

import gc
import threading
import time

import psutil
import torch




class 				__UpperCamelCase				:







	def __init__(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    psutil.Process()
						__magic_name__							=    False







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						__magic_name__							=    -1

						while True:
											__magic_name__							=    max(self.process.memory_info().rss  , self.cpu_memory_peak      )

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											if not self.peak_monitoring:
																break







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    True
						__magic_name__							=    threading.Thread(target=self.peak_monitor      )
						__magic_name__							=    True
						self.thread.start()







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    False
						self.thread.join()
						return self.cpu_memory_peak




A__						=							PeakCPUMemory()




def 					_lowercase     (				)				->	List[str]:




					'''simple docstring'''







					__magic_name__							=    {'time': time.time()}

					gc.collect()
					torch.cuda.empty_cache()

					# CPU mem
					__magic_name__							=    psutil.Process().memory_info().rss
					cpu_peak_tracker.start()

					# GPU mem
					for i in range(torch.cuda.device_count()				):
										__magic_name__							=    torch.cuda.memory_allocated(a_				)
					torch.cuda.reset_peak_memory_stats()

					return measures




def 					_lowercase     (				a_				:					Dict				)				->	Optional[Any]:




					'''simple docstring'''







					__magic_name__							=    {'time': time.time() - start_measures['time']}

					gc.collect()
					torch.cuda.empty_cache()

					# CPU mem
					__magic_name__							=    (psutil.Process().memory_info().rss - start_measures['cpu']) / 2**2_0
					__magic_name__							=    (cpu_peak_tracker.stop() - start_measures['cpu']) / 2**2_0

					# GPU mem
					for i in range(torch.cuda.device_count()				):
										__magic_name__							=    (torch.cuda.memory_allocated(a_				) - start_measures[str(a_				)]) / 2**2_0
										__magic_name__							=    (torch.cuda.max_memory_allocated(a_				) - start_measures[str(a_				)]) / 2**2_0

					return measures




def 					_lowercase     (				a_				:					Optional[Any]					,a_				:					Dict				)				->	Any:




					'''simple docstring'''







					print(F'{description}:'				)
					print(F'- Time: {measures["time"]:.2f}s'				)
					for i in range(torch.cuda.device_count()				):
										print(F'- GPU {i} allocated: {measures[str(a_				)]:.2f}MiB'				)
										__magic_name__							=    measures[F'{i}-peak']
										print(F'- GPU {i} peak: {peak:.2f}MiB'				)
					print(F'- CPU RAM allocated: {measures["cpu"]:.2f}MiB'				)
					print(F'- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB'				)







import torch
from torch import nn

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

						__magic_name__							=    nn.Parameter(torch.zeros(__UpperCamelCase      )      )

						# parameters for additional clip time embeddings
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    nn.Linear(__UpperCamelCase  , __UpperCamelCase      )

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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





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

						__magic_name__							=    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, ...
						__magic_name__							=    self.embedding_proj(__UpperCamelCase      )
						__magic_name__							=    self.clip_image_embeddings_project_to_time_embeddings(__UpperCamelCase      )
						__magic_name__							=    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"
						__magic_name__							=    self.clip_extra_context_tokens_proj(__UpperCamelCase      )
						__magic_name__							=    clip_extra_context_tokens.reshape(__UpperCamelCase  , -1  , self.clip_extra_context_tokens      )
						__magic_name__							=    clip_extra_context_tokens.permute(0  , 2  , 1      )

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

						return text_encoder_hidden_states, additive_clip_time_embeddings




