


from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin




@dataclass
class 			lowercase_				(a_      ):
				snake_case  						=42







class 			lowercase_				(a_			,				a_      ):





				@register_to_config
				def __init__(							self					,     lowercase_ = 16					,     lowercase_ = 88					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = 1					,     lowercase_ = 0.0					,     lowercase_ = 32					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = "geglu"					,     lowercase_ = None					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = False					,     lowercase_ = "layer_norm"					,     lowercase_ = True					,     )     ->   int:
											super().__init__()
											a__     						=use_linear_projection
											a__     						=num_attention_heads
											a__     						=attention_head_dim
											a__     						=num_attention_heads * attention_head_dim

											# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
											# Define whether input is continuous or discrete depending on configuration
											a__     						=(in_channels is not None) and (patch_size is None)
											a__     						=num_vector_embeds is not None
											a__     						=in_channels is not None and patch_size is not None

											if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
																		a__     						=(
																		    F"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
																		    ''' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'''
																		    ''' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'''
																		    ''' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'''
																		    ''' would be very nice if you could open a Pull request for the `transformer/config.json` file'''
																		)
																		deprecate('norm_type!=num_embeds_ada_norm'					,     '1.0.0'					,     _snake_case					,     standard_warn=_snake_case)
																		a__     						='''ada_norm'''

											if self.is_input_continuous and self.is_input_vectorized:
																		raise ValueError(
																		    F"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
																		    ' sure that either `in_channels` or `num_vector_embeds` is None.')
											elif self.is_input_vectorized and self.is_input_patches:
																		raise ValueError(
																		    F"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
																		    ' sure that either `num_vector_embeds` or `num_patches` is None.')
											elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
																		raise ValueError(
																		    F"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
																		    F""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.""")

											# 2. Define input layers
											if self.is_input_continuous:
																		a__     						=in_channels

																		a__     						=torch.nn.GroupNorm(num_groups=_snake_case					,     num_channels=_snake_case					,     eps=1e-6					,     affine=_snake_case)
																		if use_linear_projection:
																									a__     						=nn.Linear(_snake_case					,     _snake_case)
																		else:
																									a__     						=nn.Convad(_snake_case					,     _snake_case					,     kernel_size=1					,     stride=1					,     padding=0)
											elif self.is_input_vectorized:
																		assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
																		assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

																		a__     						=sample_size
																		a__     						=sample_size
																		a__     						=num_vector_embeds
																		a__     						=self.height * self.width

																		a__     						=ImagePositionalEmbeddings(
																		    num_embed=_snake_case					,     embed_dim=_snake_case					,     height=self.height					,     width=self.width)
											elif self.is_input_patches:
																		assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

																		a__     						=sample_size
																		a__     						=sample_size

																		a__     						=patch_size
																		a__     						=PatchEmbed(
																		    height=_snake_case					,     width=_snake_case					,     patch_size=_snake_case					,     in_channels=_snake_case					,     embed_dim=_snake_case					,     )

											# 3. Define transformers blocks
											a__     						=nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            _snake_case					,     _snake_case					,     _snake_case					,     dropout=_snake_case					,     cross_attention_dim=_snake_case					,     activation_fn=_snake_case					,     num_embeds_ada_norm=_snake_case					,     attention_bias=_snake_case					,     only_cross_attention=_snake_case					,     upcast_attention=_snake_case					,     norm_type=_snake_case					,     norm_elementwise_affine=_snake_case					,     )
											        for d in range(_snake_case)
											    ])

											# 4. Define output layers
											a__     						=in_channels if out_channels is None else out_channels
											if self.is_input_continuous:
																		# TODO: should use out_channels for continuous projections
																		if use_linear_projection:
																									a__     						=nn.Linear(_snake_case					,     _snake_case)
																		else:
																									a__     						=nn.Convad(_snake_case					,     _snake_case					,     kernel_size=1					,     stride=1					,     padding=0)
											elif self.is_input_vectorized:
																		a__     						=nn.LayerNorm(_snake_case)
																		a__     						=nn.Linear(_snake_case					,     self.num_vector_embeds - 1)
											elif self.is_input_patches:
																		a__     						=nn.LayerNorm(_snake_case					,     elementwise_affine=_snake_case					,     eps=1e-6)
																		a__     						=nn.Linear(_snake_case					,     2 * inner_dim)
																		a__     						=nn.Linear(_snake_case					,     patch_size * patch_size * self.out_channels)





				def 	__UpperCamelCase						(							self					,     lowercase_					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = None					,     lowercase_ = True					,     )     ->   Any:
											# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
											#   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
											#   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
											# expects mask of shape:
											#   [batch, key_tokens]
											# adds singleton query_tokens dimension:
											#   [batch,                    1, key_tokens]
											# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
											#   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
											#   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
											if attention_mask is not None and attention_mask.ndim == 2:
																		# assume that mask is expressed as:
																		#   (1 = keep,      0 = discard)
																		# convert mask into a bias that can be added to attention scores:
																		#       (keep = +0,     discard = -10000.0)
																		a__     						=(1 - attention_mask.to(hidden_states.dtype)) * -10000.0
																		a__     						=attention_mask.unsqueeze(1)

											# convert encoder_attention_mask to a bias the same way we do for attention_mask
											if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
																		a__     						=(1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
																		a__     						=encoder_attention_mask.unsqueeze(1)

