





from __future__ import annotations





def      lowerCAmelCase_ (       __a	,    __a	,    __a	,    __a		)     ->		list:







  """simple docstring"""





  lowerCamelCase__:      Any			  =[]
  lowerCamelCase__  ,					lowerCamelCase__:      Any			  =input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0		)		)
  lowerCamelCase__:      str			  =result + left + right
  return input_list





def      lowerCAmelCase_ (       __a		)     ->		list:







  """simple docstring"""





  if len(__a		) <= 1:
    return input_list
  lowerCamelCase__:      Any			  =list(__a		)

  # iteration for two-way merging
  lowerCamelCase__:      str			  =2
  while p <= len(__a		):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0	,    len(__a		)	,    __a		):
      lowerCamelCase__:      Dict			  =i
      lowerCamelCase__:      List[str]			  =i + p - 1
      lowerCamelCase__:      int			  =(low + high + 1) // 2
      lowerCamelCase__:      Optional[int]			  =merge(__a	,    __a	,    __a	,    __a		)
    # final merge of last two parts
    if p * 2 >= len(__a		):
      lowerCamelCase__:      List[Any]			  =i
      lowerCamelCase__:      Optional[int]			  =merge(__a	,    0	,    __a	,    len(__a		) - 1		)
      break
    p *= 2

  return input_list


if __name__ == "__main__":
     __A									=				input("Enter numbers separated by a comma:\n").strip()
     if user_input == "":
          __A									=				[]
     else:
          __A									=				[int(item.strip()) for item in user_input.split(",")]
     print(iter_merge_sort(unsorted))



from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class    SCREAMING_SNAKE_CASE__     (	nn.Module					):





	'''simple docstring'''



	def __init__(  self	:    Optional[int]			,  lowercase	:    int = 16			,  lowercase	:    int = 88			,  lowercase	:    Optional[int] = None			,  lowercase	:    int = 1			,  lowercase	:    float = 0.0			,  lowercase	:    int = 32			,  lowercase	:    Optional[int] = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[int] = None			,  lowercase	:    Optional[int] = None			,  lowercase	:    str = "geglu"			,  lowercase	:    Optional[int] = None			,  ):


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(
				    [
				        TransformeraDModel(
				            num_attention_heads=lowercase			,  attention_head_dim=lowercase			,  in_channels=lowercase			,  num_layers=lowercase			,  dropout=lowercase			,  norm_num_groups=lowercase			,  cross_attention_dim=lowercase			,  attention_bias=lowercase			,  sample_size=lowercase			,  num_vector_embeds=lowercase			,  activation_fn=lowercase			,  num_embeds_ada_norm=lowercase			,  )
				        for _ in range(2	)
				    ]	)

				# Variables that can be set by a pipeline:

				# The ratio of transformer1 to transformer2's output states to be combined during inference
				_snake_case		=			0.5

				# The shape of `encoder_hidden_states` is expected to be
				# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
				_snake_case		=			[77, 257]

				# Which transformer to use to encode which condition.
				# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
				_snake_case		=			[1, 0]





	def  A							(  self	:    Optional[int]			,  lowercase	:    Optional[int]			,  lowercase	:    List[Any]			,  lowercase	:    List[str]=None			,  lowercase	:    Tuple=None			,  lowercase	:    Dict=None			,  lowercase	:    bool = True			,  ):


				'''simple docstring'''


				_snake_case		=			hidden_states

				_snake_case		=			[]
				_snake_case		=			0
				# attention_mask is not used yet
				for i in range(2	):
							# for each of the two transformers, pass the corresponding condition tokens
							_snake_case		=			encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
							_snake_case		=			self.transformer_index_for_condition[i]
							_snake_case		=			self.transformers[transformer_index](
							    lowercase			,  encoder_hidden_states=lowercase			,  timestep=lowercase			,  cross_attention_kwargs=lowercase			,  return_dict=lowercase			,  )[0]
							encoded_states.append(encoded_state - input_states	)
							tokens_start += self.condition_lengths[i]

				_snake_case		=			encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
				_snake_case		=			output_states + input_states

				if not return_dict:
							return (output_states,)

				return TransformeraDModelOutput(sample=lowercase	)