




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

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

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


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

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

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

a_      						=   list("ARNDCQEGHILKMFPSTWYV")

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




def     __SCREAMING_SNAKE_CASE      (     )				->		Any:

    """simple docstring"""


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




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		Any:

    """simple docstring"""


    __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     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_    ,   lowercase_					)				->		Dict:

    """simple docstring"""


    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(lowercase_    ,   lowercase_					) and key != "trunk":
                setattr(lowercase_    ,   lowercase_    ,   lowercase_					)
        for key, val in esm.cfg.trunk.items():
            if hasattr(esmfold_config.trunk    ,   lowercase_					) and key != "structure_module":
                setattr(esmfold_config.trunk    ,   lowercase_    ,   lowercase_					)
        for key, val in esm.cfg.trunk.structure_module.items():
            if hasattr(esmfold_config.trunk.structure_module    ,   lowercase_					):
                setattr(esmfold_config.trunk.structure_module    ,   lowercase_    ,   lowercase_					)
    elif hasattr(lowercase_    ,   '''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=lowercase_    ,   hidden_size=lowercase_    ,   num_hidden_layers=lowercase_    ,   num_attention_heads=lowercase_    ,   intermediate_size=lowercase_    ,   max_position_embeddings=10_26    ,   layer_norm_eps=1E-5    ,   attention_probs_dropout_prob=0.0    ,   hidden_dropout_prob=0.0    ,   pad_token_id=lowercase_    ,   emb_layer_norm_before=lowercase_    ,   token_dropout=lowercase_    ,   position_embedding_type=lowercase_    ,   is_folding_model=lowercase_    ,   esmfold_config=lowercase_    ,   vocab_list=lowercase_    ,   )
    if classification_head:
        __UpperCamelCase     						=  esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
    print('''Our ESM config:'''    ,   lowercase_					)

    if model.startswith('''esmfold'''					):
        __UpperCamelCase     						=  EsmForProteinFolding
    elif classification_head:
        __UpperCamelCase     						=  EsmForSequenceClassification
    else:
        __UpperCamelCase     						=  EsmForMaskedLM
    __UpperCamelCase     						=  model_class(lowercase_					)
    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'''    ,   lowercase_					) 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=lowercase_    ,   add_special_tokens=lowercase_					)
        __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(lowercase_					)
        # Prepare tokenizer and make sure it matches
        with TemporaryDirectory() as tempdir:
            __UpperCamelCase     						=  '''\n'''.join(alphabet.all_toks					)
            __UpperCamelCase     						=  Path(lowercase_					) / '''vocab.txt'''
            vocab_file.write_text(lowercase_					)
            __UpperCamelCase     						=  EsmTokenizer(vocab_file=str(lowercase_					)					)

        __UpperCamelCase     						=  hf_tokenizer([row[1] for row in sample_data]    ,   return_tensors='''pt'''    ,   padding=lowercase_					)
        __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(**lowercase_    ,   output_hidden_states=lowercase_					)
            __UpperCamelCase     						=  our_output['''logits''']
            if classification_head:
                __UpperCamelCase     						=  esm.model.classification_heads['''mnli'''](esm.extract_features(lowercase_					)					)
            else:
                __UpperCamelCase     						=  esm(hf_tokens['''input_ids''']    ,   repr_layers=list(range(9_99					)					)					)
                __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(lowercase_    ,   lowercase_    ,   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(lowercase_    ,   lowercase_    ,   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(lowercase_					).mkdir(parents=lowercase_    ,   exist_ok=lowercase_					)
        print(F"Saving model to {pytorch_dump_folder_path}"					)
        model.save_pretrained(lowercase_					)

        del esm  # Free up some memory before continuing

    print(F"Saving tokenizer to {pytorch_dump_folder_path}"					)
    hf_tokenizer.save_pretrained(lowercase_					)

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


if __name__ == "__main__":
 a_      						=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_dump_folder_path", type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 parser.add_argument("--model", default=None, type=str, required=True, help="Name of model to convert.")
 parser.add_argument("--push_to_repo", type=str, help="Repo to upload to (including username!).")
 parser.add_argument("--auth_token", type=str, help="HuggingFace auth token.")
 a_      						=   parser.parse_args()
 convert_esm_checkpoint_to_pytorch(
     args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
 )












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


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

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

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







def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=0.999    ,   lowercase_="cosine"    ,   )				->		Dict:

    """simple docstring"""


    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowercase_					):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2					) ** 2

    elif alpha_transform_type == "exp":

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

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

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


class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE      ):

      """simple docstring"""

      lowerCAmelCase__	:       Tuple			=    [e.name for e in KarrasDiffusionSchedulers]
      lowerCAmelCase__	:       List[Any]			=    1







