
"""simple docstring"""

from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin






@dataclass
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      42






class 			_lowercase  (							__UpperCAmelCase							,			__UpperCAmelCase						):




	@register_to_config
	def __init__(					self						,					UpperCAmelCase_ = 16						,					UpperCAmelCase_ = 88						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = 1						,					UpperCAmelCase_ = 0.0						,					UpperCAmelCase_ = 32						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "geglu"						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = "layer_norm"						,					UpperCAmelCase_ = True						,					)						->				Dict:
			super().__init__()
			lowerCamelCase	:     str            =       use_linear_projection
			lowerCamelCase	:     int            =       num_attention_heads
			lowerCamelCase	:     str            =       attention_head_dim
			lowerCamelCase	:     str            =       num_attention_heads * attention_head_dim

			# 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
			# Define whether input is continuous or discrete depending on configuration
			lowerCamelCase	:     Tuple            =       (in_channels is not None) and (patch_size is None)
			lowerCamelCase	:     List[Any]            =       num_vector_embeds is not None
			lowerCamelCase	:     List[str]            =       in_channels is not None and patch_size is not None

			if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
					lowerCamelCase	:     List[str]            =       (
					    F"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
					    ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
					    ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
					    ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
					    ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
					)
					deprecate('norm_type!=num_embeds_ada_norm'						,					'1.0.0'						,					_snake_case						,					standard_warn=_snake_case   )
					lowerCamelCase	:     str            =       'ada_norm'

			if self.is_input_continuous and self.is_input_vectorized:
					raise ValueError(
					    F"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
					    ' sure that either `in_channels` or `num_vector_embeds` is None.'   )
			elif self.is_input_vectorized and self.is_input_patches:
					raise ValueError(
					    F"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
					    ' sure that either `num_vector_embeds` or `num_patches` is None.'   )
			elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
					raise ValueError(
					    F"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
					    F""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."""   )

			# 2. Define input layers
			if self.is_input_continuous:
					lowerCamelCase	:     int            =       in_channels

					lowerCamelCase	:     Union[str, Any]            =       torch.nn.GroupNorm(num_groups=_snake_case						,					num_channels=_snake_case						,					eps=1E-6						,					affine=_snake_case   )
					if use_linear_projection:
							lowerCamelCase	:     str            =       nn.Linear(_snake_case						,					_snake_case   )
					else:
							lowerCamelCase	:     Optional[int]            =       nn.Convad(_snake_case						,					_snake_case						,					kernel_size=1						,					stride=1						,					padding=0   )
			elif self.is_input_vectorized:
					assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
					assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

					lowerCamelCase	:     Dict            =       sample_size
					lowerCamelCase	:     List[str]            =       sample_size
					lowerCamelCase	:     Tuple            =       num_vector_embeds
					lowerCamelCase	:     List[str]            =       self.height * self.width

					lowerCamelCase	:     Any            =       ImagePositionalEmbeddings(
					    num_embed=_snake_case						,					embed_dim=_snake_case						,					height=self.height						,					width=self.width   )
			elif self.is_input_patches:
					assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

					lowerCamelCase	:     str            =       sample_size
					lowerCamelCase	:     Optional[int]            =       sample_size

					lowerCamelCase	:     Tuple            =       patch_size
					lowerCamelCase	:     List[str]            =       PatchEmbed(
					    height=_snake_case						,					width=_snake_case						,					patch_size=_snake_case						,					in_channels=_snake_case						,					embed_dim=_snake_case						,					)

			# 3. Define transformers blocks
			lowerCamelCase	:     List[Any]            =       nn.ModuleList(
			    [
			        BasicTransformerBlock(
			            _snake_case						,					_snake_case						,					_snake_case						,					dropout=_snake_case						,					cross_attention_dim=_snake_case						,					activation_fn=_snake_case						,					num_embeds_ada_norm=_snake_case						,					attention_bias=_snake_case						,					only_cross_attention=_snake_case						,					upcast_attention=_snake_case						,					norm_type=_snake_case						,					norm_elementwise_affine=_snake_case						,					)
			        for d in range(_snake_case   )
			    ]   )

