
'''simple docstring'''



from string import ascii_lowercase, ascii_uppercase


def A				(__lowerCamelCase				:str		):
						if not sentence:
												return ""
						_lowerCAmelCase  						=			dict(zip(__lowerCamelCase			,     __lowerCamelCase		)		)
						return lower_to_upper.get(sentence[0]			,     sentence[0]		) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()


'''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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				jnp.ndarray






@flax_register_to_config
class   UpperCAmelCase_					(		nn.Module	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






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




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



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

												_lowerCAmelCase						,   _lowerCAmelCase  						=			jax.random.split(_lowercase				)
												_lowerCAmelCase  						=			{"""params""": params_rng, """dropout""": dropout_rng}

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.block_out_channels
												_lowerCAmelCase  						=			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.
												_lowerCAmelCase  						=			self.num_attention_heads or self.attention_head_dim

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

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

												_lowerCAmelCase  						=			self.only_cross_attention
												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			(only_cross_attention,) * len(self.down_block_types				)

												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			(num_attention_heads,) * len(self.down_block_types				)

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

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

																		down_blocks.append(_lowercase				)
												_lowerCAmelCase  						=			down_blocks

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

																		_lowerCAmelCase  						=			i == len(_lowercase				) - 1

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

																		up_blocks.append(_lowercase				)
																		_lowerCAmelCase  						=			output_channel
												_lowerCAmelCase  						=			up_blocks

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




						def __call__( self   ,       _lowercase   ,       _lowercase   ,       _lowercase   ,       _lowercase=None   ,       _lowercase=None   ,       _lowercase = True   ,       _lowercase = False   ,       ):

												"""simple docstring"""



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

												_lowerCAmelCase  						=			self.time_proj(_lowercase				)
												_lowerCAmelCase  						=			self.time_embedding(_lowercase				)

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

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

												if down_block_additional_residuals is not None:
																		_lowerCAmelCase  						=			()

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

																		_lowerCAmelCase  						=			new_down_block_res_samples

												# 4. mid
												_lowerCAmelCase  						=			self.mid_block(_lowercase   ,       _lowercase   ,       _lowercase   ,       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:
																		_lowerCAmelCase  						=			down_block_res_samples[-(self.layers_per_block + 1) :]
																		_lowerCAmelCase  						=			down_block_res_samples[: -(self.layers_per_block + 1)]
																		if isinstance(_lowercase   ,       _lowercase				):
																								_lowerCAmelCase  						=			up_block(
																								    _lowercase   ,       temb=_lowercase   ,       encoder_hidden_states=_lowercase   ,       res_hidden_states_tuple=_lowercase   ,       deterministic=not train   ,       )
																		else:
																								_lowerCAmelCase  						=			up_block(_lowercase   ,       temb=_lowercase   ,       res_hidden_states_tuple=_lowercase   ,       deterministic=not train				)

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

												if not return_dict:
																		return (sample,)

												return FlaxUNetaDConditionOutput(sample=_lowercase				)
