




'''simple docstring'''


from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)

@flax.struct.dataclass
class     A					(	_a					):
							lowercase_           =       42



@flax_register_to_config
class     A					(	nn.Module  ,_a  ,_a					):
							lowercase_           =       32
							lowercase_           =       4
							lowercase_           =       4
							lowercase_           =       (
							    'CrossAttnDownBlock2D',
							    'CrossAttnDownBlock2D',
							    'CrossAttnDownBlock2D',
							    'DownBlock2D',
							)
							lowercase_           =       ('UpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'CrossAttnUpBlock2D')
							lowercase_           =       False
							lowercase_           =       (320, 640, 1280, 1280)
							lowercase_           =       2
							lowercase_           =       8
							lowercase_           =       None
							lowercase_           =       1280
							lowercase_           =       0.0
							lowercase_           =       False
							lowercase_           =       jnp.floataa
							lowercase_           =       True
							lowercase_           =       0
							lowercase_           =       False







							def      __lowerCAmelCase    (						self :						Any							,    lowerCAmelCase_ :						jax.random.KeyArray      )    ->							FrozenDict:


														"""simple docstring"""


														_a			  =       (1, self.in_channels, self.sample_size, self.sample_size)
														_a			  =       jnp.zeros(lowerCAmelCase_							,    dtype=jnp.floataa      )
														_a			  =       jnp.ones((1,)							,    dtype=jnp.intaa      )
														_a			  =       jnp.zeros((1, 1, self.cross_attention_dim)							,    dtype=jnp.floataa      )

														_a  ,							_a			  =       jax.random.split(lowerCAmelCase_      )
														_a			  =       {'''params''': params_rng, '''dropout''': dropout_rng}

														return self.init(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )["params"]







							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       self.block_out_channels
														_a			  =       block_out_channels[0] * 4

														if self.num_attention_heads is not None:
																					raise ValueError(
																					    '''At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.'''      )

														# If `num_attention_heads` is not defined (which is the case for most models)
														# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
														# The reason for this behavior is to correct for incorrectly named variables that were introduced
														# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
														# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
														# which is why we correct for the naming here.
														_a			  =       self.num_attention_heads or self.attention_head_dim

														# input
														_a			  =       nn.Conv(
														    block_out_channels[0]							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )

														# time
														_a			  =       FlaxTimesteps(
														    block_out_channels[0]							,    flip_sin_to_cos=self.flip_sin_to_cos							,    freq_shift=self.config.freq_shift      )
														_a			  =       FlaxTimestepEmbedding(lowerCAmelCase_							,    dtype=self.dtype      )

														_a			  =       self.only_cross_attention
														if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					_a			  =       (only_cross_attention,) * len(self.down_block_types      )

														if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																					_a			  =       (num_attention_heads,) * len(self.down_block_types      )

														# down
														_a			  =       []
														_a			  =       block_out_channels[0]
														for i, down_block_type in enumerate(self.down_block_types      ):
																					_a			  =       output_channel
																					_a			  =       block_out_channels[i]
																					_a			  =       i == len(lowerCAmelCase_      ) - 1

																					if down_block_type == "CrossAttnDownBlock2D":
																												_a			  =       FlaxCrossAttnDownBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    dropout=self.dropout							,    num_layers=self.layers_per_block							,    num_attention_heads=num_attention_heads[i]							,    add_downsample=not is_final_block							,    use_linear_projection=self.use_linear_projection							,    only_cross_attention=only_cross_attention[i]							,    use_memory_efficient_attention=self.use_memory_efficient_attention							,    dtype=self.dtype							,    )
																					else:
																												_a			  =       FlaxDownBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    dropout=self.dropout							,    num_layers=self.layers_per_block							,    add_downsample=not is_final_block							,    dtype=self.dtype							,    )

																					down_blocks.append(lowerCAmelCase_      )
														_a			  =       down_blocks

														# mid
														_a			  =       FlaxUNetMidBlockaDCrossAttn(
														    in_channels=block_out_channels[-1]							,    dropout=self.dropout							,    num_attention_heads=num_attention_heads[-1]							,    use_linear_projection=self.use_linear_projection							,    use_memory_efficient_attention=self.use_memory_efficient_attention							,    dtype=self.dtype							,    )

														# up
														_a			  =       []
														_a			  =       list(reversed(lowerCAmelCase_      )      )
														_a			  =       list(reversed(lowerCAmelCase_      )      )
														_a			  =       list(reversed(lowerCAmelCase_      )      )
														_a			  =       reversed_block_out_channels[0]
														for i, up_block_type in enumerate(self.up_block_types      ):
																					_a			  =       output_channel
																					_a			  =       reversed_block_out_channels[i]
																					_a			  =       reversed_block_out_channels[min(i + 1							,    len(lowerCAmelCase_      ) - 1      )]

																					_a			  =       i == len(lowerCAmelCase_      ) - 1

																					if up_block_type == "CrossAttnUpBlock2D":
																												_a			  =       FlaxCrossAttnUpBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    prev_output_channel=lowerCAmelCase_							,    num_layers=self.layers_per_block + 1							,    num_attention_heads=reversed_num_attention_heads[i]							,    add_upsample=not is_final_block							,    dropout=self.dropout							,    use_linear_projection=self.use_linear_projection							,    only_cross_attention=only_cross_attention[i]							,    use_memory_efficient_attention=self.use_memory_efficient_attention							,    dtype=self.dtype							,    )
																					else:
																												_a			  =       FlaxUpBlockaD(
																												    in_channels=lowerCAmelCase_							,    out_channels=lowerCAmelCase_							,    prev_output_channel=lowerCAmelCase_							,    num_layers=self.layers_per_block + 1							,    add_upsample=not is_final_block							,    dropout=self.dropout							,    dtype=self.dtype							,    )

																					up_blocks.append(lowerCAmelCase_      )
																					_a			  =       output_channel
														_a			  =       up_blocks