			# 4. Define output layers
			lowerCamelCase	:     Union[str, Any]            =       in_channels if out_channels is None else out_channels
			if self.is_input_continuous:
					# TODO: should use out_channels for continuous projections
					if use_linear_projection:
							lowerCamelCase	:     Optional[int]            =       nn.Linear(_snake_case						,					_snake_case   )
					else:
							lowerCamelCase	:     List[str]            =       nn.Convad(_snake_case						,					_snake_case						,					kernel_size=1						,					stride=1						,					padding=0   )
			elif self.is_input_vectorized:
					lowerCamelCase	:     Dict            =       nn.LayerNorm(_snake_case   )
					lowerCamelCase	:     Union[str, Any]            =       nn.Linear(_snake_case						,					self.num_vector_embeds - 1   )
			elif self.is_input_patches:
					lowerCamelCase	:     Any            =       nn.LayerNorm(_snake_case						,					elementwise_affine=_snake_case						,					eps=1E-6   )
					lowerCamelCase	:     List[Any]            =       nn.Linear(_snake_case						,					2 * inner_dim   )
					lowerCamelCase	:     Dict            =       nn.Linear(_snake_case						,					patch_size * patch_size * self.out_channels   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = True						,					)						->				List[str]:






			if attention_mask is not None and attention_mask.ndim == 2:
					# assume that mask is expressed as:
					#   (1 = keep,      0 = discard)
					# convert mask into a bias that can be added to attention scores:
					#       (keep = +0,     discard = -10000.0)
					lowerCamelCase	:     Dict            =       (1 - attention_mask.to(hidden_states.dtype   )) * -10000.0
					lowerCamelCase	:     Dict            =       attention_mask.unsqueeze(1   )

			# convert encoder_attention_mask to a bias the same way we do for attention_mask
			if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
					lowerCamelCase	:     str            =       (1 - encoder_attention_mask.to(hidden_states.dtype   )) * -10000.0
					lowerCamelCase	:     List[str]            =       encoder_attention_mask.unsqueeze(1   )

			# 1. Input
			if self.is_input_continuous:
					lowerCamelCase	:     str            =       hidden_states.shape
					lowerCamelCase	:     Union[str, Any]            =       hidden_states

					lowerCamelCase	:     List[Any]            =       self.norm(_snake_case   )
					if not self.use_linear_projection:
							lowerCamelCase	:     Optional[int]            =       self.proj_in(_snake_case   )
							lowerCamelCase	:     Dict            =       hidden_states.shape[1]
							lowerCamelCase	:     List[str]            =       hidden_states.permute(0						,					2						,					3						,					1   ).reshape(_snake_case						,					height * width						,					_snake_case   )
					else:
							lowerCamelCase	:     Dict            =       hidden_states.shape[1]
							lowerCamelCase	:     int            =       hidden_states.permute(0						,					2						,					3						,					1   ).reshape(_snake_case						,					height * width						,					_snake_case   )
							lowerCamelCase	:     Optional[Any]            =       self.proj_in(_snake_case   )
			elif self.is_input_vectorized:
					lowerCamelCase	:     Dict            =       self.latent_image_embedding(_snake_case   )
			elif self.is_input_patches:
					lowerCamelCase	:     Any            =       self.pos_embed(_snake_case   )

			# 2. Blocks
			for block in self.transformer_blocks:
					lowerCamelCase	:     Optional[int]            =       block(
					    _snake_case						,					attention_mask=_snake_case						,					encoder_hidden_states=_snake_case						,					encoder_attention_mask=_snake_case						,					timestep=_snake_case						,					cross_attention_kwargs=_snake_case						,					class_labels=_snake_case						,					)

			# 3. Output
			if self.is_input_continuous:
					if not self.use_linear_projection:
							lowerCamelCase	:     Optional[Any]            =       hidden_states.reshape(_snake_case						,					_snake_case						,					_snake_case						,					_snake_case   ).permute(0						,					3						,					1						,					2   ).contiguous()
							lowerCamelCase	:     List[str]            =       self.proj_out(_snake_case   )
					else:
							lowerCamelCase	:     Union[str, Any]            =       self.proj_out(_snake_case   )
							lowerCamelCase	:     str            =       hidden_states.reshape(_snake_case						,					_snake_case						,					_snake_case						,					_snake_case   ).permute(0						,					3						,					1						,					2   ).contiguous()

					lowerCamelCase	:     Tuple            =       hidden_states + residual
			elif self.is_input_vectorized:
					lowerCamelCase	:     List[Any]            =       self.norm_out(_snake_case   )
					lowerCamelCase	:     Tuple            =       self.out(_snake_case   )
					# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
					lowerCamelCase	:     Optional[Any]            =       logits.permute(0						,					2						,					1   )

