


'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class      lowercase			(							SCREAMING_SNAKE_CASE__      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							super().__init__()
							self.register_modules(transformer=snake_case__					,     vae=snake_case__					,     scheduler=snake_case__							)

							# create a imagenet -> id dictionary for easier use
							UpperCamelCase__     :Union[str, Any]							      =				{}
							if idalabel is not None:
												for key, value in idalabel.items():
																	for label in value.split(''','''							):
																						UpperCamelCase__     :Optional[int]							      =				int(snake_case__							)
												UpperCamelCase__     :Optional[Any]							      =				dict(sorted(self.labels.items()							)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if not isinstance(snake_case__					,     snake_case__							):
												UpperCamelCase__     :int							      =				list(snake_case__							)

							for l in label:
												if l not in self.labels:
																	raise ValueError(
																	    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''							)

							return [self.labels[l] for l in label]




		@torch.no_grad()
		def __call__(	self					,     UpperCamelCase_					,     UpperCamelCase_ = 4.0					,     UpperCamelCase_ = None					,     UpperCamelCase_ = 50					,     UpperCamelCase_ = "pil"					,     UpperCamelCase_ = True					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[Any]							      =				len(snake_case__							)
							UpperCamelCase__     :Any							      =				self.transformer.config.sample_size
							UpperCamelCase__     :List[Any]							      =				self.transformer.config.in_channels

							UpperCamelCase__     :List[Any]							      =				randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)					,     generator=snake_case__					,     device=self.device					,     dtype=self.transformer.dtype					,     )
							UpperCamelCase__     :str							      =				torch.cat([latents] * 2							) if guidance_scale > 1 else latents

							UpperCamelCase__     :List[str]							      =				torch.tensor(snake_case__					,     device=self.device							).reshape(-1							)
							UpperCamelCase__     :Optional[Any]							      =				torch.tensor([1000] * batch_size					,     device=self.device							)
							UpperCamelCase__     :int							      =				torch.cat([class_labels, class_null]					,     0							) if guidance_scale > 1 else class_labels

							# set step values
							self.scheduler.set_timesteps(snake_case__							)

							for t in self.progress_bar(self.scheduler.timesteps							):
												if guidance_scale > 1:
																	UpperCamelCase__     :Optional[Any]							      =				latent_model_input[: len(snake_case__							) // 2]
																	UpperCamelCase__     :Tuple							      =				torch.cat([half, half]					,     dim=0							)
												UpperCamelCase__     :Optional[int]							      =				self.scheduler.scale_model_input(snake_case__					,     snake_case__							)

												UpperCamelCase__     :Union[str, Any]							      =				t
												if not torch.is_tensor(snake_case__							):
																	# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																	# This would be a good case for the `match` statement (Python 3.10+)
																	UpperCamelCase__     :Any							      =				latent_model_input.device.type == "mps"
																	if isinstance(snake_case__					,     snake_case__							):
																						UpperCamelCase__     :Tuple							      =				torch.floataa if is_mps else torch.floataa
																	else:
																						UpperCamelCase__     :Tuple							      =				torch.intaa if is_mps else torch.intaa
																	UpperCamelCase__     :List[Any]							      =				torch.tensor([timesteps]					,     dtype=snake_case__					,     device=latent_model_input.device							)
												elif len(timesteps.shape							) == 0:
																	UpperCamelCase__     :Dict							      =				timesteps[None].to(latent_model_input.device							)
												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												UpperCamelCase__     :Optional[int]							      =				timesteps.expand(latent_model_input.shape[0]							)
												# predict noise model_output
												UpperCamelCase__     :Optional[Any]							      =				self.transformer(
												    snake_case__					,     timestep=snake_case__					,     class_labels=snake_case__							).sample

												# perform guidance
												if guidance_scale > 1:
																	UpperCamelCase__     :Optional[int]							      =				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																	UpperCamelCase__     :Dict							      =				torch.split(snake_case__					,     len(snake_case__							) // 2					,     dim=0							)

																	UpperCamelCase__     :Optional[Any]							      =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																	UpperCamelCase__     :Tuple							      =				torch.cat([half_eps, half_eps]					,     dim=0							)

																	UpperCamelCase__     :Optional[int]							      =				torch.cat([eps, rest]					,     dim=1							)

												# learned sigma
												if self.transformer.config.out_channels // 2 == latent_channels:
																	UpperCamelCase__     :Dict							      =				torch.split(snake_case__					,     snake_case__					,     dim=1							)
												else:
																	UpperCamelCase__     :str							      =				noise_pred

