from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def        __lowerCAmelCase      (	):



							"""simple docstring"""


							_lowercase					=						[randint(-1_000			,1_000       ) for i in range(10       )]
							_lowercase					=						randint(-5_000			,5_000       )
							return (arr, r)


A_:   Dict   =      make_dataset()


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							for triplet in permutations(_A			,3       ):
														if sum(_A       ) == target:
																					return tuple(sorted(_A       )       )
							return (0, 0, 0)


def        __lowerCAmelCase      (	_A			,_A       ):



							"""simple docstring"""


							arr.sort()
							_lowercase					=						len(_A       )
							for i in range(n - 1       ):
														_lowercase					,							_lowercase					=						i + 1, n - 1
														while left < right:
																					if arr[i] + arr[left] + arr[right] == target:
																												return (arr[i], arr[left], arr[right])
																					elif arr[i] + arr[left] + arr[right] < target:
																												left += 1
																					elif arr[i] + arr[left] + arr[right] > target:
																												right -= 1
							return (0, 0, 0)


def        __lowerCAmelCase      (	):



							"""simple docstring"""


							_lowercase					=						"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
							_lowercase					=						"""
triplet_sum1(*dataset)
"""
							_lowercase					=						"""
triplet_sum2(*dataset)
"""
							_lowercase					=						repeat(setup=_A			,stmt=_A			,repeat=5			,number=10_000       )
							_lowercase					=						repeat(setup=_A			,stmt=_A			,repeat=5			,number=10_000       )
							return (min(_A       ), min(_A       ))


if __name__ == "__main__":
				from doctest import testmod

				testmod()
				A_:   Optional[int]   =      solution_times()
				print(F'''The time for naive implementation is {times[0]}.''')
				print(F'''The time for optimized implementation is {times[1]}.''')




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class     lowerCamelCase      (       __lowerCamelCase ,		__lowerCamelCase					):







	@register_to_config
	def __init__(     self      :Tuple   ,     *,
	    lowercase      :int = 4   ,     lowercase      :int = 7_6_8   ,     lowercase      :int   ,     lowercase      :Any   ,     )		->      Optional[Any]:







				"""simple docstring"""






				super().__init__()

				SCREAMING_SNAKE_CASE     						= nn.Parameter(torch.zeros(lowercase			)			)

				# parameters for additional clip time embeddings
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)

				# parameters for encoder hidden states
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens
				SCREAMING_SNAKE_CASE     						= nn.Linear(
				    lowercase   ,     self.clip_extra_context_tokens * cross_attention_dim			)
				SCREAMING_SNAKE_CASE     						= nn.Linear(lowercase   ,     lowercase			)
				SCREAMING_SNAKE_CASE     						= nn.LayerNorm(lowercase			)







	def  snake_case__     (     self      :Union[str, Any]   ,     *, lowercase      :Optional[int]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Union[str, Any]   ,     lowercase      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






				if do_classifier_free_guidance:
							# Add the classifier free guidance embeddings to the image embeddings
							SCREAMING_SNAKE_CASE     						= image_embeddings.shape[0]
							SCREAMING_SNAKE_CASE     						= self.learned_classifier_free_guidance_embeddings.unsqueeze(0			)
							SCREAMING_SNAKE_CASE     						= classifier_free_guidance_embeddings.expand(
							    lowercase   ,     -1			)
							SCREAMING_SNAKE_CASE     						= torch.cat([classifier_free_guidance_embeddings, image_embeddings]   ,     dim=0			)

				# The image embeddings batch size and the text embeddings batch size are equal
				assert image_embeddings.shape[0] == prompt_embeds.shape[0]

				SCREAMING_SNAKE_CASE     						= prompt_embeds.shape[0]

				# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
				# adding CLIP embeddings to the existing timestep embedding, ...
				SCREAMING_SNAKE_CASE     						= self.embedding_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.clip_image_embeddings_project_to_time_embeddings(lowercase			)
				SCREAMING_SNAKE_CASE     						= time_projected_image_embeddings + time_projected_prompt_embeds

				# ... and by projecting CLIP embeddings into four
				# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
				SCREAMING_SNAKE_CASE     						= self.clip_extra_context_tokens_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens.reshape(lowercase   ,     -1   ,     self.clip_extra_context_tokens			)
				SCREAMING_SNAKE_CASE     						= clip_extra_context_tokens.permute(0   ,     2   ,     1			)

				SCREAMING_SNAKE_CASE     						= self.encoder_hidden_states_proj(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.text_encoder_hidden_states_norm(lowercase			)
				SCREAMING_SNAKE_CASE     						= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]   ,     dim=1			)

				return text_encoder_hidden_states, additive_clip_time_embeddings