
"""simple docstring"""




from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

snake_case			       =		{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     {start}
					SCREAMING_SNAKE_CASE           =     [start]
					SCREAMING_SNAKE_CASE           =     Queue()
					queue.put(SCREAMING_SNAKE_CASE_      )
					while not queue.empty():
										SCREAMING_SNAKE_CASE           =     queue.get()
										for w in graph[v]:
															if w not in explored:
																				explored.add(SCREAMING_SNAKE_CASE_      )
																				result.append(SCREAMING_SNAKE_CASE_      )
																				queue.put(SCREAMING_SNAKE_CASE_      )
					return result

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     {start}
					SCREAMING_SNAKE_CASE           =     [start]
					SCREAMING_SNAKE_CASE           =     deque([start]      )
					while queue:
										SCREAMING_SNAKE_CASE           =     queue.popleft()
										for child in graph[v]:
															if child not in visited:
																				visited.add(SCREAMING_SNAKE_CASE_      )
																				result.append(SCREAMING_SNAKE_CASE_      )
																				queue.append(SCREAMING_SNAKE_CASE_      )
					return result

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     f'''from __main__ import G, {name}'''
					SCREAMING_SNAKE_CASE           =     1_0_0_0_0
					SCREAMING_SNAKE_CASE           =     timeit(f'''{name}(G, \'A\')''',					setup=SCREAMING_SNAKE_CASE_,					number=SCREAMING_SNAKE_CASE_      )
					print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function('breadth_first_search')
	benchmark_function('breadth_first_search_with_deque')
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds


"""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 					UpperCamelCase     (	__magic_name__				):

				"""simple docstring"""



				UpperCAmelCase_    :				torch.FloatTensor






class 					UpperCamelCase     (	__magic_name__  ,				__magic_name__				):

				"""simple docstring"""



				@register_to_config
				def __init__(    self		,     lowercase__ = 16		,     lowercase__ = 88		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = 1		,     lowercase__ = 0.0		,     lowercase__ = 32		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = "geglu"		,     lowercase__ = None		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = False		,     lowercase__ = "layer_norm"		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE           =     use_linear_projection
									SCREAMING_SNAKE_CASE           =     num_attention_heads
									SCREAMING_SNAKE_CASE           =     attention_head_dim
									SCREAMING_SNAKE_CASE           =     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
									SCREAMING_SNAKE_CASE           =     (in_channels is not None) and (patch_size is None)
									SCREAMING_SNAKE_CASE           =     num_vector_embeds is not None
									SCREAMING_SNAKE_CASE           =     in_channels is not None and patch_size is not None

									if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
														SCREAMING_SNAKE_CASE           =     (
														    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'		,     lowercase__		,     standard_warn=lowercase__      )
														SCREAMING_SNAKE_CASE           =     '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:
														SCREAMING_SNAKE_CASE           =     in_channels

														SCREAMING_SNAKE_CASE           =     torch.nn.GroupNorm(num_groups=lowercase__		,     num_channels=lowercase__		,     eps=1E-6		,     affine=lowercase__      )
														if use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     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"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     num_vector_embeds
														SCREAMING_SNAKE_CASE           =     self.height * self.width

														SCREAMING_SNAKE_CASE           =     ImagePositionalEmbeddings(
														    num_embed=lowercase__		,     embed_dim=lowercase__		,     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"

														SCREAMING_SNAKE_CASE           =     sample_size
														SCREAMING_SNAKE_CASE           =     sample_size

														SCREAMING_SNAKE_CASE           =     patch_size
														SCREAMING_SNAKE_CASE           =     PatchEmbed(
														    height=lowercase__		,     width=lowercase__		,     patch_size=lowercase__		,     in_channels=lowercase__		,     embed_dim=lowercase__		,     )

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

									# 4. Define output layers
									SCREAMING_SNAKE_CASE           =     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:
																			SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     nn.Convad(lowercase__		,     lowercase__		,     kernel_size=1		,     stride=1		,     padding=0      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     self.num_vector_embeds - 1      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     nn.LayerNorm(lowercase__		,     elementwise_affine=lowercase__		,     eps=1E-6      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     2 * inner_dim      )
														SCREAMING_SNAKE_CASE           =     nn.Linear(lowercase__		,     patch_size * patch_size * self.out_channels      )




				def 							A (    self		,     lowercase__		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = None		,     lowercase__ = True		,     )   ->   List[Any]:
									"""simple docstring"""







									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)
														SCREAMING_SNAKE_CASE           =     (1 - attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     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:
														SCREAMING_SNAKE_CASE           =     (1 - encoder_attention_mask.to(hidden_states.dtype      )) * -10_000.0
														SCREAMING_SNAKE_CASE           =     encoder_attention_mask.unsqueeze(1      )

									# 1. Input
									if self.is_input_continuous:
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     hidden_states.shape
														SCREAMING_SNAKE_CASE           =     hidden_states

														SCREAMING_SNAKE_CASE           =     self.norm(lowercase__      )
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     hidden_states.shape[1]
																			SCREAMING_SNAKE_CASE           =     hidden_states.permute(0		,     2		,     3		,     1      ).reshape(lowercase__		,     height * width		,     lowercase__      )
																			SCREAMING_SNAKE_CASE           =     self.proj_in(lowercase__      )
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.latent_image_embedding(lowercase__      )
									elif self.is_input_patches:
														SCREAMING_SNAKE_CASE           =     self.pos_embed(lowercase__      )

									# 2. Blocks
									for block in self.transformer_blocks:
														SCREAMING_SNAKE_CASE           =     block(
														    lowercase__		,     attention_mask=lowercase__		,     encoder_hidden_states=lowercase__		,     encoder_attention_mask=lowercase__		,     timestep=lowercase__		,     cross_attention_kwargs=lowercase__		,     class_labels=lowercase__		,     )

									# 3. Output
									if self.is_input_continuous:
														if not self.use_linear_projection:
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
														else:
																			SCREAMING_SNAKE_CASE           =     self.proj_out(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     hidden_states.reshape(lowercase__		,     lowercase__		,     lowercase__		,     lowercase__      ).permute(0		,     3		,     1		,     2      ).contiguous()

														SCREAMING_SNAKE_CASE           =     hidden_states + residual
									elif self.is_input_vectorized:
														SCREAMING_SNAKE_CASE           =     self.norm_out(lowercase__      )
														SCREAMING_SNAKE_CASE           =     self.out(lowercase__      )
														# (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
														SCREAMING_SNAKE_CASE           =     logits.permute(0		,     2		,     1      )

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

														# unpatchify
														SCREAMING_SNAKE_CASE           =     SCREAMING_SNAKE_CASE           =     int(hidden_states.shape[1] ** 0.5      )
														SCREAMING_SNAKE_CASE           =     hidden_states.reshape(
														    shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels)      )
														SCREAMING_SNAKE_CASE           =     torch.einsum('nhwpqc->nchpwq'		,     lowercase__      )
														SCREAMING_SNAKE_CASE           =     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=lowercase__      )