					# log(p(x_0))
					lowerCamelCase	:     int            =       F.log_softmax(logits.double()						,					dim=1   ).float()
			elif self.is_input_patches:
					# TODO: cleanup!
					lowerCamelCase	:     List[Any]            =       self.transformer_blocks[0].norma.emb(
					    _snake_case						,					_snake_case						,					hidden_dtype=hidden_states.dtype   )
					lowerCamelCase	:     Optional[int]            =       self.proj_out_a(F.silu(_snake_case   )   ).chunk(2						,					dim=1   )
					lowerCamelCase	:     Tuple            =       self.norm_out(_snake_case   ) * (1 + scale[:, None]) + shift[:, None]
					lowerCamelCase	:     Dict            =       self.proj_out_a(_snake_case   )

					# unpatchify
					lowerCamelCase	:     Tuple            =       int(hidden_states.shape[1] ** 0.5   )
					lowerCamelCase	:     Optional[Any]            =       hidden_states.reshape(
					    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)   )
					lowerCamelCase	:     int            =       torch.einsum('nhwpqc->nchpwq'						,					_snake_case   )
					lowerCamelCase	:     Any            =       hidden_states.reshape(
					    shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size)   )

			if not return_dict:
					return (output,)

			return TransformeraDModelOutput(sample=_snake_case   )














import math




class 	lowerCamelCase__				:






      def __init__(self					:    int		,		_snake_case					:    Dict		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     len(_snake_case		)
       lowerCamelCase_							:    Optional[Any]							=     [0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1		,		0		,		self.N - 1		)






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    List[str]		)       ->   int:


       """simple docstring"""






       return idx * 2






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       return idx * 2 + 1






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    str		,		_snake_case					:    List[Any]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if l == r:
        lowerCamelCase_							:    List[Any]							=     A[l]
       else:
        lowerCamelCase_							:    int							=     (l + r) // 2
        self.build(self.left(_snake_case		)		,		_snake_case		,		_snake_case		)
        self.build(self.right(_snake_case		)		,		mid + 1		,		_snake_case		)
        lowerCamelCase_							:    Tuple							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    Tuple		,		_snake_case					:    Any		,		_snake_case					:    str		)       ->   Tuple:


       """simple docstring"""






       return self.update_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    List[str]		,		_snake_case					:    Tuple		,		_snake_case					:    Optional[int]		,		_snake_case					:    int		,		_snake_case					:    Tuple		,		_snake_case					:    List[str]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return True
       if l == r:
        lowerCamelCase_							:    Tuple							=     val
        return True
       lowerCamelCase_							:    Optional[int]							=     (l + r) // 2
       self.update_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       self.update_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)
       return True






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[int]		)       ->   str:


       """simple docstring"""






       return self.query_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    int		,		_snake_case					:    Optional[int]		,		_snake_case					:    Optional[Any]		)       ->   str:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return -math.inf
       if l >= a and r <= b:
        return self.st[idx]
       lowerCamelCase_							:    int							=     (l + r) // 2
       lowerCamelCase_							:    Optional[Any]							=     self.query_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     self.query_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		)
       return max(_snake_case		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    str		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     []
       for i in range(1		,		N + 1		):
        show_list += [self.query(_snake_case		,		_snake_case		)]
       print(_snake_case		)


if __name__ == "__main__":
       UpperCamelCase											=      [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
       UpperCamelCase											=      1_5
       UpperCamelCase											=      SegmentTree(A)
       print(segt.query(4, 6))
       print(segt.query(7, 1_1))
       print(segt.query(7, 1_2))
       segt.update(1, 3, 1_1_1)
       print(segt.query(1, 1_5))
       segt.update(7, 8, 2_3_5)
       segt.show_data()
