
import torch
from torch import nn

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


class 			lowercase_		(      a__  ,      a__       ):


       @register_to_config
       def __init__(    self						,       *,
           __A = 4						,       __A = 768						,       __A						,       __A						,       )						->    List[Any]:
              super().__init__()

              SCREAMING_SNAKE_CASE_							:				str		=nn.Parameter(torch.zeros(_UpperCAmelCase				)				)

              # parameters for additional clip time embeddings
              SCREAMING_SNAKE_CASE_							:				List[str]		=nn.Linear(_UpperCAmelCase						,       _UpperCAmelCase				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=nn.Linear(_UpperCAmelCase						,       _UpperCAmelCase				)

              # parameters for encoder hidden states
              SCREAMING_SNAKE_CASE_							:				Dict		=clip_extra_context_tokens
              SCREAMING_SNAKE_CASE_							:				List[Any]		=nn.Linear(
                  _UpperCAmelCase						,       self.clip_extra_context_tokens * cross_attention_dim				)
              SCREAMING_SNAKE_CASE_							:				Dict		=nn.Linear(_UpperCAmelCase						,       _UpperCAmelCase				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=nn.LayerNorm(_UpperCAmelCase				)





       def 	_snake_case				(    self						,       *, __A						,       __A						,       __A						,       __A				)						->    Tuple:






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

              SCREAMING_SNAKE_CASE_							:				List[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, ...
              SCREAMING_SNAKE_CASE_							:				Any		=self.embedding_proj(_UpperCAmelCase				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.clip_image_embeddings_project_to_time_embeddings(_UpperCAmelCase				)
              SCREAMING_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"
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.clip_extra_context_tokens_proj(_UpperCAmelCase				)
              SCREAMING_SNAKE_CASE_							:				int		=clip_extra_context_tokens.reshape(_UpperCAmelCase						,       -1						,       self.clip_extra_context_tokens				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=clip_extra_context_tokens.permute(0						,       2						,       1				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.encoder_hidden_states_proj(_UpperCAmelCase				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.text_encoder_hidden_states_norm(_UpperCAmelCase				)
              SCREAMING_SNAKE_CASE_							:				Any		=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

import math


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					int						=			size
          # approximate the overall size of segment tree with given value
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          # create array to store lazy update
          __A		:					Optional[Any]						=			[0 for i in range(0		,      4 * size)]
          __A		:					str						=			[0 for i in range(0		,      4 * size)]  # flag for lazy update


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return idx * 2 + 1


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if left_element == right_element:
               __A		:					List[Any]						=			a[left_element - 1]
          else:
               __A		:					List[str]						=			(left_element + right_element) // 2
               self.build(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
               self.build(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase)
               __A		:					Any						=			max(
                   self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Optional[Any]						=			self.lazy[idx]
               __A		:					Optional[Any]						=			False
               if left_element != right_element:
                    __A		:					List[Any]						=			self.lazy[idx]
                    __A		:					Dict						=			self.lazy[idx]
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True

          if right_element < a or left_element > b:
               return True
          if left_element >= a and right_element <= b:
               __A		:					Optional[int]						=			val
               if left_element != right_element:
                    __A		:					Tuple						=			val
                    __A		:					Any						=			val
                    __A		:					Tuple						=			True
                    __A		:					Union[str, Any]						=			True
               return True
          __A		:					str						=			(left_element + right_element) // 2
          self.update(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          self.update(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					int						=			max(
              self.segment_tree[self.left(_UpperCAmelCase)]		,      self.segment_tree[self.right(_UpperCAmelCase)])
          return True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          if self.flag[idx] is True:
               __A		:					Union[str, Any]						=			self.lazy[idx]
               __A		:					List[str]						=			False
               if left_element != right_element:
                    __A		:					Union[str, Any]						=			self.lazy[idx]
                    __A		:					Optional[int]						=			self.lazy[idx]
                    __A		:					str						=			True
                    __A		:					Union[str, Any]						=			True
          if right_element < a or left_element > b:
               return -math.inf
          if left_element >= a and right_element <= b:
               return self.segment_tree[idx]
          __A		:					Any						=			(left_element + right_element) // 2
          __A		:					int						=			self.query(self.left(_UpperCAmelCase)		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					Union[str, Any]						=			self.query(self.right(_UpperCAmelCase)		,      mid + 1		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)
          return max(_UpperCAmelCase		,      _UpperCAmelCase)


     def __str__(		self):

          '''simple docstring'''






          return str([self.query(1		,      1		,      self.size		,      _UpperCAmelCase		,      _UpperCAmelCase) for i in range(1		,      self.size + 1)])


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
  lowercase__       : str         =       15
  lowercase__       : List[Any]         =       SegmentTree(size)
  segt.build(1, 1, size, A)
  print(segt.query(1, 1, size, 4, 6))
  print(segt.query(1, 1, size, 7, 11))
  print(segt.query(1, 1, size, 7, 12))
  segt.update(1, 1, size, 1, 3, 1_11)
  print(segt.query(1, 1, size, 1, 15))
  segt.update(1, 1, size, 7, 8, 2_35)
  print(segt)