





'''simple docstring'''




import torch
from torch import nn

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







class   A_	(  lowerCAmelCase_							,						lowerCAmelCase_			):




 @register_to_config
 def __init__(					self       :    Union[str, Any]	,			*,
     snake_case_       :    int = 4	,			snake_case_       :    int = 7_6_8	,			snake_case_       :    int	,			snake_case_       :    List[Any]	,			):
      super().__init__()

      _UpperCAmelCase     							=   nn.Parameter(torch.zeros(snake_case_    )    )

      # parameters for additional clip time embeddings
      _UpperCAmelCase     							=   nn.Linear(snake_case_	,			snake_case_    )
      _UpperCAmelCase     							=   nn.Linear(snake_case_	,			snake_case_    )

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




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

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

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

      return text_encoder_hidden_states, additive_clip_time_embeddings











'''simple docstring'''




from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




 def      lowercase	(					self       :    List[Any]    ):
      for p in range(self.N - 1	,			0	,			-1    ):
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


if __name__ == "__main__":
  from functools import reduce

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

  for index, value in test_updates.items():
    __SCREAMING_SNAKE_CASE     :str       =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()