												# compute previous image: x_t -> x_t-1
												UpperCamelCase__     :Any							      =				self.scheduler.step(snake_case__					,     snake_case__					,     snake_case__							).prev_sample

							if guidance_scale > 1:
												UpperCamelCase__     :Any							      =				latent_model_input.chunk(2					,     dim=0							)
							else:
												UpperCamelCase__     :List[str]							      =				latent_model_input

							UpperCamelCase__     :Any							      =				1 / self.vae.config.scaling_factor * latents
							UpperCamelCase__     :Dict							      =				self.vae.decode(snake_case__							).sample

							UpperCamelCase__     :Dict							      =				(samples / 2 + 0.5).clamp(0					,     1							)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							UpperCamelCase__     :Tuple							      =				samples.cpu().permute(0					,     2					,     3					,     1							).float().numpy()

							if output_type == "pil":
												UpperCamelCase__     :Tuple							      =				self.numpy_to_pil(snake_case__							)

							if not return_dict:
												return (samples,)

							return ImagePipelineOutput(images=snake_case__							)



'''simple docstring'''
import torch
from torch import nn
class      lowercase			(							nn.Module      ):

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_=1					,     UpperCamelCase_=False							):

							'''simple docstring'''


							super().__init__()

							UpperCamelCase__     :Dict							      =				n_token
							UpperCamelCase__     :List[Any]							      =				d_embed
							UpperCamelCase__     :Dict							      =				d_proj

							UpperCamelCase__     :Dict							      =				cutoffs + [n_token]
							UpperCamelCase__     :Union[str, Any]							      =				[0] + self.cutoffs
							UpperCamelCase__     :Any							      =				div_val

							UpperCamelCase__     :int							      =				self.cutoffs[0]
							UpperCamelCase__     :List[Any]							      =				len(self.cutoffs							) - 1
							UpperCamelCase__     :List[Any]							      =				self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
												UpperCamelCase__     :Any							      =				nn.Parameter(torch.zeros(self.n_clusters					,     self.d_embed							)							)
												UpperCamelCase__     :Optional[Any]							      =				nn.Parameter(torch.zeros(self.n_clusters							)							)

							UpperCamelCase__     :Union[str, Any]							      =				nn.ModuleList()
							UpperCamelCase__     :str							      =				nn.ParameterList()

							if div_val == 1:
												for i in range(len(self.cutoffs							)							):
																	if d_proj != d_embed:
																						self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)
																	else:
																						self.out_projs.append(UpperCamelCase_							)

												self.out_layers.append(nn.Linear(UpperCamelCase_					,     UpperCamelCase_							)							)
							else:
												for i in range(len(self.cutoffs							)							):
																	UpperCamelCase__      ,       UpperCamelCase__     :List[str]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																	UpperCamelCase__     :Dict							      =				d_embed // (div_val**i)

																	self.out_projs.append(nn.Parameter(torch.FloatTensor(UpperCamelCase_					,     UpperCamelCase_							)							)							)

																	self.out_layers.append(nn.Linear(UpperCamelCase_					,     r_idx - l_idx							)							)

							UpperCamelCase__     :Tuple							      =				keep_order




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							if proj is None:
												UpperCamelCase__     :List[str]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
							else:
												# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
												UpperCamelCase__     :Any							      =				nn.functional.linear(UpperCamelCase_					,     proj.t().contiguous()							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.linear(UpperCamelCase_					,     UpperCamelCase_					,     bias=UpperCamelCase_							)
												# else:
												#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
												#     if bias is not None:
												#         logit = logit + bias

							return logit




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=None					,     UpperCamelCase_=False							):

							'''simple docstring'''


							if labels is not None:
												# Shift so that tokens < n predict n
												UpperCamelCase__     :Optional[Any]							      =				hidden[..., :-1, :].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				labels[..., 1:].contiguous()
												UpperCamelCase__     :Optional[Any]							      =				hidden.view(-1					,     hidden.size(-1							)							)
												UpperCamelCase__     :str							      =				labels.view(-1							)
												if hidden.size(0							) != labels.size(0							):
																	raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''							)
							else:
												UpperCamelCase__     :int							      =				hidden.view(-1					,     hidden.size(-1							)							)

							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												if labels is not None:
																	UpperCamelCase__     :int							      =				labels != -100
																	UpperCamelCase__     :List[Any]							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)
																	UpperCamelCase__     :str							      =				(
																	    -nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)[mask].gather(1					,     labels[mask].unsqueeze(1							)							).squeeze(1							)
																	)
												else:
																	UpperCamelCase__     :Dict							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :str							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :Optional[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :Optional[int]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :str							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :List[str]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[Any]							      =				weights[0], biases[0], self.out_projs[0]

												UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												if labels is None:
																	UpperCamelCase__     :Union[str, Any]							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												else:
																	UpperCamelCase__     :Any							      =				torch.zeros_like(UpperCamelCase_					,     dtype=hidden.dtype					,     device=hidden.device							)

												UpperCamelCase__     :Any							      =				0
												UpperCamelCase__     :str							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if labels is not None:
																						UpperCamelCase__     :Any							      =				(labels >= l_idx) & (labels < r_idx)
																						UpperCamelCase__     :int							      =				mask_i.nonzero().squeeze()

																						if indices_i.numel() == 0:
																											continue

																						UpperCamelCase__     :Tuple							      =				labels.index_select(0					,     UpperCamelCase_							) - l_idx
																						UpperCamelCase__     :str							      =				head_logprob.index_select(0					,     UpperCamelCase_							)
																						UpperCamelCase__     :int							      =				hidden.index_select(0					,     UpperCamelCase_							)
																	else:
																						UpperCamelCase__     :Dict							      =				hidden

																	if i == 0:
																						if labels is not None:
																											UpperCamelCase__     :Any							      =				head_logprob_i.gather(1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :str							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)
																						UpperCamelCase__     :Any							      =				self.cutoffs[0] + i - 1  # No probability for the head cluster
																						if labels is not None:
																											UpperCamelCase__     :List[Any]							      =				head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
																											    1					,     target_i[:, None]							).squeeze(1							)
																						else:
																											UpperCamelCase__     :List[str]							      =				head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
																											UpperCamelCase__     :Dict							      =				logprob_i

																	if labels is not None:
																						if (hasattr(self					,     '''keep_order'''							) and self.keep_order) or keep_order:
																											out.index_copy_(0					,     UpperCamelCase_					,     -logprob_i							)
																						else:
																											out[offset : offset + logprob_i.size(0							)].copy_(-logprob_i							)
																						offset += logprob_i.size(0							)

							return out




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.n_clusters == 0:
												UpperCamelCase__     :Optional[Any]							      =				self._compute_logit(UpperCamelCase_					,     self.out_layers[0].weight					,     self.out_layers[0].bias					,     self.out_projs[0]							)
												return nn.functional.log_softmax(UpperCamelCase_					,     dim=-1							)
							else:
												# construct weights and biases
												UpperCamelCase__      ,       UpperCamelCase__     :List[Any]							      =				[], []
												for i in range(len(self.cutoffs							)							):
																	if self.div_val == 1:
																						UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.cutoff_ends[i], self.cutoff_ends[i + 1]
																						UpperCamelCase__     :Union[str, Any]							      =				self.out_layers[0].weight[l_idx:r_idx]
																						UpperCamelCase__     :str							      =				self.out_layers[0].bias[l_idx:r_idx]
																	else:
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].weight
																						UpperCamelCase__     :List[Any]							      =				self.out_layers[i].bias

																	if i == 0:
																						UpperCamelCase__     :Dict							      =				torch.cat([weight_i, self.cluster_weight]					,     dim=0							)
																						UpperCamelCase__     :Optional[Any]							      =				torch.cat([bias_i, self.cluster_bias]					,     dim=0							)

																	weights.append(UpperCamelCase_							)
																	biases.append(UpperCamelCase_							)

												UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :str							      =				weights[0], biases[0], self.out_projs[0]
												UpperCamelCase__     :Optional[int]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

												UpperCamelCase__     :int							      =				hidden.new_empty((head_logit.size(0							), self.n_token)							)
												UpperCamelCase__     :Union[str, Any]							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

												UpperCamelCase__     :int							      =				[0] + self.cutoffs
												for i in range(len(UpperCamelCase_							) - 1							):
																	UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				cutoff_values[i], cutoff_values[i + 1]

																	if i == 0:
																						UpperCamelCase__     :str							      =				head_logprob[:, : self.cutoffs[0]]
																	else:
																						UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				weights[i], biases[i], self.out_projs[i]

																						UpperCamelCase__     :List[str]							      =				self._compute_logit(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)
																						UpperCamelCase__     :Tuple							      =				nn.functional.log_softmax(UpperCamelCase_					,     dim=1							)

																						UpperCamelCase__     :Optional[Any]							      =				head_logprob[:, -i] + tail_logprob_i
																						UpperCamelCase__     :Union[str, Any]							      =				logprob_i

												return out