'''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 						SCREAMING_SNAKE_CASE_   (   UpperCAmelCase   ):
							a_        =      4_2






class 						SCREAMING_SNAKE_CASE_   (   UpperCAmelCase       ,	UpperCAmelCase   ):





							@register_to_config
							def __init__(      self				:							List[str]     ,		__a				:							int = 3_2     ,		__a				:							int = 6_4     ,		__a				:							int = 2_0     ,		__a				:							int = 7_6_8     ,		__a				:							Union[str, Any]=7_7     ,		__a				:							Any=4     ,		__a				:							float = 0.0     ,		__a				:							str = "silu"     ,		__a				:							Optional[str] = None     ,		__a				:							Optional[str] = None     ,		__a				:							Optional[str] = "linear"     ,		__a				:							Optional[str] = "prd"     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[int] = None     ,		__a				:							Optional[int] = None     ,		)							->	Union[str, Any]:
								super().__init__()
								__UpperCAmelCase							=			num_attention_heads
								__UpperCAmelCase							=			attention_head_dim
								__UpperCAmelCase							=			num_attention_heads * attention_head_dim
								__UpperCAmelCase							=			additional_embeddings

								__UpperCAmelCase							=			time_embed_dim or inner_dim
								__UpperCAmelCase							=			embedding_proj_dim or embedding_dim
								__UpperCAmelCase							=			clip_embed_dim or embedding_dim

								__UpperCAmelCase							=			Timesteps(__a     ,		__a     ,		0	)
								__UpperCAmelCase							=			TimestepEmbedding(__a     ,		__a     ,		out_dim=__a     ,		act_fn=__a	)

								__UpperCAmelCase							=			nn.Linear(__a     ,		__a	)

								if embedding_proj_norm_type is None:
									__UpperCAmelCase							=			None
								elif embedding_proj_norm_type == "layer":
									__UpperCAmelCase							=			nn.LayerNorm(__a	)
								else:
									raise ValueError(f"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""	)

								__UpperCAmelCase							=			nn.Linear(__a     ,		__a	)

								if encoder_hid_proj_type is None:
									__UpperCAmelCase							=			None
								elif encoder_hid_proj_type == "linear":
									__UpperCAmelCase							=			nn.Linear(__a     ,		__a	)
								else:
									raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""	)

								__UpperCAmelCase							=			nn.Parameter(torch.zeros(1     ,		num_embeddings + additional_embeddings     ,		__a	)	)

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

								__UpperCAmelCase							=			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							=			nn.LayerNorm(__a	)
								elif norm_in_type is None:
									__UpperCAmelCase							=			None
								else:
									raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""	)

								__UpperCAmelCase							=			nn.LayerNorm(__a	)

								__UpperCAmelCase							=			nn.Linear(__a     ,		__a	)

								__UpperCAmelCase							=			torch.full(
								    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]     ,		-1_0_0_0_0.0	)
								causal_attention_mask.triu_(1	)
								__UpperCAmelCase							=			causal_attention_mask[None, ...]
								self.register_buffer('''causal_attention_mask'''     ,		__a     ,		persistent=__a	)

								__UpperCAmelCase							=			nn.Parameter(torch.zeros(1     ,		__a	)	)
								__UpperCAmelCase							=			nn.Parameter(torch.zeros(1     ,		__a	)	)





							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def       snake_case__						(      self				:							Dict	)							->	Dict[str, AttentionProcessor]:
								__UpperCAmelCase							=			{}

								def fn_recursive_add_processors(__a				:							str     ,		__a				:							torch.nn.Module     ,		__a				:							Dict[str, AttentionProcessor]	):
									if hasattr(__a     ,		'''set_processor'''	):
										__UpperCAmelCase							=			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				:							Optional[Any]     ,		__a				:							Union[AttentionProcessor, Dict[str, AttentionProcessor]]	)							->	Any:
								__UpperCAmelCase							=			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(__a				:							str     ,		__a				:							torch.nn.Module     ,		__a				:							Union[str, Any]	):
									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				:							Any	)							->	int:
								self.set_attn_processor(AttnProcessor()	)





							def       snake_case__						(      self				:							List[str]     ,		__a				:							str     ,		__a				:							Union[torch.Tensor, float, int]     ,		__a				:							torch.FloatTensor     ,		__a				:							Optional[torch.FloatTensor] = None     ,		__a				:							Optional[torch.BoolTensor] = None     ,		__a				:							bool = True     ,		)							->	str:
								__UpperCAmelCase							=			hidden_states.shape[0]

