


'''simple docstring'''



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

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

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin


@dataclass
class 		_snake_case					(     snake_case__	):
					lowerCAmelCase      :torch.FloatTensor


class 		_snake_case					(     snake_case__       ,   snake_case__	):




					@register_to_config
					def __init__(      self							,							_lowerCamelCase = 32							,							_lowerCamelCase = 64							,							_lowerCamelCase = 20							,							_lowerCamelCase = 768							,							_lowerCamelCase=77							,							_lowerCamelCase=4							,							_lowerCamelCase = 0.0							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = "linear"							,							_lowerCamelCase = "prd"							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								super().__init__()
								UpperCAmelCase__  :					List[str]        =     num_attention_heads
								UpperCAmelCase__  :					Optional[int]        =     attention_head_dim
								UpperCAmelCase__  :					Optional[int]        =     num_attention_heads * attention_head_dim
								UpperCAmelCase__  :					Any        =     additional_embeddings

								UpperCAmelCase__  :					str        =     time_embed_dim or inner_dim
								UpperCAmelCase__  :					Union[str, Any]        =     embedding_proj_dim or embedding_dim
								UpperCAmelCase__  :					Tuple        =     clip_embed_dim or embedding_dim

								UpperCAmelCase__  :					Optional[Any]        =     Timesteps(_A							,							_A							,							0)
								UpperCAmelCase__  :					Dict        =     TimestepEmbedding(_A							,							_A							,							out_dim=_A							,							act_fn=_A)

								UpperCAmelCase__  :					Any        =     nn.Linear(_A							,							_A)

								if embedding_proj_norm_type is None:
											UpperCAmelCase__  :					Any        =     None
								elif embedding_proj_norm_type == "layer":
											UpperCAmelCase__  :					Dict        =     nn.LayerNorm(_A)
								else:
											raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}''')

								UpperCAmelCase__  :					int        =     nn.Linear(_A							,							_A)

								if encoder_hid_proj_type is None:
											UpperCAmelCase__  :					Dict        =     None
								elif encoder_hid_proj_type == "linear":
											UpperCAmelCase__  :					Tuple        =     nn.Linear(_A							,							_A)
								else:
											raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}''')

								UpperCAmelCase__  :					List[str]        =     nn.Parameter(torch.zeros(1							,							num_embeddings + additional_embeddings							,							_A))

								if added_emb_type == "prd":
											UpperCAmelCase__  :					Any        =     nn.Parameter(torch.zeros(1							,							1							,							_A))
								elif added_emb_type is None:
											UpperCAmelCase__  :					Any        =     None
								else:
											raise ValueError(
											    f'''`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.''')

								UpperCAmelCase__  :					Any        =     nn.ModuleList(
								    [
								        BasicTransformerBlock(
								            _A							,							_A							,							_A							,							dropout=_A							,							activation_fn="""gelu"""							,							attention_bias=_A							,							)
								        for d in range(_A)
								    ])

								if norm_in_type == "layer":
											UpperCAmelCase__  :					List[str]        =     nn.LayerNorm(_A)
								elif norm_in_type is None:
											UpperCAmelCase__  :					Dict        =     None
								else:
											raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.''')

								UpperCAmelCase__  :					List[Any]        =     nn.LayerNorm(_A)

								UpperCAmelCase__  :					int        =     nn.Linear(_A							,							_A)

								UpperCAmelCase__  :					Optional[Any]        =     torch.full(
								    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]							,							-1_0000.0)
								causal_attention_mask.triu_(1)
								UpperCAmelCase__  :					str        =     causal_attention_mask[None, ...]
								self.register_buffer("""causal_attention_mask"""							,							_A							,							persistent=_A)

								UpperCAmelCase__  :					str        =     nn.Parameter(torch.zeros(1							,							_A))
								UpperCAmelCase__  :					Union[str, Any]        =     nn.Parameter(torch.zeros(1							,							_A))




					@property
					# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     {}

								def fn_recursive_add_processors(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											if hasattr(_A							,							"""set_processor"""):
														UpperCAmelCase__  :					int        =     module.processor

											for sub_name, child in module.named_children():
														fn_recursive_add_processors(f'''{name}.{sub_name}'''							,							_A							,							_A)

											return processors

								for name, module in self.named_children():
											fn_recursive_add_processors(_A							,							_A							,							_A)

								return processors




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					int        =     len(self.attn_processors.keys())

								if isinstance(_A							,							_A) and len(_A) != count:
											raise ValueError(
											    f'''A dict of processors was passed, but the number of processors {len(_A)} does not match the'''
											    f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''')

								def fn_recursive_attn_processor(_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
											if hasattr(_A							,							"""set_processor"""):
														if not isinstance(_A							,							_A):
																	module.set_processor(_A)
														else:
																	module.set_processor(processor.pop(f'''{name}.processor'''))

											for sub_name, child in module.named_children():
														fn_recursive_attn_processor(f'''{name}.{sub_name}'''							,							_A							,							_A)

								for name, module in self.named_children():
											fn_recursive_attn_processor(_A							,							_A							,							_A)




					def 				snake_case__						(      self):
								self.set_attn_processor(AttnProcessor())




