





'''simple docstring'''







import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
					# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
					# setup instructions, if using on-demand hardware

					# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
					# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
					# Throw an error if user passes both BYO and on-demand cluster args
					# Otherwise, use default values
					lowerCAmelCase_					:    List[str]      	=			argparse.ArgumentParser()
					parser.add_argument('--user', type=str, default='ubuntu')
					parser.add_argument('--host', type=str, default='localhost')
					parser.add_argument('--key_path', type=str, default=None)
					parser.add_argument('--instance', type=str, default='V100:1')
					parser.add_argument('--provider', type=str, default='cheapest')
					parser.add_argument('--use_spot', type=bool, default=False)
					parser.add_argument('--example', type=str, default='pytorch/text-generation/run_generation.py')
					lowerCAmelCase_	,  lowerCAmelCase_					:    List[Any]      	=			parser.parse_known_args()
					if args.host != "localhost":
										if args.instance != "V100:1" or args.provider != "cheapest":
															raise ValueError('Cannot specify both BYO and on-demand cluster args')
										lowerCAmelCase_					:    List[Any]      	=			rh.cluster(
										    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
										)
					else:
										lowerCAmelCase_					:    Dict      	=			rh.cluster(
										    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
										)
					lowerCAmelCase_					:    Optional[Any]      	=			args.example.rsplit('/', 1)[0]

					# Set up remote environment
					cluster.install_packages(['pip:./'])  # Installs transformers from local source
					# Note transformers is copied into the home directory on the remote machine, so we can install from there
					cluster.run([f"""pip install -r transformers/examples/{example_dir}/requirements.txt"""])
					cluster.run(['pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117'])

					# Run example. You can bypass the CLI wrapper and paste your own code here.
					cluster.run([f"""python transformers/examples/{args.example} {' '.join(shlex.quote(arg) for arg in unknown)}"""])

					# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
					# from my_script... import train
					# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
					# launch_train_gpu = rh.function(fn=train,
					#                                system=gpu,
					#                                reqs=reqs,
					#                                name='train_bert_glue')
					#
					# We can pass in arguments just like we would to a function:
					# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
					#                  stream_logs=True)







'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

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




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= 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]

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

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

								return text_encoder_hidden_states, additive_clip_time_embeddings
