






"""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()
lowercase__      =				logging.get_logger(__name__)

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

lowercase__      =				{
    """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,
}

lowercase__      =				list("""ARNDCQEGHILKMFPSTWYV""")

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

def 	__lowerCamelCase			(						)		->					int:

		"""simple docstring"""







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

def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase )		->					List[str]:

		"""simple docstring"""







		lowerCAmelCase_     :							Optional[Any]             =    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 )		->					Optional[Any]:

		"""simple docstring"""







		if model.startswith("esmfold" ):
				lowerCAmelCase_     :							Any             =    MODEL_MAPPING[model]()
		else:
				lowerCAmelCase_     :							Dict             =    MODEL_MAPPING[model]()
		esm.eval()  # disable dropout

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

		if is_folding_model:
				lowerCAmelCase_     :							int             =    esm.esm.alphabet
		lowerCAmelCase_     :							Tuple             =    tuple(alphabet.all_toks )
		lowerCAmelCase_     :							str             =    alphabet.mask_idx
		lowerCAmelCase_     :							str             =    alphabet.padding_idx

		if is_folding_model:
				lowerCAmelCase_     :							Dict             =    esm.esm
		else:
				lowerCAmelCase_     :							int             =    esm

		lowerCAmelCase_     :							Union[str, Any]             =    EsmConfig(
		    vocab_size=original_esm_model.embed_tokens.num_embeddings   , mask_token_id=__UpperCamelCase   , hidden_size=__UpperCamelCase   , num_hidden_layers=__UpperCamelCase   , num_attention_heads=__UpperCamelCase   , intermediate_size=__UpperCamelCase   , max_position_embeddings=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:
				lowerCAmelCase_     :							int             =    esm.classification_heads["mnli"].out_proj.weight.shape[0]
		print("Our ESM config:"   , __UpperCamelCase )

		if model.startswith("esmfold" ):
				lowerCAmelCase_     :							Optional[int]             =    EsmForProteinFolding
		elif classification_head:
				lowerCAmelCase_     :							Union[str, Any]             =    EsmForSequenceClassification
		else:
				lowerCAmelCase_     :							Tuple             =    EsmForMaskedLM
		lowerCAmelCase_     :							int             =    model_class(__UpperCamelCase )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		lowerCAmelCase_     :							Optional[Any]             =    original_esm_model.embed_tokens.weight
		if position_embedding_type == "absolute":
				lowerCAmelCase_     :							str             =    original_esm_model.embed_positions.weight

		if config.emb_layer_norm_before:
				lowerCAmelCase_     :							List[Any]             =    original_esm_model.emb_layer_norm_before.weight
				lowerCAmelCase_     :							Dict             =    original_esm_model.emb_layer_norm_before.bias

		lowerCAmelCase_     :							Union[str, Any]             =    original_esm_model.emb_layer_norm_after.weight
		lowerCAmelCase_     :							Union[str, Any]             =    original_esm_model.emb_layer_norm_after.bias

		for i in range(config.num_hidden_layers ):
				# Encoder: start of layer
				lowerCAmelCase_     :							EsmLayer             =    model.esm.encoder.layer[i]
				# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
				lowerCAmelCase_     :							Union[str, Any]             =    original_esm_model.layers[i]

				# self attention
				lowerCAmelCase_     :							EsmSelfAttention             =    layer.attention.self
				assert (
				    esm_layer.self_attn.k_proj.weight.data.shape
				    == esm_layer.self_attn.q_proj.weight.data.shape
				    == esm_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size) )
				)

				lowerCAmelCase_     :							Any             =    esm_layer.self_attn.q_proj.weight
				lowerCAmelCase_     :							Optional[Any]             =    esm_layer.self_attn.q_proj.bias
				lowerCAmelCase_     :							Any             =    esm_layer.self_attn.k_proj.weight
				lowerCAmelCase_     :							Optional[int]             =    esm_layer.self_attn.k_proj.bias
				lowerCAmelCase_     :							Dict             =    esm_layer.self_attn.v_proj.weight
				lowerCAmelCase_     :							Optional[int]             =    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.
						lowerCAmelCase_     :							Optional[Any]             =    esm_layer.self_attn.rot_emb.inv_freq