								__UpperCAmelCase							=			timestep
								if not torch.is_tensor(__a	):
									__UpperCAmelCase							=			torch.tensor([timesteps]     ,		dtype=torch.long     ,		device=hidden_states.device	)
								elif torch.is_tensor(__a	) and len(timesteps.shape	) == 0:
									__UpperCAmelCase							=			timesteps[None].to(hidden_states.device	)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								__UpperCAmelCase							=			timesteps * torch.ones(__a     ,		dtype=timesteps.dtype     ,		device=timesteps.device	)

								__UpperCAmelCase							=			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							=			timesteps_projected.to(dtype=self.dtype	)
								__UpperCAmelCase							=			self.time_embedding(__a	)

								if self.embedding_proj_norm is not None:
									__UpperCAmelCase							=			self.embedding_proj_norm(__a	)

								__UpperCAmelCase							=			self.embedding_proj(__a	)
								if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
									__UpperCAmelCase							=			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							=			self.proj_in(__a	)

								__UpperCAmelCase							=			self.positional_embedding.to(hidden_states.dtype	)

								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			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							=			proj_embeddings[:, None, :]

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

								__UpperCAmelCase							=			additional_embeds + [
								    proj_embeddings,
								    time_embeddings[:, None, :],
								    hidden_states,
								]

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

								__UpperCAmelCase							=			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							=			additional_embeddings_len + proj_embeddings.shape[1] + 1
								if positional_embeddings.shape[1] < hidden_states.shape[1]:
									__UpperCAmelCase							=			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							=			hidden_states + positional_embeddings

								if attention_mask is not None:
									__UpperCAmelCase							=			(1 - attention_mask.to(hidden_states.dtype	)) * -1_0_0_0_0.0
									__UpperCAmelCase							=			F.pad(__a     ,		(0, self.additional_embeddings)     ,		value=0.0	)
									__UpperCAmelCase							=			(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype	)
									__UpperCAmelCase							=			attention_mask.repeat_interleave(self.config.num_attention_heads     ,		dim=0	)

								if self.norm_in is not None:
									__UpperCAmelCase							=			self.norm_in(__a	)

								for block in self.transformer_blocks:
									__UpperCAmelCase							=			block(__a     ,		attention_mask=__a	)

								__UpperCAmelCase							=			self.norm_out(__a	)

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

								__UpperCAmelCase							=			self.proj_to_clip_embeddings(__a	)

								if not return_dict:
									return (predicted_image_embedding,)

								return PriorTransformerOutput(predicted_image_embedding=__a	)






							def       snake_case__						(      self				:							str     ,		__a				:							int	)							->	str:
								__UpperCAmelCase							=			(prior_latents * self.clip_std) + self.clip_mean
								return prior_latents

'''simple docstring'''


def       lowerCAmelCase					(		UpperCamelCase__				:   Tuple       ):


	"""simple docstring"""
	# if the collection is empty, returns empty
	if collection == []:
		return []

	# get some information about the collection
	__UpperCAmelCase							=			len(UpperCamelCase__       )
	__UpperCAmelCase							=			max(UpperCamelCase__       )
	__UpperCAmelCase							=			min(UpperCamelCase__       )

	# create the counting array
	__UpperCAmelCase							=			coll_max + 1 - coll_min
	__UpperCAmelCase							=			[0] * counting_arr_length

	# count how much a number appears in the collection
	for number in collection:
		counting_arr[number - coll_min] += 1

	# sum each position with it's predecessors. now, counting_arr[i] tells
	# us how many elements <= i has in the collection
	for i in range(1    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			counting_arr[i] + counting_arr[i - 1]

	# create the output collection
	__UpperCAmelCase							=			[0] * coll_len

	# place the elements in the output, respecting the original order (stable
	# sort) from end to begin, updating counting_arr
	for i in reversed(range(0    ,					UpperCamelCase__       )       ):
		__UpperCAmelCase							=			collection[i]
		counting_arr[collection[i] - coll_min] -= 1

	return ordered





def       lowerCAmelCase					(		UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	return "".join([chr(UpperCamelCase__       ) for i in counting_sort([ord(UpperCamelCase__       ) for c in string]       )]       )


if __name__ == "__main__":
					# Test string sort
					assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

					__lowerCAmelCase	:       str						       =	input("Enter numbers separated by a comma:\n").strip()
					__lowerCAmelCase	:       List[Any]						       =	[int(item) for item in user_input.split(",")]
					print(counting_sort(unsorted))
