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   lowercase      (       a       ):
							lowercase__  :    jnp.ndarray






@flax_register_to_config
class   lowercase      (       nn.Module			,    a			,    a       ):
							lowercase__  :    int									=							32
							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]									=							(320, 640, 1_280, 1_280)
							lowercase__  :    int									=							2
							lowercase__  :    Union[int, Tuple[int]]									=							8
							lowercase__  :    Optional[Union[int, Tuple[int]]]									=							None
							lowercase__  :    int									=							1_280
							lowercase__  :    float									=							0.0
							lowercase__  :    bool									=							False
							lowercase__  :    jnp.dtype									=							jnp.floataa
							lowercase__  :    bool									=							True
							lowercase__  :    int									=							0
							lowercase__  :    bool									=							False




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   jax.random.KeyArray )   ->			FrozenDict:



												'''simple docstring'''

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

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

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




							def 		__snake_case(  self				:   Dict )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self.block_out_channels
												SCREAMING_SNAKE_CASE           =    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.
												SCREAMING_SNAKE_CASE           =    self.num_attention_heads or self.attention_head_dim

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

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

												SCREAMING_SNAKE_CASE           =    self.only_cross_attention
												if isinstance(_UpperCamelCase		,							_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    (only_cross_attention,) * len(self.down_block_types )

												if isinstance(_UpperCamelCase		,							_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    (num_attention_heads,) * len(self.down_block_types )

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

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

																	down_blocks.append(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    down_blocks

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

																	SCREAMING_SNAKE_CASE           =    i == len(_UpperCamelCase ) - 1

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

																	up_blocks.append(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    output_channel
												SCREAMING_SNAKE_CASE           =    up_blocks

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





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



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    self.time_proj(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    self.time_embedding(_UpperCamelCase )

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

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

												if down_block_additional_residuals is not None:
																	SCREAMING_SNAKE_CASE           =    ()

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

																	SCREAMING_SNAKE_CASE           =    new_down_block_res_samples

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

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

												if not return_dict:
																	return (sample,)

												return FlaxUNetaDConditionOutput(sample=_UpperCamelCase )





import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


class   lowercase      :
							lowercase__  :    str									=							None





@experimental
def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							Any					):
					if ParallelBackendConfig.backend_name is None:
										return _map_with_multiprocessing_pool(
										    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)

					return _map_with_joblib(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    num_proc if num_proc <= len(UpperCAmelCase__					) else len(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE           =    []  # We organize the splits ourselve (contiguous splits)
					for index in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) // num_proc
										SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					) % num_proc
										SCREAMING_SNAKE_CASE           =    div * index + min(UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    start + div + (1 if index < mod else 0)
										split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)					)

					if len(UpperCAmelCase__					) != sum(len(i[1]					) for i in split_kwds					):
										raise ValueError(
										    F"Error dividing inputs iterable among processes. "
										    F"Total number of objects {len(UpperCAmelCase__					)}, "
										    F"length: {sum(len(i[1]					) for i in split_kwds					)}"					)

					logger.info(
					    F"Spawning {num_proc} processes for {len(UpperCAmelCase__					)} objects in slices of {[len(i[1]					) for i in split_kwds]}"					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    None, None
					if not disable_tqdm:
										SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    (RLock(),), tqdm.set_lock
					with Pool(UpperCAmelCase__ ,	initargs=UpperCAmelCase__ ,	initializer=UpperCAmelCase__					) as pool:
										SCREAMING_SNAKE_CASE           =    pool.map(UpperCAmelCase__ ,	UpperCAmelCase__					)
					logger.info(F"Finished {num_proc} processes"					)
					SCREAMING_SNAKE_CASE           =    [obj for proc_res in mapped for obj in proc_res]
					logger.info(F"Unpacked {len(UpperCAmelCase__					)} objects"					)

					return mapped





def 		__lowerCamelCase							(UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							Any					):
					# progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
					# and it requires monkey-patching joblib internal classes which is subject to change
					import joblib

					with joblib.parallel_backend(ParallelBackendConfig.backend_name ,	n_jobs=UpperCAmelCase__					):
										return joblib.Parallel()(
										    joblib.delayed(UpperCAmelCase__					)((function, obj, types, None, True, None)					) for obj in iterable					)





@experimental
@contextlib.contextmanager
def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    backend_name

					if backend_name == "spark":
										from joblibspark import register_spark

										register_spark()

										# TODO: call create_cache_and_write_probe if "download" in steps
										# TODO: raise NotImplementedError when Dataset.map etc is called

					try:
										yield
					finally:
										SCREAMING_SNAKE_CASE           =    None