				# LayerNorm changes for pre-activation
				lowerCAmelCase_     :							List[Any]             =    esm_layer.self_attn_layer_norm.weight
				lowerCAmelCase_     :							str             =    esm_layer.self_attn_layer_norm.bias
				lowerCAmelCase_     :							int             =    esm_layer.final_layer_norm.weight
				lowerCAmelCase_     :							Optional[int]             =    esm_layer.final_layer_norm.bias

				# self-attention output
				lowerCAmelCase_     :							EsmSelfOutput             =    layer.attention.output
				assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
				lowerCAmelCase_     :							Any             =    esm_layer.self_attn.out_proj.weight
				lowerCAmelCase_     :							Dict             =    esm_layer.self_attn.out_proj.bias

				# intermediate
				lowerCAmelCase_     :							EsmIntermediate             =    layer.intermediate
				assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
				lowerCAmelCase_     :							List[Any]             =    esm_layer.fca.weight
				lowerCAmelCase_     :							str             =    esm_layer.fca.bias

				# output
				lowerCAmelCase_     :							EsmOutput             =    layer.output
				assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
				lowerCAmelCase_     :							List[Any]             =    esm_layer.fca.weight
				lowerCAmelCase_     :							Any             =    esm_layer.fca.bias
				# end of layer

		if is_folding_model:
				lowerCAmelCase_     :							Union[str, Any]             =    esm.esm_s_combine.data
				lowerCAmelCase_     :							str             =    esm.afa_to_esm.data
				transfer_and_check_weights(esm.embedding   , model.embedding )
				transfer_and_check_weights(esm.esm_s_mlp   , model.esm_s_mlp )
				transfer_and_check_weights(esm.trunk   , model.trunk )
				transfer_and_check_weights(esm.distogram_head   , model.distogram_head )
				transfer_and_check_weights(esm.ptm_head   , model.ptm_head )
				transfer_and_check_weights(esm.lm_head   , model.lm_head )
				transfer_and_check_weights(esm.lddt_head   , model.lddt_head )

		elif classification_head:
				lowerCAmelCase_     :							str             =    esm.esm.classification_heads["mnli"].dense.weight
				lowerCAmelCase_     :							List[str]             =    esm.classification_heads["mnli"].dense.bias
				lowerCAmelCase_     :							Dict             =    esm.classification_heads["mnli"].out_proj.weight
				lowerCAmelCase_     :							List[str]             =    esm.classification_heads["mnli"].out_proj.bias
		else:
				# LM Head
				lowerCAmelCase_     :							List[str]             =    esm.lm_head.dense.weight
				lowerCAmelCase_     :							Optional[int]             =    esm.lm_head.dense.bias
				lowerCAmelCase_     :							int             =    esm.lm_head.layer_norm.weight
				lowerCAmelCase_     :							Union[str, Any]             =    esm.lm_head.layer_norm.bias
				lowerCAmelCase_     :							str             =    esm.lm_head.weight
				lowerCAmelCase_     :							List[Any]             =    esm.lm_head.bias

		# Contact prediction head
		transfer_and_check_weights(esm.contact_head   , model.esm.contact_head )

		# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
		if is_folding_model:
				# Folding models aren't trained on masked inputs and don't like mask tokens.
				lowerCAmelCase_     :							List[Any]             =    SAMPLE_DATA[:2]
		else:
				lowerCAmelCase_     :							Tuple             =    SAMPLE_DATA

