

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	:   Union[str, Any]	=					logging.get_logger(__name__)

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

__A	:   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,
}

__A	:   Optional[Any]	=					list('ARNDCQEGHILKMFPSTWYV')

__A	:   List[Any]	=					restypes + ['X']
__A	:   Dict	=					restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']




def        __UpperCamelCase     (      )				->Dict:



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




def        __UpperCamelCase     (      _A					:							Optional[int]      ,  _A					:							Tuple		)				->Dict:



							"""simple docstring"""
							lowerCamelCase_								=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     (      _A					:							str      ,  _A					:							str      ,  _A					:							bool      ,  _A					:							str      ,  _A					:							str		)				->int:



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

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

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

							if is_folding_model:
														lowerCamelCase_								=esm.esm
							else:
														lowerCamelCase_								=esm

							lowerCamelCase_								=EsmConfig(
							    vocab_size=original_esm_model.embed_tokens.num_embeddings      ,  mask_token_id=_A      ,  hidden_size=_A      ,  num_hidden_layers=_A      ,  num_attention_heads=_A      ,  intermediate_size=_A      ,  max_position_embeddings=1026      ,  layer_norm_eps=1E-5      ,  attention_probs_dropout_prob=0.0      ,  hidden_dropout_prob=0.0      ,  pad_token_id=_A      ,  emb_layer_norm_before=_A      ,  token_dropout=_A      ,  position_embedding_type=_A      ,  is_folding_model=_A      ,  esmfold_config=_A      ,  vocab_list=_A      ,  )
							if classification_head:
														lowerCamelCase_								=esm.classification_heads["""mnli"""].out_proj.weight.shape[0]
							print("""Our ESM config:"""      ,  _A		)

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

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

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

							lowerCamelCase_								=original_esm_model.emb_layer_norm_after.weight
							lowerCamelCase_								=original_esm_model.emb_layer_norm_after.bias

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

														# self attention
														lowerCamelCase_								=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)		)
														)

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

														if getattr(esm_layer.self_attn      ,  """rot_emb"""      ,  _A		) 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.
																					lowerCamelCase_								=esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

														lowerCamelCase_								=hf_tokenizer([row[1] for row in sample_data]      ,  return_tensors="""pt"""      ,  padding=_A		)
														lowerCamelCase_								=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.
																					lowerCamelCase_								=esm.cuda().infer([row[1] for row in sample_data]		)
																					lowerCamelCase_								=model.cuda()(
																					    input_ids=hf_tokens["""input_ids"""].cuda()      ,  attention_mask=hf_tokens["""attention_mask"""].cuda()		)
														else:
																					lowerCamelCase_								=model(**_A      ,  output_hidden_states=_A		)
																					lowerCamelCase_								=our_output["""logits"""]
																					if classification_head:
																												lowerCamelCase_								=esm.model.classification_heads["""mnli"""](esm.extract_features(_A		)		)
																					else:
																												lowerCamelCase_								=esm(hf_tokens["""input_ids"""]      ,  repr_layers=list(range(999		)		)		)
																												lowerCamelCase_								=their_output["""logits"""]

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

														del esm  # Free up some memory before continuing

							print(f'Saving tokenizer to {pytorch_dump_folder_path}'		)
							hf_tokenizer.save_pretrained(_A		)

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


if __name__ == "__main__":
	__A	:   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	:   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
	)



import math
from collections.abc import Generator




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)		))
							for i in (n for n in numbers if n > 1):
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)		))
							for i in (n for n in numbers if n > 1):
														# only need to check for factors up to sqrt(i)
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(2      ,  _A		):
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      _A					:							int		)				->Generator[int, None, None]:



							"""simple docstring"""
							lowerCamelCase_								=(i for i in range(1      ,  (max_n + 1)      ,  2		))
							# It's useless to test even numbers as they will not be prime
							if max_n > 2:
														yield 2  # Because 2 will not be tested, it's necessary to yield it now
							for i in (n for n in numbers if n > 1):
														lowerCamelCase_								=int(math.sqrt(_A		)		) + 1
														for j in range(3      ,  _A      ,  2		):
																					# As we removed the even numbers, we don't need them now
																					if (i % j) == 0:
																												break
														else:
																					yield i




def        __UpperCamelCase     (      )				->Dict:



							"""simple docstring"""
							from timeit import timeit

							lowerCamelCase_								="""from __main__ import slow_primes, primes, fast_primes"""
							print(timeit("""slow_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)
							print(timeit("""fast_primes(1_000_000_000_000)"""      ,  setup=_A      ,  number=1000000		)		)


if __name__ == "__main__":
	__A	:   Union[str, Any]	=					int(input('Calculate primes up to:\n>> ').strip())
	for ret in primes(number):
		print(ret)
	benchmark()