      @register_to_config
      def __init__(   self    :       Tuple       ,				snake_case    :       int = 1000       ,				snake_case    :       float = 0.00_01       ,				snake_case    :       float = 0.02       ,				snake_case    :       str = "linear"       ,				snake_case    :       Optional[Union[np.ndarray, List[float]]] = None       ,				snake_case    :       str = "epsilon"       ,				snake_case    :       str = "linspace"       ,				snake_case    :       int = 0       ,				):
          if trained_betas is not None:
              __UpperCamelCase     						=  torch.tensor(snake_case       ,				dtype=torch.floataa )
          elif beta_schedule == "linear":
              __UpperCamelCase     						=  torch.linspace(snake_case       ,				snake_case       ,				snake_case       ,				dtype=torch.floataa )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              __UpperCamelCase     						=  (
                  torch.linspace(beta_start**0.5       ,				beta_end**0.5       ,				snake_case       ,				dtype=torch.floataa ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              __UpperCamelCase     						=  betas_for_alpha_bar(snake_case )
          else:
              raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

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

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

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







      @property
      def 					snake_case		(   self    :       Tuple ):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

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







      def 					snake_case		(   self    :       int       ,				snake_case    :       torch.FloatTensor       ,				snake_case    :       Union[float, torch.FloatTensor] ):
          if isinstance(snake_case       ,				torch.Tensor ):
              __UpperCamelCase     						=  timestep.to(self.timesteps.device )
          __UpperCamelCase     						=  (self.timesteps == timestep).nonzero().item()
          __UpperCamelCase     						=  self.sigmas[step_index]
          __UpperCamelCase     						=  sample / ((sigma**2 + 1) ** 0.5)
          __UpperCamelCase     						=  True
          return sample







      def 					snake_case		(   self    :       Any       ,				snake_case    :       int       ,				snake_case    :       Union[str, torch.device] = None ):
          __UpperCamelCase     						=  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":
              __UpperCamelCase     						=  np.linspace(0       ,				self.config.num_train_timesteps - 1       ,				snake_case       ,				dtype=snake_case )[
                  ::-1
              ].copy()
          elif self.config.timestep_spacing == "leading":
              __UpperCamelCase     						=  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
              __UpperCamelCase     						=  (np.arange(0       ,				snake_case ) * step_ratio).round()[::-1].copy().astype(snake_case )
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              __UpperCamelCase     						=  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
              __UpperCamelCase     						=  (np.arange(self.config.num_train_timesteps       ,				0       ,				-step_ratio )).round().copy().astype(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'." )

          __UpperCamelCase     						=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
          __UpperCamelCase     						=  np.interp(snake_case       ,				np.arange(0       ,				len(snake_case ) )       ,				snake_case )
          __UpperCamelCase     						=  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
          __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(device=snake_case )
          if str(snake_case ).startswith('''mps''' ):
              # mps does not support float64
              __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(snake_case       ,				dtype=torch.floataa )
          else:
              __UpperCamelCase     						=  torch.from_numpy(snake_case ).to(device=snake_case )







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

          if (
              isinstance(snake_case       ,				snake_case )
              or isinstance(snake_case       ,				torch.IntTensor )
              or isinstance(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(snake_case       ,				torch.Tensor ):
              __UpperCamelCase     						=  timestep.to(self.timesteps.device )

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

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase     						=  sample - sigma * model_output
          elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              __UpperCamelCase     						=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
          elif self.config.prediction_type == "sample":
              raise NotImplementedError('''prediction_type not implemented yet: sample''' )
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

          __UpperCamelCase     						=  self.sigmas[step_index]
          __UpperCamelCase     						=  self.sigmas[step_index + 1]
          __UpperCamelCase     						=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
          __UpperCamelCase     						=  (sigma_to**2 - sigma_up**2) ** 0.5

          # 2. Convert to an ODE derivative
          __UpperCamelCase     						=  (sample - pred_original_sample) / sigma

          __UpperCamelCase     						=  sigma_down - sigma

          __UpperCamelCase     						=  sample + derivative * dt

          __UpperCamelCase     						=  model_output.device
          __UpperCamelCase     						=  randn_tensor(model_output.shape       ,				dtype=model_output.dtype       ,				device=snake_case       ,				generator=snake_case )

          __UpperCamelCase     						=  prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return EulerAncestralDiscreteSchedulerOutput(
              prev_sample=snake_case       ,				pred_original_sample=snake_case )







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

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

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

          __UpperCamelCase     						=  original_samples + noise * sigma
          return noisy_samples







      def __len__(   self    :       Optional[Any] ):
          return self.config.num_train_timesteps






