


"""simple docstring"""






import torch
from torch import nn

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


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				@register_to_config
				def __init__(						self       ,		*,
				    UpperCAmelCase__ = 4       ,		UpperCAmelCase__ = 768       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								super().__init__()

								SCREAMING_SNAKE_CASE__     							=				nn.Parameter(torch.zeros(UpperCAmelCase__     )     )

								# parameters for additional clip time embeddings
								SCREAMING_SNAKE_CASE__     							=				nn.Linear(UpperCAmelCase__       ,		UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				nn.Linear(UpperCAmelCase__       ,		UpperCAmelCase__     )

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






				def   lowerCAmelCase__		(						self       ,		*, UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								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(
												    UpperCAmelCase__       ,		-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(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.clip_image_embeddings_project_to_time_embeddings(UpperCAmelCase__     )
								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(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				clip_extra_context_tokens.reshape(UpperCAmelCase__       ,		-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(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				self.text_encoder_hidden_states_norm(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]       ,		dim=1     )

								return text_encoder_hidden_states, additive_clip_time_embeddings




"""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     =  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     =  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     =  rh.cluster(
		    name='rh-cluster', ips=[args.host], ssh_creds={'ssh_user': args.user, 'ssh_private_key': args.key_path}
		)
	else:
		_lowerCamelCase     =  rh.cluster(
		    name='rh-cluster', instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
		)
	_lowerCamelCase     =  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)
