelCase     :				str   ,      __UpperCAmelCase     :				bool   ,      __UpperCAmelCase     :				str   ,      __UpperCAmelCase     :				str )       ->  List[Any]:
		if model.startswith('''esmfold''' ):
				lowercase							:       Dict											=      MODEL_MAPPING[model]()
		else:
				lowercase							:       List[Any]											=      MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

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

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

		if is_folding_model:
				lowercase							:       List[Any]											=      esm.esm
		else:
				lowercase							:       Union[str, Any]											=      esm

		lowercase							:       List[Any]											=      EsmConfig(
		    vocab_size=original_esm_model.embed_tokens.num_embeddings   ,      mask_token_id=UpperCamelCase__   ,      hidden_size=UpperCamelCase__   ,      num_hidden_layers=UpperCamelCase__   ,      num_attention_heads=UpperCamelCase__   ,      intermediate_size=UpperCamelCase__   ,      max_position_embeddings=1_026   ,      layer_norm_eps=1e-5   ,      attention_probs_dropout_prob=0.0   ,      hidden_dropout_prob=0.0   ,      pad_token_id=UpperCamelCase__   ,      emb_layer_norm_before=UpperCamelCase__   ,      token_dropout=UpperCamelCase__   ,      position_embedding_type=UpperCamelCase__   ,      is_folding_model=UpperCamelCase__   ,      esmfold_config=UpperCamelCase__   ,      vocab_list=UpperCamelCase__   ,      )
		if classification_head:
				lowercase							:       int											=      esm.classification_heads["mnli"].out_proj.weight.shape[0]
		print('''Our ESM config:'''   ,      UpperCamelCase__ )

		if model.startswith('''esmfold''' ):
				lowercase							:       str											=      EsmForProteinFolding
		elif classification_head:
				lowercase							:       Optional[int]											=      EsmForSequenceClassification
		else:
				lowercase							:       Tuple											=      EsmForMaskedLM
		lowercase							:       Any											=      model_class(UpperCamelCase__ )
		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							:       Dict											=      original_esm_model.embed_positions.weight

		if config.emb_layer_norm_before:
				lowercase							:       List[Any]											=      original_esm_model.emb_layer_norm_before.weight
				lowercase							:       Optional[int]											=      original_esm_model.emb_layer_norm_before.bias

		lowercase							:       int											=      original_esm_model.emb_layer_norm_after.weight
		lowercase							:       Tuple											=      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							:       List[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							:       List[str]											=      esm_layer.self_attn.q_proj.weight
				lowercase							:       Optional[int]											=      esm_layer.self_attn.q_proj.bias
				lowercase							:       str											=      esm_layer.self_attn.k_proj.weight
				lowercase							:       List[Any]											=      esm_layer.self_attn.k_proj.bias
				lowercase							:       List[str]											=      esm_layer.self_attn.v_proj.weight
				lowercase							:       Union[str, Any]											=      esm_layer.self_attn.v_proj.bias

				if getattr(esm_layer.self_attn   ,      '''rot_emb'''   ,      UpperCamelCase__ ) 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							:       Union[str, Any]											=      esm_layer.self_attn.rot_emb.inv_freq

				# LayerNorm changes for pre-activation
				lowercase							:       Tuple											=      esm_layer.self_attn_layer_norm.weight
				lowercase							:       Optional[int]											=      esm_layer.self_attn_layer_norm.bias
				lowercase							:       Tuple											=      esm_layer.final_layer_norm.weight
				lowercase							:       str											=      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							:       Tuple											=      esm_layer.self_attn.out_proj.weight
				lowercase							:       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							:       Optional[int]											=      esm_layer.fca.bias

				# output
				lowercase							:       EsmOutput											=      layer.output
				assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
				lowercase							:       Union[str, Any]											=      esm_layer.fca.weight
				lowercase							:       Optional[Any]											=      esm_layer.fca.bias
				# end of layer

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

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

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

				if is_folding_model:
						lowercase							:       Dict											=      torch.max(torch.abs(our_output['''positions'''] - their_output['''positions'''] ) ).item()
						lowercase							:       List[str]											=      torch.allclose(our_output['''positions''']   ,      their_output['''positions''']   ,      atol=1e-5 )
				else:
						lowercase							:       List[Any]											=      torch.max(torch.abs(our_output - their_output ) ).item()
						lowercase							:       List[Any]											=      torch.allclose(UpperCamelCase__   ,      UpperCamelCase__   ,      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							:       int											=      model.predict_contacts(hf_tokens['''input_ids''']   ,      hf_tokens['''attention_mask'''] )
						lowercase							:       Any											=      esm.predict_contacts(hf_tokens['''input_ids'''] )
						lowercase							:       Optional[int]											=      torch.max(torch.abs(our_output - their_output ) ).item()
						lowercase							:       Optional[Any]											=      torch.allclose(UpperCamelCase__   ,      UpperCamelCase__   ,      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(UpperCamelCase__ ).mkdir(parents=UpperCamelCase__   ,      exist_ok=UpperCamelCase__ )
				print(f"""Saving model to {pytorch_dump_folder_path}""" )
				model.save_pretrained(UpperCamelCase__ )

				del esm  # Free up some memory before continuing

		print(f"""Saving tokenizer to {pytorch_dump_folder_path}""" )
		hf_tokenizer.save_pretrained(UpperCamelCase__ )

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


if __name__ == "__main__":
	_A		:			Optional[int]	      =argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
	)
	parser.add_argument('''--model''', default=None, type=str, required=True, help='''Name of model to convert.''')
	parser.add_argument('''--push_to_repo''', type=str, help='''Repo to upload to (including username!).''')
	parser.add_argument('''--auth_token''', type=str, help='''HuggingFace auth token.''')
	_A		:			Union[str, Any]	      =parser.parse_args()
	convert_esm_checkpoint_to_pytorch(
	    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
	)
'''simple docstring'''
def 							__UpperCamelCase   (				__UpperCAmelCase     :				int )       ->  list:
  lowercase							:       Union[str, Any]											=      [True for i in range(n + 1 )]
  lowercase							:       Dict											=      [i - 1 for i in range(n + 1 )]
  lowercase							:       Union[str, Any]											=      []
  for i in range(2   ,      n + 1 ):
    if is_prime[i]:
      primes.append(__UpperCAmelCase )
    for j in range(0   ,      len(__UpperCAmelCase ) ):
      if i * primes[j] >= n:
        break
      lowercase							:       List[str]											=      False

      if i % primes[j] == 0:
        lowercase							:       Optional[Any]											=      totients[i] * primes[j]
        break

      lowercase							:       Union[str, Any]											=      totients[i] * (primes[j] - 1)

  return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()