


"""simple docstring"""






import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


_lowerCamelCase  =	logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class       lowerCamelCase_		(      lowerCAmelCase_ ):





				"""simple docstring"""







				_lowerCAmelCase   :   torch.FloatTensor
				_lowerCAmelCase   :   Optional[torch.FloatTensor]   				=       None


def __lowercase  (   lowerCamelCase_	:   Any    ,       lowerCamelCase_	:   List[str]=0.999    ,       lowerCamelCase_	:   Dict="cosine"    ,       ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_	:   Optional[Any]	):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_	:   int	):
												return math.exp(t * -12.0	)

				else:
								raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

				SCREAMING_SNAKE_CASE__  				=					[]
				for i in range(UpperCamelCase_	):
								SCREAMING_SNAKE_CASE__  				=					i / num_diffusion_timesteps
								SCREAMING_SNAKE_CASE__  				=					(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(UpperCamelCase_	) / alpha_bar_fn(UpperCamelCase_	)    ,       UpperCamelCase_	)	)
				return torch.tensor(UpperCamelCase_    ,       dtype=torch.floataa	)


class       lowerCamelCase_		(      lowerCAmelCase_							,	lowerCAmelCase_ ):





				"""simple docstring"""







				_lowerCAmelCase   :   str   				=       [e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase   :   Tuple   				=       1



				@register_to_config
				def __init__(    self							,		UpperCAmelCase__ = 1000							,		UpperCAmelCase__ = 0.0_001							,		UpperCAmelCase__ = 0.02							,		UpperCAmelCase__ = "linear"							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = "epsilon"							,		UpperCAmelCase__ = "linear"							,		UpperCAmelCase__ = False							,		UpperCAmelCase__ = "linspace"							,		UpperCAmelCase__ = 0							,		):


								if trained_betas is not None:
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(_SCREAMING_SNAKE_CASE							,		dtype=torch.floataa  )
								elif beta_schedule == "linear":
												SCREAMING_SNAKE_CASE__  				=					torch.linspace(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		dtype=torch.floataa  )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												SCREAMING_SNAKE_CASE__  				=					(
												    torch.linspace(beta_start**0.5							,		beta_end**0.5							,		_SCREAMING_SNAKE_CASE							,		dtype=torch.floataa  ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												SCREAMING_SNAKE_CASE__  				=					betas_for_alpha_bar(_SCREAMING_SNAKE_CASE  )
								else:
												raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

								SCREAMING_SNAKE_CASE__  				=					1.0 - self.betas
								SCREAMING_SNAKE_CASE__  				=					torch.cumprod(self.alphas							,		dim=0  )

								SCREAMING_SNAKE_CASE__  				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
								SCREAMING_SNAKE_CASE__  				=					np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_SCREAMING_SNAKE_CASE  )

								# setable values
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					np.linspace(0							,		num_train_timesteps - 1							,		_SCREAMING_SNAKE_CASE							,		dtype=_SCREAMING_SNAKE_CASE  )[::-1].copy()
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_SCREAMING_SNAKE_CASE  )
								SCREAMING_SNAKE_CASE__  				=					False
								SCREAMING_SNAKE_CASE__  				=					use_karras_sigmas



				@property
				def 	lowerCAmelCase__		(    self  ):


								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):


								if isinstance(_SCREAMING_SNAKE_CASE							,		torch.Tensor  ):
												SCREAMING_SNAKE_CASE__  				=					timestep.to(self.timesteps.device  )
								SCREAMING_SNAKE_CASE__  				=					(self.timesteps == timestep).nonzero().item()
								SCREAMING_SNAKE_CASE__  				=					self.sigmas[step_index]

								SCREAMING_SNAKE_CASE__  				=					sample / ((sigma**2 + 1) ** 0.5)

