





'''simple docstring'''

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
from ....modeling_utils import ModuleUtilsMixin
from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings


lowerCAmelCase__      :   str      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   Optional[int]      	=			"MMBTConfig"


class      SCREAMING_SNAKE_CASE__      (   nn.Module		):







					"""simple docstring"""






					def __init__(   self       :      List[str]		,	UpperCAmelCase_       :      Union[str, Any]		,	UpperCAmelCase_       :      Union[str, Any]		,	UpperCAmelCase_       :      Dict		):







							"""simple docstring"""
							super().__init__()
							__UpperCAmelCase	:							Optional[int]     =     config
							__UpperCAmelCase	:							Union[str, Any]     =     encoder
							__UpperCAmelCase	:							Optional[Any]     =     nn.Linear(config.modal_hidden_size		,	config.hidden_size		)
							__UpperCAmelCase	:							str     =     embeddings.position_embeddings
							__UpperCAmelCase	:							List[str]     =     embeddings.token_type_embeddings
							__UpperCAmelCase	:							str     =     embeddings.word_embeddings
							__UpperCAmelCase	:							Tuple     =     embeddings.LayerNorm
							__UpperCAmelCase	:							str     =     nn.Dropout(p=config.hidden_dropout_prob		)






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      Optional[int]		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Tuple=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							List[str]     =     self.proj_embeddings(self.encoder(UpperCAmelCase_		)		)
							__UpperCAmelCase	:							List[str]     =     token_embeddings.size(1		)

							if start_token is not None:
									__UpperCAmelCase	:							Optional[Any]     =     self.word_embeddings(UpperCAmelCase_		)
									seq_length += 1
									__UpperCAmelCase	:							int     =     torch.cat([start_token_embeds.unsqueeze(1		), token_embeddings]		,	dim=1		)

							if end_token is not None:
									__UpperCAmelCase	:							List[str]     =     self.word_embeddings(UpperCAmelCase_		)
									seq_length += 1
									__UpperCAmelCase	:							List[Any]     =     torch.cat([token_embeddings, end_token_embeds.unsqueeze(1		)]		,	dim=1		)

							if position_ids is None:
									__UpperCAmelCase	:							Dict     =     torch.arange(UpperCAmelCase_		,	dtype=torch.long		,	device=input_modal.device		)
									__UpperCAmelCase	:							Optional[int]     =     position_ids.unsqueeze(0		).expand(input_modal.size(0		)		,	UpperCAmelCase_		)

							if token_type_ids is None:
									__UpperCAmelCase	:							int     =     torch.zeros(
									    (input_modal.size(0		), seq_length)		,	dtype=torch.long		,	device=input_modal.device		)

							__UpperCAmelCase	:							int     =     self.position_embeddings(UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     self.token_type_embeddings(UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     token_embeddings + position_embeddings + token_type_embeddings
							__UpperCAmelCase	:							Tuple     =     self.LayerNorm(UpperCAmelCase_		)
							__UpperCAmelCase	:							int     =     self.dropout(UpperCAmelCase_		)
							return embeddings






lowerCAmelCase__      :   List[str]      	=			R"\n    MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and\n    Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.\n    It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and\n    obtain state-of-the-art performance on various multimodal classification benchmark tasks.\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration.\n        transformer (`nn.Module`): A text transformer that is used by MMBT.\n            It should have embeddings, encoder, and pooler attributes.\n        encoder (`nn.Module`): Encoder for the second modality.\n            It should take in a batch of modal inputs and return k, n dimension embeddings.\n"

lowerCAmelCase__      :   Tuple      	=			R"\n    Args:\n        input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):\n            The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image\n            Encoder, the shape would be (batch_size, channels, height, width)\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's\n            appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See\n            [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification\n            tasks.\n        modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\n            Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.\n        attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:\n            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n            [What are attention masks?](../glossary#attention-mask)\n        token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:\n            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,\n            1]`:\n\n            - 0 corresponds to a *sentence A* token,\n            - 1 corresponds to a *sentence B* token.\n\n            [What are token type IDs?](../glossary#token-type-ids)\n        modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:\n            Segment token indices to indicate different portions of the non-text modality. The embeddings from these\n            tokens will be summed with the respective token embeddings for the non-text modality.\n        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,\n            config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):\n            Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.\n            Selected in the range `[0, config.max_position_embeddings - 1]`.\n\n            [What are position IDs?](../glossary#position-ids)\n        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):\n            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:\n\n            - 1 indicates the head is **not masked**,\n            - 0 indicates the head is **masked**.\n\n        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):\n            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This\n            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the\n            model's internal embedding lookup matrix.\n        encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):\n            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if\n            the model is configured as a decoder.\n        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):\n            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in\n            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:\n\n            - 1 for tokens that are **not masked**,\n            - 0 for tokens that are **masked**.\n\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned\n            tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"


