import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					42
							A_						  =					0.0
							A_						  =					1
							A_						  =					1
							A_						  =					True
							A_						  =					False
							A_						  =					False
							A_						  =					False
							A_						  =					jnp.floataa

							def       __A (  self:   str   )    ->				str:
												_A								= []
												_A								= []

												for i in range(self.num_layers   ):
																	_A								= self.in_channels if i == 0 else self.out_channels

																	_A								= FlaxResnetBlockaD(
																	    in_channels=__A		,     out_channels=self.out_channels		,     dropout_prob=self.dropout		,     dtype=self.dtype		,     )
																	resnets.append(__A   )

																	_A								= FlaxTransformeraDModel(
																	    in_channels=self.out_channels		,     n_heads=self.num_attention_heads		,     d_head=self.out_channels // self.num_attention_heads		,     depth=1		,     use_linear_projection=self.use_linear_projection		,     only_cross_attention=self.only_cross_attention		,     use_memory_efficient_attention=self.use_memory_efficient_attention		,     dtype=self.dtype		,     )
																	attentions.append(__A   )

												_A								= resnets
												_A								= attentions

												if self.add_downsample:
																	_A								= FlaxDownsampleaD(self.out_channels		,     dtype=self.dtype   )



							def __call__(  self:   int		,     __A:   int		,     __A:   Any		,     __A:   Tuple		,     __A:   int=True   )    ->				Any:
												_A								= ()

												for resnet, attn in zip(self.resnets		,     self.attentions   ):
																	_A								= resnet(__A		,     __A		,     deterministic=__A   )
																	_A								= attn(__A		,     __A		,     deterministic=__A   )
																	output_states += (hidden_states,)

												if self.add_downsample:
																	_A								= self.downsamplers_a(__A   )
																	output_states += (hidden_states,)

												return hidden_states, output_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					42
							A_						  =					0.0
							A_						  =					1
							A_						  =					True
							A_						  =					jnp.floataa

							def       __A (  self:   List[Any]   )    ->				Optional[Any]:
												_A								= []

												for i in range(self.num_layers   ):
																	_A								= self.in_channels if i == 0 else self.out_channels

																	_A								= FlaxResnetBlockaD(
																	    in_channels=__A		,     out_channels=self.out_channels		,     dropout_prob=self.dropout		,     dtype=self.dtype		,     )
																	resnets.append(__A   )
												_A								= resnets

												if self.add_downsample:
																	_A								= FlaxDownsampleaD(self.out_channels		,     dtype=self.dtype   )



							def __call__(  self:   Tuple		,     __A:   Optional[Any]		,     __A:   Tuple		,     __A:   Tuple=True   )    ->				Optional[int]:
												_A								= ()

												for resnet in self.resnets:
																	_A								= resnet(__A		,     __A		,     deterministic=__A   )
																	output_states += (hidden_states,)

												if self.add_downsample:
																	_A								= self.downsamplers_a(__A   )
																	output_states += (hidden_states,)

												return hidden_states, output_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					42
							A_						  =					42
							A_						  =					0.0
							A_						  =					1
							A_						  =					1
							A_						  =					True
							A_						  =					False
							A_						  =					False
							A_						  =					False
							A_						  =					jnp.floataa

							def       __A (  self:   Optional[int]   )    ->				Any:
												_A								= []
												_A								= []

												for i in range(self.num_layers   ):
																	_A								= self.in_channels if (i == self.num_layers - 1) else self.out_channels
																	_A								= self.prev_output_channel if i == 0 else self.out_channels

																	_A								= FlaxResnetBlockaD(
																	    in_channels=resnet_in_channels + res_skip_channels		,     out_channels=self.out_channels		,     dropout_prob=self.dropout		,     dtype=self.dtype		,     )
																	resnets.append(__A   )

																	_A								= FlaxTransformeraDModel(
																	    in_channels=self.out_channels		,     n_heads=self.num_attention_heads		,     d_head=self.out_channels // self.num_attention_heads		,     depth=1		,     use_linear_projection=self.use_linear_projection		,     only_cross_attention=self.only_cross_attention		,     use_memory_efficient_attention=self.use_memory_efficient_attention		,     dtype=self.dtype		,     )
																	attentions.append(__A   )