								SCREAMING_SNAKE_CASE__  				=					True
								return sample



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__ = None  ):
								SCREAMING_SNAKE_CASE__  				=					num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												SCREAMING_SNAKE_CASE__  				=					np.linspace(0							,		self.config.num_train_timesteps - 1							,		_SCREAMING_SNAKE_CASE							,		dtype=_SCREAMING_SNAKE_CASE  )[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												SCREAMING_SNAKE_CASE__  				=					self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												SCREAMING_SNAKE_CASE__  				=					(np.arange(0							,		_SCREAMING_SNAKE_CASE  ) * step_ratio).round()[::-1].copy().astype(_SCREAMING_SNAKE_CASE  )
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												SCREAMING_SNAKE_CASE__  				=					self.config.num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												SCREAMING_SNAKE_CASE__  				=					(np.arange(self.config.num_train_timesteps							,		0							,		-step_ratio  )).round().copy().astype(_SCREAMING_SNAKE_CASE  )
												timesteps -= 1
								else:
												raise ValueError(
												    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''  )

								SCREAMING_SNAKE_CASE__  				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
								SCREAMING_SNAKE_CASE__  				=					np.log(_SCREAMING_SNAKE_CASE  )

								if self.config.interpolation_type == "linear":
												SCREAMING_SNAKE_CASE__  				=					np.interp(_SCREAMING_SNAKE_CASE							,		np.arange(0							,		len(_SCREAMING_SNAKE_CASE  )  )							,		_SCREAMING_SNAKE_CASE  )
								elif self.config.interpolation_type == "log_linear":
												SCREAMING_SNAKE_CASE__  				=					torch.linspace(np.log(sigmas[-1]  )							,		np.log(sigmas[0]  )							,		num_inference_steps + 1  ).exp()
								else:
												raise ValueError(
												    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
												    " 'linear' or 'log_linear'"  )

								if self.use_karras_sigmas:
												SCREAMING_SNAKE_CASE__  				=					self._convert_to_karras(in_sigmas=_SCREAMING_SNAKE_CASE							,		num_inference_steps=self.num_inference_steps  )
												SCREAMING_SNAKE_CASE__  				=					np.array([self._sigma_to_t(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE  ) for sigma in sigmas]  )

								SCREAMING_SNAKE_CASE__  				=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
								SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_SCREAMING_SNAKE_CASE  ).to(device=_SCREAMING_SNAKE_CASE  )
								if str(_SCREAMING_SNAKE_CASE  ).startswith("mps"  ):
												# mps does not support float64
												SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_SCREAMING_SNAKE_CASE  ).to(_SCREAMING_SNAKE_CASE							,		dtype=torch.floataa  )
								else:
												SCREAMING_SNAKE_CASE__  				=					torch.from_numpy(_SCREAMING_SNAKE_CASE  ).to(device=_SCREAMING_SNAKE_CASE  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					np.log(_SCREAMING_SNAKE_CASE  )

								# get distribution
								SCREAMING_SNAKE_CASE__  				=					log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								SCREAMING_SNAKE_CASE__  				=					np.cumsum((dists >= 0)							,		axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
								SCREAMING_SNAKE_CASE__  				=					low_idx + 1

								SCREAMING_SNAKE_CASE__  				=					log_sigmas[low_idx]
								SCREAMING_SNAKE_CASE__  				=					log_sigmas[high_idx]

								# interpolate sigmas
								SCREAMING_SNAKE_CASE__  				=					(low - log_sigma) / (low - high)
								SCREAMING_SNAKE_CASE__  				=					np.clip(_SCREAMING_SNAKE_CASE							,		0							,		1  )

								# transform interpolation to time range
								SCREAMING_SNAKE_CASE__  				=					(1 - w) * low_idx + w * high_idx
								SCREAMING_SNAKE_CASE__  				=					t.reshape(sigma.shape  )
								return t



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					in_sigmas[-1].item()
								SCREAMING_SNAKE_CASE__  				=					in_sigmas[0].item()