@add_start_docstrings(
    '''The bare MMBT Model outputting raw hidden-states without any specific head on top.'''							,snake_case__							,)
class      SCREAMING_SNAKE_CASE__      (   nn.Module							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      str		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Optional[int]		):







							"""simple docstring"""
							super().__init__()
							__UpperCAmelCase	:							Optional[int]     =     config
							__UpperCAmelCase	:							str     =     transformer
							__UpperCAmelCase	:							List[str]     =     ModalEmbeddings(UpperCAmelCase_		,	UpperCAmelCase_		,	transformer.embeddings		)
					@add_start_docstrings_to_model_forward(UpperCAmelCase_		)
					@replace_return_docstrings(output_type=UpperCAmelCase_		,	config_class=_CONFIG_FOR_DOC		)
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      List[str]		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Any=None		,	UpperCAmelCase_       :      Dict=None		,	UpperCAmelCase_       :      int=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      Any=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      str=None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     output_attentions if output_attentions is not None else self.config.output_attentions
							__UpperCAmelCase	:							List[Any]     =     (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							__UpperCAmelCase	:							Union[str, Any]     =     return_dict if return_dict is not None else self.config.use_return_dict

							if input_ids is not None and inputs_embeds is not None:
									raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
							elif input_ids is not None:
									__UpperCAmelCase	:							Optional[int]     =     input_ids.size()
							elif inputs_embeds is not None:
									__UpperCAmelCase	:							Optional[Any]     =     inputs_embeds.size()[:-1]
							else:
									raise ValueError("You have to specify either input_ids or inputs_embeds"		)

							__UpperCAmelCase	:							Tuple     =     input_ids.device if input_ids is not None else inputs_embeds.device

							__UpperCAmelCase	:							str     =     self.modal_encoder(
							    UpperCAmelCase_		,	start_token=UpperCAmelCase_		,	end_token=UpperCAmelCase_		,	position_ids=UpperCAmelCase_		,	token_type_ids=UpperCAmelCase_		,	)

							__UpperCAmelCase	:							str     =     modal_embeddings.size()[:-1]

							if token_type_ids is None:
									__UpperCAmelCase	:							Union[str, Any]     =     torch.ones(UpperCAmelCase_		,	dtype=torch.long		,	device=UpperCAmelCase_		)

							__UpperCAmelCase	:							int     =     self.transformer.embeddings(
							    input_ids=UpperCAmelCase_		,	position_ids=UpperCAmelCase_		,	token_type_ids=UpperCAmelCase_		,	inputs_embeds=UpperCAmelCase_		)

							__UpperCAmelCase	:							Union[str, Any]     =     torch.cat([modal_embeddings, txt_embeddings]		,	1		)

							__UpperCAmelCase	:							int     =     embedding_output.size()[:-1]

							if attention_mask is None:
									__UpperCAmelCase	:							str     =     torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		)
							else:
									__UpperCAmelCase	:							List[Any]     =     torch.cat(
									    [torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=torch.long		), attention_mask]		,	dim=1		)
							if encoder_attention_mask is None:
									__UpperCAmelCase	:							int     =     torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		)
							else:
									__UpperCAmelCase	:							str     =     torch.cat(
									    [torch.ones(UpperCAmelCase_		,	device=UpperCAmelCase_		), encoder_attention_mask]		,	dim=1		)

							__UpperCAmelCase	:							str     =     self.get_extended_attention_mask(UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     self.invert_attention_mask(UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     self.get_head_mask(UpperCAmelCase_		,	self.config.num_hidden_layers		)

							__UpperCAmelCase	:							int     =     self.transformer.encoder(
							    UpperCAmelCase_		,	attention_mask=UpperCAmelCase_		,	head_mask=UpperCAmelCase_		,	encoder_hidden_states=UpperCAmelCase_		,	encoder_attention_mask=UpperCAmelCase_		,	output_attentions=UpperCAmelCase_		,	output_hidden_states=UpperCAmelCase_		,	return_dict=UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Any     =     encoder_outputs[0]
							__UpperCAmelCase	:							List[str]     =     self.transformer.pooler(UpperCAmelCase_		)

							if not return_dict:
									return (sequence_output, pooled_output) + encoder_outputs[1:]

							return BaseModelOutputWithPooling(
							    last_hidden_state=UpperCAmelCase_		,	pooler_output=UpperCAmelCase_		,	hidden_states=encoder_outputs.hidden_states		,	attentions=encoder_outputs.attentions		,	)
					def 		lowerCamelCase_   (   self       :      Optional[int]		):