												_A								= resnets
												_A								= attentions

												if self.add_upsample:
																	_A								= FlaxUpsampleaD(self.out_channels		,     dtype=self.dtype   )



							def __call__(  self:   Tuple		,     __A:   int		,     __A:   Union[str, Any]		,     __A:   Optional[int]		,     __A:   Union[str, Any]		,     __A:   int=True   )    ->				Optional[int]:
												for resnet, attn in zip(self.resnets		,     self.attentions   ):
																	# pop res hidden states
																	_A								= res_hidden_states_tuple[-1]
																	_A								= res_hidden_states_tuple[:-1]
																	_A								= jnp.concatenate((hidden_states, res_hidden_states)		,     axis=-1   )

																	_A								= resnet(__A		,     __A		,     deterministic=__A   )
																	_A								= attn(__A		,     __A		,     deterministic=__A   )

												if self.add_upsample:
																	_A								= self.upsamplers_a(__A   )

												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					42
							A_						  =					42
							A_						  =					0.0
							A_						  =					1
							A_						  =					True
							A_						  =					jnp.floataa

							def       __A (  self:   str   )    ->				Optional[Any]:
												_A								= []

												for i in range(self.num_layers   ):
																	_A								= self.in_channels if (i == self.num_layers - 1) else self.out_channels
																	_A								= self.prev_output_channel if i == 0 else self.out_channels

																	_A								= FlaxResnetBlockaD(
																	    in_channels=resnet_in_channels + res_skip_channels		,     out_channels=self.out_channels		,     dropout_prob=self.dropout		,     dtype=self.dtype		,     )
																	resnets.append(__A   )

												_A								= resnets

												if self.add_upsample:
																	_A								= FlaxUpsampleaD(self.out_channels		,     dtype=self.dtype   )



							def __call__(  self:   Dict		,     __A:   Optional[Any]		,     __A:   int		,     __A:   Dict		,     __A:   str=True   )    ->				Optional[int]:
												for resnet in self.resnets:
																	# pop res hidden states
																	_A								= res_hidden_states_tuple[-1]
																	_A								= res_hidden_states_tuple[:-1]
																	_A								= jnp.concatenate((hidden_states, res_hidden_states)		,     axis=-1   )

																	_A								= resnet(__A		,     __A		,     deterministic=__A   )

												if self.add_upsample:
																	_A								= self.upsamplers_a(__A   )

												return hidden_states





class 						SCREAMING_SNAKE_CASE				( nn.Module					):





							"""simple docstring"""

							A_						  =					42
							A_						  =					0.0
							A_						  =					1
							A_						  =					1
							A_						  =					False
							A_						  =					False
							A_						  =					jnp.floataa

							def       __A (  self:   Any   )    ->				Dict:
												# there is always at least one resnet
												_A								= [
												    FlaxResnetBlockaD(
												        in_channels=self.in_channels		,     out_channels=self.in_channels		,     dropout_prob=self.dropout		,     dtype=self.dtype		,     )
												]

												_A								= []

												for _ in range(self.num_layers   ):
																	_A								= FlaxTransformeraDModel(
																	    in_channels=self.in_channels		,     n_heads=self.num_attention_heads		,     d_head=self.in_channels // self.num_attention_heads		,     depth=1		,     use_linear_projection=self.use_linear_projection		,     use_memory_efficient_attention=self.use_memory_efficient_attention		,     dtype=self.dtype		,     )
																	attentions.append(__A   )

																	_A								= FlaxResnetBlockaD(
																	    in_channels=self.in_channels		,     out_channels=self.in_channels		,     dropout_prob=self.dropout		,     dtype=self.dtype		,     )
																	resnets.append(__A   )

												_A								= resnets
												_A								= attentions



							def __call__(  self:   Optional[Any]		,     __A:   str		,     __A:   int		,     __A:   Tuple		,     __A:   List[Any]=True   )    ->				Optional[int]:
												_A								= self.resnets[0](__A		,     __A   )
												for attn, resnet in zip(self.attentions		,     self.resnets[1:]   ):
																	_A								= attn(__A		,     __A		,     deterministic=__A   )
																	_A								= resnet(__A		,     __A		,     deterministic=__A   )

												return hidden_states











def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_A							,_A								= len(_lowercase ), len(grid[0] )
					if (
					    min(_lowercase					,			_lowercase ) < 0
					    or row == row_length
					    or col == col_length
					    or (row, col) in visit
					    or grid[row][col] == 1
					):
										return 0
					if row == row_length - 1 and col == col_length - 1:
										return 1

					visit.add((row, col) )

					_A								= 0
					count += depth_first_search(_lowercase					,			row + 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			row - 1					,			_lowercase					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col + 1					,			_lowercase )
					count += depth_first_search(_lowercase					,			_lowercase					,			col - 1					,			_lowercase )

					visit.remove((row, col) )
					return count


if __name__ == "__main__":
				import doctest

				doctest.testmod()



