





import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 							SCREAMING_SNAKE_CASE__  (      ) ->    str:
	snake_case_						:     Tuple     =		ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
	    )				)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores'				,	type=_lowerCamelCase				,	default=1				,	help='Number of TPU cores to use (1 or 8).'				)

	# positional
	parser.add_argument(
	    'training_script'				,	type=_lowerCamelCase				,	help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    )				,	)

	# rest from the training program
	parser.add_argument('training_script_args'				,	nargs=_lowerCamelCase				)

	return parser.parse_args()


def 							SCREAMING_SNAKE_CASE__  (      ) ->    Union[str, Any]:
	snake_case_						:     int     =		parse_args()

	# Import training_script as a module.
	snake_case_						:     Optional[Any]     =		Path(args.training_script				)
	sys.path.append(str(script_fpath.parent.resolve()				)				)
	snake_case_						:     Any     =		script_fpath.stem
	snake_case_						:     str     =		importlib.import_module(_lowerCamelCase				)

	# Patch sys.argv
	snake_case_						:     str     =		[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores				)]

	xmp.spawn(mod._mp_fn				,	args=()				,	nprocs=args.num_cores				)


if __name__ == "__main__":
						main()








import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):






						@register_to_config
						def __init__( self : Optional[Any]						,				*,
						    _A : int = 4						,				_A : int = 768						,				_A : int						,				_A : Tuple						,				)							->      Dict:







							"""simple docstring"""


							super().__init__()

							snake_case_						:     int     =		nn.Parameter(torch.zeros(_A		)		)

							# parameters for additional clip time embeddings
							snake_case_						:     Tuple     =		nn.Linear(_A						,				_A		)
							snake_case_						:     List[Any]     =		nn.Linear(_A						,				_A		)

							# parameters for encoder hidden states
							snake_case_						:     Union[str, Any]     =		clip_extra_context_tokens
							snake_case_						:     str     =		nn.Linear(
							    _A						,				self.clip_extra_context_tokens * cross_attention_dim		)
							snake_case_						:     Any     =		nn.Linear(_A						,				_A		)
							snake_case_						:     Tuple     =		nn.LayerNorm(_A		)






						def UpperCAmelCase_			( self : List[str]						,				*, _A : Tuple						,				_A : List[Any]						,				_A : str						,				_A : Optional[Any]		)							->      List[Any]:







							"""simple docstring"""


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

							snake_case_						:     str     =		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, ...
							snake_case_						:     str     =		self.embedding_proj(_A		)
							snake_case_						:     Dict     =		self.clip_image_embeddings_project_to_time_embeddings(_A		)
							snake_case_						:     Tuple     =		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"
							snake_case_						:     List[str]     =		self.clip_extra_context_tokens_proj(_A		)
							snake_case_						:     Optional[Any]     =		clip_extra_context_tokens.reshape(_A						,				-1						,				self.clip_extra_context_tokens		)
							snake_case_						:     int     =		clip_extra_context_tokens.permute(0						,				2						,				1		)

							snake_case_						:     Optional[int]     =		self.encoder_hidden_states_proj(_A		)
							snake_case_						:     Any     =		self.text_encoder_hidden_states_norm(_A		)
							snake_case_						:     Dict     =		torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]						,				dim=1		)

							return text_encoder_hidden_states, additive_clip_time_embeddings