								SCREAMING_SNAKE_CASE__  				=					7.0  # 7.0 is the value used in the paper
								SCREAMING_SNAKE_CASE__  				=					np.linspace(0							,		1							,		_SCREAMING_SNAKE_CASE  )
								SCREAMING_SNAKE_CASE__  				=					sigma_min ** (1 / rho)
								SCREAMING_SNAKE_CASE__  				=					sigma_max ** (1 / rho)
								SCREAMING_SNAKE_CASE__  				=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = 0.0							,		UpperCAmelCase__ = 0.0							,		UpperCAmelCase__ = float("inf"  )							,		UpperCAmelCase__ = 1.0							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = True							,		):


								if (
								    isinstance(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE  )
								    or isinstance(_SCREAMING_SNAKE_CASE							,		torch.IntTensor  )
								    or isinstance(_SCREAMING_SNAKE_CASE							,		torch.LongTensor  )
								):
												raise ValueError(
												    (
												        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
												        " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
												        " one of the `scheduler.timesteps` as a timestep."
												    )							,		)

								if not self.is_scale_input_called:
												logger.warning(
												    "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
												    "See `StableDiffusionPipeline` for a usage example."  )

								if isinstance(_SCREAMING_SNAKE_CASE							,		torch.Tensor  ):
												SCREAMING_SNAKE_CASE__  				=					timestep.to(self.timesteps.device  )

								SCREAMING_SNAKE_CASE__  				=					(self.timesteps == timestep).nonzero().item()
								SCREAMING_SNAKE_CASE__  				=					self.sigmas[step_index]

								SCREAMING_SNAKE_CASE__  				=					min(s_churn / (len(self.sigmas  ) - 1)							,		2**0.5 - 1  ) if s_tmin <= sigma <= s_tmax else 0.0

								SCREAMING_SNAKE_CASE__  				=					randn_tensor(
								    model_output.shape							,		dtype=model_output.dtype							,		device=model_output.device							,		generator=_SCREAMING_SNAKE_CASE  )

								SCREAMING_SNAKE_CASE__  				=					noise * s_noise
								SCREAMING_SNAKE_CASE__  				=					sigma * (gamma + 1)

								if gamma > 0:
												SCREAMING_SNAKE_CASE__  				=					sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
												SCREAMING_SNAKE_CASE__  				=					model_output
								elif self.config.prediction_type == "epsilon":
												SCREAMING_SNAKE_CASE__  				=					sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												SCREAMING_SNAKE_CASE__  				=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
												raise ValueError(
												    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

								# 2. Convert to an ODE derivative
								SCREAMING_SNAKE_CASE__  				=					(sample - pred_original_sample) / sigma_hat

								SCREAMING_SNAKE_CASE__  				=					self.sigmas[step_index + 1] - sigma_hat

								SCREAMING_SNAKE_CASE__  				=					sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE							,		pred_original_sample=_SCREAMING_SNAKE_CASE  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					self.sigmas.to(device=original_samples.device							,		dtype=original_samples.dtype  )
								if original_samples.device.type == "mps" and torch.is_floating_point(_SCREAMING_SNAKE_CASE  ):
												# mps does not support float64
												SCREAMING_SNAKE_CASE__  				=					self.timesteps.to(original_samples.device							,		dtype=torch.floataa  )
												SCREAMING_SNAKE_CASE__  				=					timesteps.to(original_samples.device							,		dtype=torch.floataa  )
								else:
												SCREAMING_SNAKE_CASE__  				=					self.timesteps.to(original_samples.device  )
												SCREAMING_SNAKE_CASE__  				=					timesteps.to(original_samples.device  )

								SCREAMING_SNAKE_CASE__  				=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								SCREAMING_SNAKE_CASE__  				=					sigmas[step_indices].flatten()
								while len(sigma.shape  ) < len(original_samples.shape  ):
												SCREAMING_SNAKE_CASE__  				=					sigma.unsqueeze(-1  )

								SCREAMING_SNAKE_CASE__  				=					original_samples + noise * sigma
								return noisy_samples






				def __len__(    self  ):
								return self.config.num_train_timesteps







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()
_UpperCAmelCase										=					logging.get_logger(__name__)

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

_UpperCAmelCase										=					{
    '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,
}

_UpperCAmelCase										=					list('ARNDCQEGHILKMFPSTWYV')

_UpperCAmelCase										=					restypes + ['X']
_UpperCAmelCase										=					restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']