							"""simple docstring"""
							return self.embeddings.word_embeddings






					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     value






@add_start_docstrings(
    '''
    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)
    '''							,snake_case__							,snake_case__							,)
class      SCREAMING_SNAKE_CASE__      (   nn.Module		):







					"""simple docstring"""






					def __init__(   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Tuple		,	UpperCAmelCase_       :      int		):







							"""simple docstring"""
							super().__init__()
							__UpperCAmelCase	:							Optional[Any]     =     config.num_labels

							__UpperCAmelCase	:							List[Any]     =     MMBTModel(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							Optional[Any]     =     nn.Dropout(config.hidden_dropout_prob		)
							__UpperCAmelCase	:							List[Any]     =     nn.Linear(config.hidden_size		,	config.num_labels		)






					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      Tuple=None		,	UpperCAmelCase_       :      List[str]=None		,	UpperCAmelCase_       :      List[Any]=None		,	UpperCAmelCase_       :      str=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	UpperCAmelCase_       :      int=None		,	UpperCAmelCase_       :      Optional[Any]=None		,	UpperCAmelCase_       :      Optional[int]=None		,	UpperCAmelCase_       :      Union[str, Any]=None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     return_dict if return_dict is not None else self.config.use_return_dict

							__UpperCAmelCase	:							int     =     self.mmbt(
							    input_modal=UpperCAmelCase_		,	input_ids=UpperCAmelCase_		,	modal_start_tokens=UpperCAmelCase_		,	modal_end_tokens=UpperCAmelCase_		,	attention_mask=UpperCAmelCase_		,	token_type_ids=UpperCAmelCase_		,	modal_token_type_ids=UpperCAmelCase_		,	position_ids=UpperCAmelCase_		,	modal_position_ids=UpperCAmelCase_		,	head_mask=UpperCAmelCase_		,	inputs_embeds=UpperCAmelCase_		,	return_dict=UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Optional[int]     =     outputs[1]

							__UpperCAmelCase	:							Dict     =     self.dropout(UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     self.classifier(UpperCAmelCase_		)

							__UpperCAmelCase	:							Tuple     =     None
							if labels is not None:
									if self.num_labels == 1:
											#  We are doing regression
											__UpperCAmelCase	:							Optional[Any]     =     MSELoss()
											__UpperCAmelCase	:							Tuple     =     loss_fct(logits.view(-1		)		,	labels.view(-1		)		)
									else:
											__UpperCAmelCase	:							int     =     CrossEntropyLoss()
											__UpperCAmelCase	:							List[str]     =     loss_fct(logits.view(-1		,	self.num_labels		)		,	labels.view(-1		)		)

							if not return_dict:
									__UpperCAmelCase	:							Union[str, Any]     =     (logits,) + outputs[2:]
									return ((loss,) + output) if loss is not None else output

							return SequenceClassifierOutput(
							    loss=UpperCAmelCase_		,	logits=UpperCAmelCase_		,	hidden_states=outputs.hidden_states		,	attentions=outputs.attentions		,	)











'''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      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	42
					SCREAMING_SNAKE_CASE							=	42






def 						__UpperCamelCase						(			_UpperCAmelCase,       _UpperCAmelCase=0.999,       _UpperCAmelCase="cosine",       ):
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(_UpperCAmelCase  ):
						return math.cos((t + 0.008) / 1.008 * 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}"  )

		__UpperCAmelCase	:							Union[str, Any]     =     []
		for i in range(_UpperCAmelCase  ):
				__UpperCAmelCase	:							int     =     i / num_diffusion_timesteps
				__UpperCAmelCase	:							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      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	1
					@register_to_config
					def __init__(   self       :      Dict		,	UpperCAmelCase_       :      int = 1_000		,	UpperCAmelCase_       :      float = 0.0001		,	UpperCAmelCase_       :      float = 0.02		,	UpperCAmelCase_       :      str = "linear"		,	UpperCAmelCase_       :      float = 0.0		,	UpperCAmelCase_       :      Optional[np.ndarray] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							if trained_betas is not None:
									__UpperCAmelCase	:							Tuple     =     torch.from_numpy(UpperCAmelCase_		)
							elif beta_schedule == "linear":
									__UpperCAmelCase	:							Dict     =     torch.linspace(UpperCAmelCase_		,	UpperCAmelCase_		,	UpperCAmelCase_		,	dtype=torch.floataa		)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									__UpperCAmelCase	:							int     =     (
									    torch.linspace(beta_start**0.5		,	beta_end**0.5		,	UpperCAmelCase_		,	dtype=torch.floataa		) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									__UpperCAmelCase	:							str     =     betas_for_alpha_bar(UpperCAmelCase_		)
							elif beta_schedule == "sigmoid":
									# GeoDiff sigmoid schedule
									__UpperCAmelCase	:							Optional[Any]     =     torch.linspace(-6		,	6		,	UpperCAmelCase_		)
									__UpperCAmelCase	:							Optional[int]     =     torch.sigmoid(UpperCAmelCase_		) * (beta_end - beta_start) + beta_start
							else:
									raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