											# 1. Input
											if self.is_input_continuous:
																		a__     						=hidden_states.shape
																		a__     						=hidden_states

																		a__     						=self.norm(_snake_case)
																		if not self.use_linear_projection:
																									a__     						=self.proj_in(_snake_case)
																									a__     						=hidden_states.shape[1]
																									a__     						=hidden_states.permute(0					,     2					,     3					,     1).reshape(_snake_case					,     height * width					,     _snake_case)
																		else:
																									a__     						=hidden_states.shape[1]
																									a__     						=hidden_states.permute(0					,     2					,     3					,     1).reshape(_snake_case					,     height * width					,     _snake_case)
																									a__     						=self.proj_in(_snake_case)
											elif self.is_input_vectorized:
																		a__     						=self.latent_image_embedding(_snake_case)
											elif self.is_input_patches:
																		a__     						=self.pos_embed(_snake_case)

											# 2. Blocks
											for block in self.transformer_blocks:
																		a__     						=block(
																		    _snake_case					,     attention_mask=_snake_case					,     encoder_hidden_states=_snake_case					,     encoder_attention_mask=_snake_case					,     timestep=_snake_case					,     cross_attention_kwargs=_snake_case					,     class_labels=_snake_case					,     )

											# 3. Output
											if self.is_input_continuous:
																		if not self.use_linear_projection:
																									a__     						=hidden_states.reshape(_snake_case					,     _snake_case					,     _snake_case					,     _snake_case).permute(0					,     3					,     1					,     2).contiguous()
																									a__     						=self.proj_out(_snake_case)
																		else:
																									a__     						=self.proj_out(_snake_case)
																									a__     						=hidden_states.reshape(_snake_case					,     _snake_case					,     _snake_case					,     _snake_case).permute(0					,     3					,     1					,     2).contiguous()

																		a__     						=hidden_states + residual
											elif self.is_input_vectorized:
																		a__     						=self.norm_out(_snake_case)
																		a__     						=self.out(_snake_case)
																		# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
																		a__     						=logits.permute(0					,     2					,     1)

																		# log(p(x_0))
																		a__     						=F.log_softmax(logits.double()					,     dim=1).float()
											elif self.is_input_patches:
																		# TODO: cleanup!
																		a__     						=self.transformer_blocks[0].norma.emb(
																		    _snake_case					,     _snake_case					,     hidden_dtype=hidden_states.dtype)
																		a__     						=self.proj_out_a(F.silu(_snake_case)).chunk(2					,     dim=1)
																		a__     						=self.norm_out(_snake_case) * (1 + scale[:, None]) + shift[:, None]
																		a__     						=self.proj_out_a(_snake_case)

																		# unpatchify
																		a__     						=int(hidden_states.shape[1] ** 0.5)
																		a__     						=hidden_states.reshape(
																		    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
																		a__     						=torch.einsum('nhwpqc->nchpwq'					,     _snake_case)
																		a__     						=hidden_states.reshape(
																		    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

											if not return_dict:
																		return (output,)

											return TransformeraDModelOutput(sample=_snake_case)





from typing import Dict, Optional, Tuple

import torch






def      _lowercase(				__a							:	torch.Tensor    ):
							a__     						=boundaries[1] - boundaries[0]
							a__     						=boundaries + step / 2
							a__     						=torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1    )]						,    dim=0    )
							return bin_centers






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	torch.Tensor						,    ):
							a__     						=_calculate_bin_centers(__a    )
							return (
							    torch.sum(aligned_distance_error_probs * bin_centers						,    dim=-1    ),
							    bin_centers[-1],
							)






def      _lowercase(				__a							:	torch.Tensor						,    __a							:	int = 31						,    __a							:	int = 64						,    **__a							:	List[str]						,    ):
							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )
							a__						,    a__     						=_calculate_expected_aligned_error(
							    alignment_confidence_breaks=__a						,    aligned_distance_error_probs=__a						,    )

							return {
							    "aligned_confidence_probs": aligned_confidence_probs,
							    "predicted_aligned_error": predicted_aligned_error,
							    "max_predicted_aligned_error": max_predicted_aligned_error,
							}







def      _lowercase(				__a							:	torch.Tensor						,    __a							:	Optional[torch.Tensor] = None						,    __a							:	int = 31						,    __a							:	int = 64						,    __a							:	float = 1e-8						,    **__a							:	str						,    ):
							if residue_weights is None:
														a__     						=logits.new_ones(logits.shape[-2]    )

							a__     						=torch.linspace(0						,    __a						,    steps=(no_bins - 1)						,    device=logits.device    )

							a__     						=_calculate_bin_centers(__a    )
							torch.sum(__a    )
							a__     						=logits.shape[-2]
							a__     						=max(__a						,    19    )

							a__     						=1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

							a__     						=torch.nn.functional.softmax(__a						,    dim=-1    )

							a__     						=1.0 / (1 + (bin_centers**2) / (da**2))
							a__     						=torch.sum(probs * tm_per_bin						,    dim=-1    )

							a__     						=residue_weights / (eps + residue_weights.sum())
							a__     						=torch.sum(predicted_tm_term * normed_residue_mask						,    dim=-1    )

							a__     						=per_alignment * residue_weights

							a__     						=(weighted == torch.max(__a    )).nonzero()[0]
							return per_alignment[tuple(__a    )]