		if is_folding_model:
				lowerCAmelCase_     :							List[Any]             =    get_esmfold_tokenizer()
				lowerCAmelCase_     :							int             =    hf_tokenizer(
				    [row[1] for row in sample_data]   , return_tensors="pt"   , padding=__UpperCamelCase   , add_special_tokens=__UpperCamelCase )
				lowerCAmelCase_     :							List[Any]             =    esmfold_encode_sequences([row[1] for row in sample_data] )
				lowerCAmelCase_     :							Dict             =    torch.all(hf_tokens["input_ids"] == esmfold_aas ) and torch.all(
				    hf_tokens["attention_mask"] == esmfold_mask )
		else:
				# Let's check that we get the same results.
				lowerCAmelCase_     :							Union[str, Any]             =    alphabet.get_batch_converter()
				lowerCAmelCase_     :							Any             =    batch_converter(__UpperCamelCase )
				# Prepare tokenizer and make sure it matches
				with TemporaryDirectory() as tempdir:
						lowerCAmelCase_     :							str             =    "\n".join(alphabet.all_toks )
						lowerCAmelCase_     :							Optional[int]             =    Path(__UpperCamelCase ) / "vocab.txt"
						vocab_file.write_text(__UpperCamelCase )
						lowerCAmelCase_     :							int             =    EsmTokenizer(vocab_file=str(__UpperCamelCase ) )

				lowerCAmelCase_     :							Union[str, Any]             =    hf_tokenizer([row[1] for row in sample_data]   , return_tensors="pt"   , padding=__UpperCamelCase )
				lowerCAmelCase_     :							List[str]             =    torch.all(hf_tokens["input_ids"] == batch_tokens )

		print("Do both models tokenizers output the same tokens?"   , "🔥" if success else "💩" )
		if not success:
				raise Exception("Tokenization does not match!" )

		with torch.no_grad():
				if is_folding_model:
						# Let's test the model in parts
						# ESMFold always converts the ESM stem to float16, which requires float16 ops
						# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
						# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
						# original and the converted model on the GPU at the same time.
						lowerCAmelCase_     :							Optional[int]             =    esm.cuda().infer([row[1] for row in sample_data] )
						lowerCAmelCase_     :							Union[str, Any]             =    model.cuda()(
						    input_ids=hf_tokens["input_ids"].cuda()   , attention_mask=hf_tokens["attention_mask"].cuda() )
				else:
						lowerCAmelCase_     :							List[str]             =    model(**__UpperCamelCase   , output_hidden_states=__UpperCamelCase )
						lowerCAmelCase_     :							List[Any]             =    our_output["logits"]
						if classification_head:
								lowerCAmelCase_     :							Union[str, Any]             =    esm.model.classification_heads["mnli"](esm.extract_features(__UpperCamelCase ) )
						else:
								lowerCAmelCase_     :							Dict             =    esm(hf_tokens["input_ids"]   , repr_layers=list(range(999 ) ) )
								lowerCAmelCase_     :							Optional[int]             =    their_output["logits"]

				if is_folding_model:
						lowerCAmelCase_     :							Union[str, Any]             =    torch.max(torch.abs(our_output["positions"] - their_output["positions"] ) ).item()
						lowerCAmelCase_     :							Union[str, Any]             =    torch.allclose(our_output["positions"]   , their_output["positions"]   , atol=1e-5 )
				else:
						lowerCAmelCase_     :							Union[str, Any]             =    torch.max(torch.abs(our_output - their_output ) ).item()
						lowerCAmelCase_     :							Union[str, Any]             =    torch.allclose(__UpperCamelCase   , __UpperCamelCase   , atol=1e-5 )

				print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-5
				print("Do both models output the same tensors?"   , "🔥" if success else "💩" )

				if not success:
						raise Exception("Something went wRoNg" )

				if not is_folding_model:
						# Let's check contact prediction too
						lowerCAmelCase_     :							Optional[int]             =    model.predict_contacts(hf_tokens["input_ids"]   , hf_tokens["attention_mask"] )
						lowerCAmelCase_     :							Optional[int]             =    esm.predict_contacts(hf_tokens["input_ids"] )
						lowerCAmelCase_     :							str             =    torch.max(torch.abs(our_output - their_output ) ).item()
						lowerCAmelCase_     :							List[Any]             =    torch.allclose(__UpperCamelCase   , __UpperCamelCase   , atol=1e-5 )

						print("Contact prediction testing:" )
						print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-5
						print("Do both models output the same tensors?"   , "🔥" if success else "💩" )

						if not success:
								raise Exception("Something went wRoNg" )

				pathlib.Path(__UpperCamelCase ).mkdir(parents=__UpperCamelCase   , exist_ok=__UpperCamelCase )
				print(f'''Saving model to {pytorch_dump_folder_path}''' )
				model.save_pretrained(__UpperCamelCase )

				del esm  # Free up some memory before continuing

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

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


if __name__ == "__main__":
					lowercase__      =				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.""")
					lowercase__      =				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"""

