



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  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      jnp.ndarray




@flax_register_to_config
class  __snake_case      (   nn.Module			,SCREAMING_SNAKE_CASE			,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					3_2
							UpperCamelCase__				:      int         =					4
							UpperCamelCase__				:      int         =					4
							UpperCamelCase__				:      Tuple[str]         =					(
							    "CrossAttnDownBlock2D",
							    "CrossAttnDownBlock2D",
							    "CrossAttnDownBlock2D",
							    "DownBlock2D",
							)
							UpperCamelCase__				:      Tuple[str]         =					("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
							UpperCamelCase__				:      Union[bool, Tuple[bool]]         =					False
							UpperCamelCase__				:      Tuple[int]         =					(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
							UpperCamelCase__				:      int         =					2
							UpperCamelCase__				:      Union[int, Tuple[int]]         =					8
							UpperCamelCase__				:      Optional[Union[int, Tuple[int]]]         =					None
							UpperCamelCase__				:      int         =					1_2_8_0
							UpperCamelCase__				:      float         =					0.0
							UpperCamelCase__				:      bool         =					False
							UpperCamelCase__				:      jnp.dtype         =					jnp.floataa
							UpperCamelCase__				:      bool         =					True
							UpperCamelCase__				:      int         =					0
							UpperCamelCase__				:      bool         =					False


							def _a      (							self  ,  a_		):
											# init input tensors
											a__				      =		(1, self.in_channels, self.sample_size, self.sample_size)
											a__				      =		jnp.zeros(a_  ,  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(a_		)
											a__				      =		{"""params""": params_rng, """dropout""": dropout_rng}

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


							def _a      (							self		):
											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(a_  ,  dtype=self.dtype		)

											a__				      =		self.only_cross_attention
											if isinstance(a_  ,  a_		):
															a__				      =		(only_cross_attention,) * len(self.down_block_types		)

											if isinstance(a_  ,  a_		):
															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(a_		) - 1

															if down_block_type == "CrossAttnDownBlock2D":
																			a__				      =		FlaxCrossAttnDownBlockaD(
																			    in_channels=a_  ,  out_channels=a_  ,  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=a_  ,  out_channels=a_  ,  dropout=self.dropout  ,  num_layers=self.layers_per_block  ,  add_downsample=not is_final_block  ,  dtype=self.dtype  ,  )

															down_blocks.append(a_		)
											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(a_		)		)
											a__				      =		list(reversed(a_		)		)
											a__				      =		list(reversed(a_		)		)
											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(a_		) - 1		)]

															a__				      =		i == len(a_		) - 1

															if up_block_type == "CrossAttnUpBlock2D":
																			a__				      =		FlaxCrossAttnUpBlockaD(
																			    in_channels=a_  ,  out_channels=a_  ,  prev_output_channel=a_  ,  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=a_  ,  out_channels=a_  ,  prev_output_channel=a_  ,  num_layers=self.layers_per_block + 1  ,  add_upsample=not is_final_block  ,  dropout=self.dropout  ,  dtype=self.dtype  ,  )

															up_blocks.append(a_		)
															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  ,  a_  ,  a_  ,  a_  ,  a_=None  ,  a_=None  ,  a_ = True  ,  a_ = False  ,  ):
											# 1. time
											if not isinstance(a_  ,  jnp.ndarray		):
															a__				      =		jnp.array([timesteps]  ,  dtype=jnp.intaa		)
											elif isinstance(a_  ,  jnp.ndarray		) and len(timesteps.shape		) == 0:
															a__				      =		timesteps.astype(dtype=jnp.floataa		)
															a__				      =		jnp.expand_dims(a_  ,  0		)

											a__				      =		self.time_proj(a_		)
											a__				      =		self.time_embedding(a_		)

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

											# 3. down
											a__				      =		(sample,)
											for down_block in self.down_blocks:
															if isinstance(a_  ,  a_		):
																			a__      ,			a__				      =		down_block(a_  ,  a_  ,  a_  ,  deterministic=not train		)
															else:
																			a__      ,			a__				      =		down_block(a_  ,  a_  ,  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(
															    a_  ,  a_		):
																			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(a_  ,  a_  ,  a_  ,  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(a_  ,  a_		):
																			a__				      =		up_block(
																			    a_  ,  temb=a_  ,  encoder_hidden_states=a_  ,  res_hidden_states_tuple=a_  ,  deterministic=not train  ,  )
															else:
																			a__				      =		up_block(a_  ,  temb=a_  ,  res_hidden_states_tuple=a_  ,  deterministic=not train		)

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

											if not return_dict:
															return (sample,)

											return FlaxUNetaDConditionOutput(sample=a_		)






def 			A_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				return abs(__a		) if a == 0 else greatest_common_divisor(b % a ,		__a		)

def 			A_      (	__a     :				int ,		__a     :				int		):

				"""simple docstring"""
				while y:  # --> when y=0 then loop will terminate and return x as final GCD.
								a__      ,			a__				      =		y, x % y
				return abs(__a		)

def 			A_      (	):

				"""simple docstring"""
				try:
								a__				      =		input("""Enter two integers separated by comma (,): """		).split(""","""		)
								a__				      =		int(nums[0]		)
								a__				      =		int(nums[1]		)
								print(
								    F'''greatest_common_divisor({num_a}, {num_a}) = '''
								    F'''{greatest_common_divisor(__a ,		__a		)}'''		)
								print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(__a ,		__a		)}'''		)
				except (IndexError, UnboundLocalError, ValueError):
								print("""Wrong input"""		)


if __name__ == "__main__":
	main()