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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		List[str]:
       UpperCamelCase_ 							=	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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Tuple:
       if model.startswith("esmfold"	):
              UpperCamelCase_ 							=	MODEL_MAPPING[model]()
       else:
              UpperCamelCase_							,			UpperCamelCase_ 							=	MODEL_MAPPING[model]()
       esm.eval()  # disable dropout

       if model.startswith("esmfold"	):
              UpperCamelCase_ 							=	esm.esm.embed_dim
              UpperCamelCase_ 							=	esm.esm.num_layers
              UpperCamelCase_ 							=	esm.esm.attention_heads
              UpperCamelCase_ 							=	4 * embed_dim
              UpperCamelCase_ 							=	esm.esm.token_dropout
              UpperCamelCase_ 							=	False  # This code path does not exist in ESM-2
              UpperCamelCase_ 							=	"rotary"
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	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
              UpperCamelCase_ 							=	esm.args.embed_dim
              UpperCamelCase_ 							=	esm.args.layers
              UpperCamelCase_ 							=	esm.args.attention_heads
              UpperCamelCase_ 							=	esm.args.ffn_embed_dim
              UpperCamelCase_ 							=	esm.args.token_dropout
              UpperCamelCase_ 							=	True if esm.emb_layer_norm_before else False
              UpperCamelCase_ 							=	"absolute"
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	None
       else:
              # Indicates an ESM-2 model
              UpperCamelCase_ 							=	esm.embed_dim
              UpperCamelCase_ 							=	esm.num_layers
              UpperCamelCase_ 							=	esm.attention_heads
              UpperCamelCase_ 							=	4 * embed_dim  # This is hardcoded in ESM-2
              UpperCamelCase_ 							=	esm.token_dropout
              UpperCamelCase_ 							=	False  # This code path does not exist in ESM-2
              UpperCamelCase_ 							=	"rotary"
              UpperCamelCase_ 							=	False
              UpperCamelCase_ 							=	None

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

       if is_folding_model:
              UpperCamelCase_ 							=	esm.esm
       else:
              UpperCamelCase_ 							=	esm

       UpperCamelCase_ 							=	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=1026       ,		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:
              UpperCamelCase_ 							=	esm.classification_heads["mnli"].out_proj.weight.shape[0]
       print("Our ESM config:"       ,		UpperCamelCase_	)

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

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

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

       UpperCamelCase_ 							=	original_esm_model.emb_layer_norm_after.weight
       UpperCamelCase_ 							=	original_esm_model.emb_layer_norm_after.bias

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

              # self attention
              UpperCamelCase_ 							=	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)	)
              )

              UpperCamelCase_ 							=	esm_layer.self_attn.q_proj.weight
              UpperCamelCase_ 							=	esm_layer.self_attn.q_proj.bias
              UpperCamelCase_ 							=	esm_layer.self_attn.k_proj.weight
              UpperCamelCase_ 							=	esm_layer.self_attn.k_proj.bias
              UpperCamelCase_ 							=	esm_layer.self_attn.v_proj.weight
              UpperCamelCase_ 							=	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.
                     UpperCamelCase_ 							=	esm_layer.self_attn.rot_emb.inv_freq

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

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

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

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

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

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

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

              if is_folding_model:
                     UpperCamelCase_ 							=	torch.max(torch.abs(our_output["positions"] - their_output["positions"]	)	).item()
                     UpperCamelCase_ 							=	torch.allclose(our_output["positions"]       ,		their_output["positions"]       ,		atol=1e-5	)
              else:
                     UpperCamelCase_ 							=	torch.max(torch.abs(our_output - their_output	)	).item()
                     UpperCamelCase_ 							=	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
                     UpperCamelCase_ 							=	model.predict_contacts(hf_tokens["input_ids"]       ,		hf_tokens["attention_mask"]	)
                     UpperCamelCase_ 							=	esm.predict_contacts(hf_tokens["input_ids"]	)
                     UpperCamelCase_ 							=	torch.max(torch.abs(our_output - their_output	)	).item()
                     UpperCamelCase_ 							=	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__":
     _UpperCAmelCase										=					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.')
     _UpperCAmelCase										=					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
     )

