





"""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()
_snake_case					:		Optional[Any]							=			logging.get_logger(__name__)

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

_snake_case					:		Tuple							=			{
    '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,
}

_snake_case					:		Any							=			list('ARNDCQEGHILKMFPSTWYV')

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






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






def  A__    (      UpperCamelCase ,  UpperCamelCase	):
							A		=  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__    (      UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase ,  UpperCamelCase	):
							if model.startswith("esmfold"	):
														A		=  MODEL_MAPPING[model]()
							else:
														A,     A		=  MODEL_MAPPING[model]()
							esm.eval()  # disable dropout

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

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

							if is_folding_model:
														A		=  esm.esm
							else:
														A		=  esm

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

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

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

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

							A		=  original_esm_model.emb_layer_norm_after.weight
							A		=  original_esm_model.emb_layer_norm_after.bias

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

														# self attention
														A		=  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)	)
														)

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

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

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

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

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

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

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

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

														if is_folding_model:
																					A		=  torch.max(torch.abs(our_output["positions"] - their_output["positions"]	)	).item()
																					A		=  torch.allclose(our_output["positions"] ,  their_output["positions"] ,  atol=1E-5	)
														else:
																					A		=  torch.max(torch.abs(our_output - their_output	)	).item()
																					A		=  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
																					A		=  model.predict_contacts(hf_tokens["input_ids"] ,  hf_tokens["attention_mask"]	)
																					A		=  esm.predict_contacts(hf_tokens["input_ids"]	)
																					A		=  torch.max(torch.abs(our_output - their_output	)	).item()
																					A		=  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__":
		_snake_case					:		Union[str, 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.')
		_snake_case					:		int							=			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"""
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 SchedulerMixin


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






@dataclass
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							42







class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :Tuple					,	__UpperCamelCase      :int = 40					,	__UpperCamelCase      :float = 0.002					,	__UpperCamelCase      :float = 80.0					,	__UpperCamelCase      :float = 0.5					,	__UpperCamelCase      :float = 1.0					,	__UpperCamelCase      :float = 7.0					,	__UpperCamelCase      :bool = True					,	):
												# standard deviation of the initial noise distribution
												A		=  sigma_max

												A		=  np.linspace(0					,	1					,	__UpperCamelCase		)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												# setable values
												A		=  None
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  False
												A		=  False


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Dict=None		):
												if schedule_timesteps is None:
																			A		=  self.timesteps

												A		=  (schedule_timesteps == timestep).nonzero()
												return indices.item()


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]		):
												# Get sigma corresponding to timestep
												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)
												A		=  self.index_for_timestep(__UpperCamelCase		)
												A		=  self.sigmas[step_idx]

												A		=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												A		=  True
												return sample


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Union[float, np.ndarray]		):
												if not isinstance(__UpperCamelCase					,	np.ndarray		):
																			A		=  np.array(__UpperCamelCase					,	dtype=np.floataa		)

												A		=  10_00 * 0.25 * np.log(sigmas + 1e-44		)

												return timesteps


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Union[str, torch.device] = None					,	__UpperCamelCase      :Optional[List[int]] = None					,	):
												if num_inference_steps is None and timesteps is None:
																			raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."		)

												if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."		)

												# Follow DDPMScheduler custom timesteps logic
												if timesteps is not None:
																			for i in range(1					,	len(__UpperCamelCase		)		):
																										if timesteps[i] >= timesteps[i - 1]:
																																	raise ValueError("`timesteps` must be in descending order."		)

																			if timesteps[0] >= self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`timesteps` must start before `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps}."		)

																			A		=  np.array(__UpperCamelCase					,	dtype=np.intaa		)
																			A		=  True
												else:
																			if num_inference_steps > self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																										    f" maximal {self.config.num_train_timesteps} timesteps."		)

																			A		=  num_inference_steps

																			A		=  self.config.num_train_timesteps // self.num_inference_steps
																			A		=  (np.arange(0					,	__UpperCamelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
																			A		=  False

												# Map timesteps to Karras sigmas directly for multistep sampling
												# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
												A		=  self.config.num_train_timesteps
												A		=  timesteps[::-1].copy()
												A		=  ramp / (num_train_timesteps - 1)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												A		=  np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
												A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)

												if str(__UpperCamelCase		).startswith("mps"		):
																			# mps does not support float64
																			A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase					,	dtype=torch.floataa		)
												else:
																			A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.config.rho
												A		=  sigma_min ** (1 / rho)
												A		=  sigma_max ** (1 / rho)
												A		=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / (sigma**2 + sigma_data**2)
												A		=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Tuple		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
												A		=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = True					,	):

												if (
												    isinstance(__UpperCamelCase					,	__UpperCamelCase		)
												    or isinstance(__UpperCamelCase					,	torch.IntTensor		)
												    or isinstance(__UpperCamelCase					,	torch.LongTensor		)
												):
																			raise ValueError(
																			    (
																			        "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
																			        f" `{self.__class__}.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(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)

												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.index_for_timestep(__UpperCamelCase		)

												# sigma_next corresponds to next_t in original implementation
												A		=  self.sigmas[step_index]
												if step_index + 1 < self.config.num_train_timesteps:
																			A		=  self.sigmas[step_index + 1]
												else:
																			# Set sigma_next to sigma_min
																			A		=  self.sigmas[-1]

												# Get scalings for boundary conditions
												A,     A		=  self.get_scalings_for_boundary_condition(__UpperCamelCase		)

												# 1. Denoise model output using boundary conditions
												A		=  c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																			A		=  denoised.clamp(-1					,	1		)

												# 2. Sample z ~ N(0, s_noise^2 * I)
												# Noise is not used for onestep sampling.
												if len(self.timesteps		) > 1:
																			A		=  randn_tensor(
																			    model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__UpperCamelCase		)
												else:
																			A		=  torch.zeros_like(__UpperCamelCase		)
												A		=  noise * self.config.s_noise

												A		=  sigma_next.clamp(min=__UpperCamelCase					,	max=__UpperCamelCase		)

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												A		=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																			return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												A		=  self.sigmas.to(device=original_samples.device					,	dtype=original_samples.dtype		)
												if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCamelCase		):
																			# mps does not support float64
																			A		=  self.timesteps.to(original_samples.device					,	dtype=torch.floataa		)
																			A		=  timesteps.to(original_samples.device					,	dtype=torch.floataa		)
												else:
																			A		=  self.timesteps.to(original_samples.device		)
																			A		=  timesteps.to(original_samples.device		)

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

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

												A		=  original_samples + noise * sigma
												return noisy_samples


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


