





def 							SCREAMING_SNAKE_CASE__  (      __a				):
	# if the collection is empty, returns empty
	if collection == []:
		return []

	# get some information about the collection
	snake_case_						:     Dict     =		len(__a				)
	snake_case_						:     Optional[int]     =		max(__a				)
	snake_case_						:     Optional[Any]     =		min(__a				)

	# create the counting array
	snake_case_						:     Any     =		coll_max + 1 - coll_min
	snake_case_						:     str     =		[0] * counting_arr_length

	# count how much a number appears in the collection
	for number in collection:
		counting_arr[number - coll_min] += 1

	# sum each position with it's predecessors. now, counting_arr[i] tells
	# us how many elements <= i has in the collection
	for i in range(1				,	__a				):
		snake_case_						:     Optional[int]     =		counting_arr[i] + counting_arr[i - 1]

	# create the output collection
	snake_case_						:     List[Any]     =		[0] * coll_len

	# place the elements in the output, respecting the original order (stable
	# sort) from end to begin, updating counting_arr
	for i in reversed(range(0				,	__a				)				):
		snake_case_						:     int     =		collection[i]
		counting_arr[collection[i] - coll_min] -= 1

	return ordered


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	return "".join([chr(__a				) for i in counting_sort([ord(__a				) for c in string]				)]				)


if __name__ == "__main__":
						# Test string sort
						assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"

						_SCREAMING_SNAKE_CASE 		=						input("""Enter numbers separated by a comma:\n""").strip()
						_SCREAMING_SNAKE_CASE 		=						[int(item) for item in user_input.split(""",""")]
						print(counting_sort(unsorted))








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