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

import numpy as np
import torch

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


@dataclass
class 	__lowerCamelCase       (				A__    ):





		'''simple docstring'''






		a_			:       torch.FloatTensor
		a_			:       torch.FloatTensor







def 	__lowerCamelCase			(						__UpperCamelCase   , __UpperCamelCase=0.9_99   , __UpperCamelCase="cosine"   , )		->					Any:

		"""simple docstring"""







		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__UpperCamelCase ):
						return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

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

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

		lowerCAmelCase_     :							List[str]             =    []
		for i in range(__UpperCamelCase ):
				lowerCAmelCase_     :							int             =    i / num_diffusion_timesteps
				lowerCAmelCase_     :							Optional[Any]             =    (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       (				A__				,							A__    ):





		'''simple docstring'''






		a_			:       Tuple      =							1





		@register_to_config
		def __init__(     self							:			Optional[int]		,    a_							:			int = 10_00		,    a_							:			float = 0.0001		,    a_							:			float = 0.02		,    a_							:			str = "linear"		,    a_							:			float = 0.0		,    a_							:			Optional[np.ndarray] = None		,    a_							:			bool = True		,    ):
				if trained_betas is not None:
						lowerCAmelCase_     :							Tuple             =    torch.from_numpy(a_			)
				elif beta_schedule == "linear":
						lowerCAmelCase_     :							int             =    torch.linspace(a_		,    a_		,    a_		,    dtype=torch.floataa			)
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						lowerCAmelCase_     :							Optional[Any]             =    (
						    torch.linspace(beta_start**0.5		,    beta_end**0.5		,    a_		,    dtype=torch.floataa			) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						lowerCAmelCase_     :							Tuple             =    betas_for_alpha_bar(a_			)
				elif beta_schedule == "sigmoid":
						# GeoDiff sigmoid schedule
						lowerCAmelCase_     :							List[str]             =    torch.linspace(-6		,    6		,    a_			)
						lowerCAmelCase_     :							int             =    torch.sigmoid(a_			) * (beta_end - beta_start) + beta_start
				else:
						raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''			)

				lowerCAmelCase_     :							Union[str, Any]             =    1.0 - self.betas
				lowerCAmelCase_     :							Optional[int]             =    torch.cumprod(self.alphas		,    dim=0			)
				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)

				lowerCAmelCase_     :							int             =    torch.tensor(1.0			)

				# standard deviation of the initial noise distribution
				lowerCAmelCase_     :							List[str]             =    1.0

				# setable values
				lowerCAmelCase_     :							Dict             =    None
				lowerCAmelCase_     :							List[str]             =    torch.from_numpy(np.arange(0		,    a_			)[::-1].copy()			)

				lowerCAmelCase_     :							int             =    eta





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			Optional[int] = None			):
				return sample





		def      lowerCamelCase (     self							:			Dict		,    a_							:			int		,    a_							:			int = 10		,    a_							:			int = 10		,    a_							:			Union[str, torch.device] = None		,    ):
				lowerCAmelCase_     :							Union[str, Any]             =    min(self.config.num_train_timesteps		,    a_			)
				lowerCAmelCase_     :							Dict             =    num_inference_steps

				lowerCAmelCase_     :							List[str]             =    []

				lowerCAmelCase_     :							Dict             =    {}
				for j in range(0		,    num_inference_steps - jump_length		,    a_			):
						lowerCAmelCase_     :							Dict             =    jump_n_sample - 1

				lowerCAmelCase_     :							List[Any]             =    num_inference_steps
				while t >= 1:
						lowerCAmelCase_     :							int             =    t - 1
						timesteps.append(a_			)

						if jumps.get(a_		,    0			) > 0:
								lowerCAmelCase_     :							Optional[int]             =    jumps[t] - 1
								for _ in range(a_			):
										lowerCAmelCase_     :							Any             =    t + 1
										timesteps.append(a_			)