														# out
														_a			  =       nn.GroupNorm(num_groups=32							,    epsilon=1e-5      )
														_a			  =       nn.Conv(
														    self.out_channels							,    kernel_size=(3, 3)							,    strides=(1, 1)							,    padding=((1, 1), (1, 1))							,    dtype=self.dtype							,    )







							def __call__(						self :						Tuple							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Union[str, Any]=None							,    lowerCAmelCase_ :						Tuple=None							,    lowerCAmelCase_ :						bool = True							,    lowerCAmelCase_ :						bool = False							,    )    ->							Union[FlaxUNetaDConditionOutput, Tuple]:


														"""simple docstring"""


														if not isinstance(lowerCAmelCase_							,    jnp.ndarray      ):
																					_a			  =       jnp.array([timesteps]							,    dtype=jnp.intaa      )
														elif isinstance(lowerCAmelCase_							,    jnp.ndarray      ) and len(timesteps.shape      ) == 0:
																					_a			  =       timesteps.astype(dtype=jnp.floataa      )
																					_a			  =       jnp.expand_dims(lowerCAmelCase_							,    0      )

														_a			  =       self.time_proj(lowerCAmelCase_      )
														_a			  =       self.time_embedding(lowerCAmelCase_      )

														# 2. pre-process
														_a			  =       jnp.transpose(lowerCAmelCase_							,    (0, 2, 3, 1)      )
														_a			  =       self.conv_in(lowerCAmelCase_      )

														# 3. down
														_a			  =       (sample,)
														for down_block in self.down_blocks:
																					if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																												_a  ,							_a			  =       down_block(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    deterministic=not train      )
																					else:
																												_a  ,							_a			  =       down_block(lowerCAmelCase_							,    lowerCAmelCase_							,    deterministic=not train      )
																					down_block_res_samples += res_samples

														if down_block_additional_residuals is not None:
																					_a			  =       ()

																					for down_block_res_sample, down_block_additional_residual in zip(
																					    lowerCAmelCase_							,    lowerCAmelCase_      ):
																												down_block_res_sample += down_block_additional_residual
																												new_down_block_res_samples += (down_block_res_sample,)

																					_a			  =       new_down_block_res_samples

														# 4. mid
														_a			  =       self.mid_block(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    deterministic=not train      )

														if mid_block_additional_residual is not None:
																					sample += mid_block_additional_residual

														# 5. up
														for up_block in self.up_blocks:
																					_a			  =       down_block_res_samples[-(self.layers_per_block + 1) :]
																					_a			  =       down_block_res_samples[: -(self.layers_per_block + 1)]
																					if isinstance(lowerCAmelCase_							,    lowerCAmelCase_      ):
																												_a			  =       up_block(
																												    lowerCAmelCase_							,    temb=lowerCAmelCase_							,    encoder_hidden_states=lowerCAmelCase_							,    res_hidden_states_tuple=lowerCAmelCase_							,    deterministic=not train							,    )
																					else:
																												_a			  =       up_block(lowerCAmelCase_							,    temb=lowerCAmelCase_							,    res_hidden_states_tuple=lowerCAmelCase_							,    deterministic=not train      )

        # 6. post-process
														_a			  =       self.conv_norm_out(lowerCAmelCase_      )
														_a			  =       nn.silu(lowerCAmelCase_      )
														_a			  =       self.conv_out(lowerCAmelCase_      )
														_a			  =       jnp.transpose(lowerCAmelCase_							,    (0, 3, 1, 2)      )

														if not return_dict:
																					return (sample,)

														return FlaxUNetaDConditionOutput(sample=lowerCAmelCase_      )







'''simple docstring'''


_snake_case   :   Any     							=	{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       set()
							# keep track of all the paths to be checked
							_a			  =       [[start]]

							# return path if start is goal
							if start == goal:
														return [start]

							# keeps looping until all possible paths have been checked
							while queue:
														# pop the first path from the queue
														_a			  =       queue.pop(0   )
														# get the last node from the path
														_a			  =       path[-1]
														if node not in explored:
																					_a			  =       graph[node]
																					# go through all neighbour nodes, construct a new path and
																					# push it into the queue
																					for neighbour in neighbours:
																												_a			  =       list(UpperCamelCase   )
																												new_path.append(UpperCamelCase   )
																												queue.append(UpperCamelCase   )
																												# return path if neighbour is goal
																												if neighbour == goal:
																																			return new_path

            # mark node as explored
																					explored.add(UpperCamelCase   )

    # in case there's no path between the 2 nodes
							return []
def 	snake_case_					(UpperCamelCase :						dict				,		UpperCamelCase :						Optional[int]				,		UpperCamelCase :						Dict   ):





							'''simple docstring'''


							if not graph or start not in graph or target not in graph:
														return -1
							if start == target:
														return 0
							_a			  =       [start]
							_a			  =       set(UpperCamelCase   )
							# Keep tab on distances from `start` node.
							_a			  =       {start: 0, target: -1}
							while queue:
														_a			  =       queue.pop(0   )
														if node == target:
																					_a			  =       (
																					    dist[node] if dist[target] == -1 else min(dist[target]				,		dist[node]   )
																					)
														for adjacent in graph[node]:
																					if adjacent not in visited:
																												visited.add(UpperCamelCase   )
																												queue.append(UpperCamelCase   )
																												_a			  =       dist[node] + 1
							return dist[target]


if __name__ == "__main__":
				print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
				print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4