							__UpperCAmelCase	:							List[str]     =     1.0 - self.betas
							__UpperCAmelCase	:							List[str]     =     torch.cumprod(self.alphas		,	dim=0		)
							__UpperCAmelCase	:							List[Any]     =     torch.tensor(1.0		)

							__UpperCAmelCase	:							str     =     torch.tensor(1.0		)

							# standard deviation of the initial noise distribution
							__UpperCAmelCase	:							Optional[Any]     =     1.0

							# setable values
							__UpperCAmelCase	:							Optional[Any]     =     None
							__UpperCAmelCase	:							Optional[Any]     =     torch.from_numpy(np.arange(0		,	UpperCAmelCase_		)[::-1].copy()		)

							__UpperCAmelCase	:							Tuple     =     eta
					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[int] = None		):







							"""simple docstring"""
							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      int = 10		,	UpperCAmelCase_       :      Union[str, torch.device] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Union[str, Any]     =     min(self.config.num_train_timesteps		,	UpperCAmelCase_		)
							__UpperCAmelCase	:							List[Any]     =     num_inference_steps

							__UpperCAmelCase	:							Dict     =     []

							__UpperCAmelCase	:							int     =     {}
							for j in range(0		,	num_inference_steps - jump_length		,	UpperCAmelCase_		):
									__UpperCAmelCase	:							int     =     jump_n_sample - 1

							__UpperCAmelCase	:							Optional[int]     =     num_inference_steps
							while t >= 1:
									__UpperCAmelCase	:							int     =     t - 1
									timesteps.append(UpperCAmelCase_		)

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

							__UpperCAmelCase	:							Optional[Any]     =     np.array(UpperCAmelCase_		) * (self.config.num_train_timesteps // self.num_inference_steps)
							__UpperCAmelCase	:							str     =     torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      Optional[Any]		,	UpperCAmelCase_       :      Tuple		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     t - self.config.num_train_timesteps // self.num_inference_steps

							__UpperCAmelCase	:							str     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							int     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							Dict     =     1 - alpha_prod_t
							__UpperCAmelCase	:							Optional[Any]     =     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]
							__UpperCAmelCase	:							Dict     =     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance
					def 		lowerCamelCase_   (   self       :      Any		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      int		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      Optional[torch.Generator] = None		,	UpperCAmelCase_       :      bool = True		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							Any     =     timestep
							__UpperCAmelCase	:							Optional[Any]     =     timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							__UpperCAmelCase	:							Any     =     self.alphas_cumprod[t]
							__UpperCAmelCase	:							Optional[int]     =     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							__UpperCAmelCase	:							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
							__UpperCAmelCase	:							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:
									__UpperCAmelCase	:							int     =     torch.clamp(UpperCAmelCase_		,	-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
							__UpperCAmelCase	:							Optional[Any]     =     model_output.device
							__UpperCAmelCase	:							Optional[int]     =     randn_tensor(model_output.shape		,	generator=UpperCAmelCase_		,	device=UpperCAmelCase_		,	dtype=model_output.dtype		)
							__UpperCAmelCase	:							List[Any]     =     self.eta * self._get_variance(UpperCAmelCase_		) ** 0.5

							__UpperCAmelCase	:							str     =     0
							if t > 0 and self.eta > 0:
									__UpperCAmelCase	:							Union[str, Any]     =     std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							__UpperCAmelCase	:							int     =     (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
							__UpperCAmelCase	:							Any     =     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
							__UpperCAmelCase	:							str     =     (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
							__UpperCAmelCase	:							Union[str, Any]     =     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=UpperCAmelCase_		,	pred_original_sample=UpperCAmelCase_		)
					def 		lowerCamelCase_   (   self       :      int		,	UpperCAmelCase_       :      List[Any]		,	UpperCAmelCase_       :      Any		,	UpperCAmelCase_       :      List[str]=None		):







							"""simple docstring"""
							__UpperCAmelCase	:							Dict     =     self.config.num_train_timesteps // self.num_inference_steps

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

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

							return sample
					def 		lowerCamelCase_   (   self       :      Tuple		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.FloatTensor		,	UpperCAmelCase_       :      torch.IntTensor		,	):







							"""simple docstring"""
							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)






					def __len__(   self       :      List[str]		):







							"""simple docstring"""
							return self.config.num_train_timesteps