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							):
								UpperCAmelCase__  :					int        =     hidden_states.shape[0]

								UpperCAmelCase__  :					Optional[int]        =     timestep
								if not torch.is_tensor(_A):
											UpperCAmelCase__  :					Optional[Any]        =     torch.tensor([timesteps]							,							dtype=torch.long							,							device=hidden_states.device)
								elif torch.is_tensor(_A) and len(timesteps.shape) == 0:
											UpperCAmelCase__  :					Optional[int]        =     timesteps[None].to(hidden_states.device)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								UpperCAmelCase__  :					List[Any]        =     timesteps * torch.ones(_A							,							dtype=timesteps.dtype							,							device=timesteps.device)

								UpperCAmelCase__  :					int        =     self.time_proj(_A)

								# timesteps does not contain any weights and will always return f32 tensors
								# but time_embedding might be fp16, so we need to cast here.
								UpperCAmelCase__  :					List[Any]        =     timesteps_projected.to(dtype=self.dtype)
								UpperCAmelCase__  :					str        =     self.time_embedding(_A)

								if self.embedding_proj_norm is not None:
											UpperCAmelCase__  :					Dict        =     self.embedding_proj_norm(_A)

								UpperCAmelCase__  :					Dict        =     self.embedding_proj(_A)
								if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
											UpperCAmelCase__  :					Any        =     self.encoder_hidden_states_proj(_A)
								elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
											raise ValueError("""`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set""")

								UpperCAmelCase__  :					Optional[int]        =     self.proj_in(_A)

								UpperCAmelCase__  :					Tuple        =     self.positional_embedding.to(hidden_states.dtype)

								UpperCAmelCase__  :					List[Any]        =     []
								UpperCAmelCase__  :					str        =     0

								if encoder_hidden_states is not None:
											additional_embeds.append(_A)
											additional_embeddings_len += encoder_hidden_states.shape[1]

								if len(proj_embeddings.shape) == 2:
											UpperCAmelCase__  :					Optional[Any]        =     proj_embeddings[:, None, :]

								if len(hidden_states.shape) == 2:
											UpperCAmelCase__  :					int        =     hidden_states[:, None, :]

								UpperCAmelCase__  :					Dict        =     additional_embeds + [
								    proj_embeddings,
								    time_embeddings[:, None, :],
								    hidden_states,
								]

								if self.prd_embedding is not None:
											UpperCAmelCase__  :					str        =     self.prd_embedding.to(hidden_states.dtype).expand(_A							,							-1							,							-1)
											additional_embeds.append(_A)

								UpperCAmelCase__  :					Union[str, Any]        =     torch.cat(
								    _A							,							dim=1							,							)

								# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
								UpperCAmelCase__  :					str        =     additional_embeddings_len + proj_embeddings.shape[1] + 1
								if positional_embeddings.shape[1] < hidden_states.shape[1]:
											UpperCAmelCase__  :					int        =     F.pad(
											    _A							,							(
											        0,
											        0,
											        additional_embeddings_len,
											        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
											    )							,							value=0.0							,							)

								UpperCAmelCase__  :					int        =     hidden_states + positional_embeddings

								if attention_mask is not None:
											UpperCAmelCase__  :					List[str]        =     (1 - attention_mask.to(hidden_states.dtype)) * -1_0000.0
											UpperCAmelCase__  :					Any        =     F.pad(_A							,							(0, self.additional_embeddings)							,							value=0.0)
											UpperCAmelCase__  :					int        =     (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype)
											UpperCAmelCase__  :					Optional[Any]        =     attention_mask.repeat_interleave(self.config.num_attention_heads							,							dim=0)

								if self.norm_in is not None:
											UpperCAmelCase__  :					int        =     self.norm_in(_A)

								for block in self.transformer_blocks:
											UpperCAmelCase__  :					Optional[Any]        =     block(_A							,							attention_mask=_A)

								UpperCAmelCase__  :					int        =     self.norm_out(_A)

								if self.prd_embedding is not None:
											UpperCAmelCase__  :					Any        =     hidden_states[:, -1]
								else:
											UpperCAmelCase__  :					Dict        =     hidden_states[:, additional_embeddings_len:]

								UpperCAmelCase__  :					Any        =     self.proj_to_clip_embeddings(_A)

								if not return_dict:
											return (predicted_image_embedding,)

								return PriorTransformerOutput(predicted_image_embedding=_A)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     (prior_latents * self.clip_std) + self.clip_mean
								return prior_latents



'''simple docstring'''



def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			if height >= 1:
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
						move_disk(UpperCamelCase__ ,      UpperCamelCase__					)
						move_tower(height - 1 ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			print("""moving disk from""" ,      UpperCamelCase__ ,      """to""" ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					List[str]        =     int(input("""Height of hanoi: """					).strip()					)
			move_tower(UpperCamelCase__ ,      """A""" ,      """B""" ,      """C"""					)


if __name__ == "__main__":
		main()