				lowerCAmelCase_     :							List[Any]             =    np.array(a_			) * (self.config.num_train_timesteps // self.num_inference_steps)
				lowerCAmelCase_     :							int             =    torch.from_numpy(a_			).to(a_			)





		def      lowerCamelCase (     self							:			Optional[int]		,    a_							:			Optional[Any]			):
				lowerCAmelCase_     :							Optional[Any]             =    t - self.config.num_train_timesteps // self.num_inference_steps

				lowerCAmelCase_     :							List[Any]             =    self.alphas_cumprod[t]
				lowerCAmelCase_     :							Dict             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCAmelCase_     :							List[str]             =    1 - alpha_prod_t
				lowerCAmelCase_     :							str             =    1 - alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
				# variance to pred_sample
				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
				# without eta.
				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
				lowerCAmelCase_     :							Dict             =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance





		def      lowerCamelCase (     self							:			Optional[Any]		,    a_							:			torch.FloatTensor		,    a_							:			int		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			Optional[torch.Generator] = None		,    a_							:			bool = True		,    ):
				lowerCAmelCase_     :							Any             =    timestep
				lowerCAmelCase_     :							str             =    timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				lowerCAmelCase_     :							Any             =    self.alphas_cumprod[t]
				lowerCAmelCase_     :							List[str]             =    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCAmelCase_     :							Optional[Any]             =    1 - alpha_prod_t

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				lowerCAmelCase_     :							List[Any]             =    (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
						lowerCAmelCase_     :							Optional[int]             =    torch.clamp(a_		,    -1		,    1			)

				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
				# substitute formula (7) in the algorithm coming from DDPM paper
				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
				# DDIM schedule gives the same results as DDPM with eta = 1.0
				# Noise is being reused in 7. and 8., but no impact on quality has
				# been observed.

				# 5. Add noise
				lowerCAmelCase_     :							Optional[Any]             =    model_output.device
				lowerCAmelCase_     :							List[str]             =    randn_tensor(model_output.shape		,    generator=a_		,    device=a_		,    dtype=model_output.dtype			)
				lowerCAmelCase_     :							List[Any]             =    self.eta * self._get_variance(a_			) ** 0.5

				lowerCAmelCase_     :							Optional[int]             =    0
				if t > 0 and self.eta > 0:
						lowerCAmelCase_     :							Optional[int]             =    std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				lowerCAmelCase_     :							Union[str, Any]             =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				lowerCAmelCase_     :							Dict             =    alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
				lowerCAmelCase_     :							Union[str, Any]             =    (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
				lowerCAmelCase_     :							Tuple             =    mask * prev_known_part + (1.0 - mask) * prev_unknown_part

				if not return_dict:
						return (
						    pred_prev_sample,
						    pred_original_sample,
						)

				return RePaintSchedulerOutput(prev_sample=a_		,    pred_original_sample=a_			)





		def      lowerCamelCase (     self							:			Dict		,    a_							:			Any		,    a_							:			Dict		,    a_							:			Optional[int]=None			):
				lowerCAmelCase_     :							Dict             =    self.config.num_train_timesteps // self.num_inference_steps

				for i in range(a_			):
						lowerCAmelCase_     :							Optional[Any]             =    self.betas[timestep + i]
						if sample.device.type == "mps":
								# randn does not work reproducibly on mps
								lowerCAmelCase_     :							str             =    randn_tensor(sample.shape		,    dtype=sample.dtype		,    generator=a_			)
								lowerCAmelCase_     :							List[str]             =    noise.to(sample.device			)
						else:
								lowerCAmelCase_     :							Tuple             =    randn_tensor(sample.shape		,    generator=a_		,    device=sample.device		,    dtype=sample.dtype			)

						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
						lowerCAmelCase_     :							int             =    (1 - beta) ** 0.5 * sample + beta**0.5 * noise

				return sample





		def      lowerCamelCase (     self							:			Any		,    a_							:			torch.FloatTensor		,    a_							:			torch.FloatTensor		,    a_							:			torch.IntTensor		,    ):
				raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)





		def __len__(     self							:			Union[str, Any]			):
				return self.config.num_train_timesteps
