
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,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)



@flax.struct.dataclass
class    a_ (			_a    ):
			a	:			jnp.ndarray
			a	:			jnp.ndarray







class    a_ (			nn.Module    ):
			a	:			int
			a	:			Tuple[int]					   =							(1_6, 3_2, 9_6, 2_5_6)
			a	:			jnp.dtype					   =							jnp.floataa






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					nn.Conv(
									    self.block_out_channels[0]				,			kernel_size=(3, 3)				,			padding=((1, 1), (1, 1))				,			dtype=self.dtype				,			)

									_lowercase					       =					[]
									for i in range(len(self.block_out_channels  ) - 1  ):
															_lowercase					       =					self.block_out_channels[i]
															_lowercase					       =					self.block_out_channels[i + 1]
															_lowercase					       =					nn.Conv(
															    __UpperCamelCase				,			kernel_size=(3, 3)				,			padding=((1, 1), (1, 1))				,			dtype=self.dtype				,			)
															blocks.append(__UpperCamelCase  )
															_lowercase					       =					nn.Conv(
															    __UpperCamelCase				,			kernel_size=(3, 3)				,			strides=(2, 2)				,			padding=((1, 1), (1, 1))				,			dtype=self.dtype				,			)
															blocks.append(__UpperCamelCase  )
									_lowercase					       =					blocks

									_lowercase					       =					nn.Conv(
									    self.conditioning_embedding_channels				,			kernel_size=(3, 3)				,			padding=((1, 1), (1, 1))				,			kernel_init=nn.initializers.zeros_init()				,			bias_init=nn.initializers.zeros_init()				,			dtype=self.dtype				,			)






			def __call__(      self				,			__UpperCamelCase  ):
									_lowercase					       =					self.conv_in(__UpperCamelCase  )
									_lowercase					       =					nn.silu(__UpperCamelCase  )

									for block in self.blocks:
															_lowercase					       =					block(__UpperCamelCase  )
															_lowercase					       =					nn.silu(__UpperCamelCase  )

									_lowercase					       =					self.conv_out(__UpperCamelCase  )

									return embedding







@flax_register_to_config
class    a_ (			nn.Module					,			_a					,			_a    ):
			a	:			int					   =							3_2
			a	:			int					   =							4
			a	:			Tuple[str]					   =							(
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "CrossAttnDownBlock2D",
			    "DownBlock2D",
			)
			a	:			Union[bool, Tuple[bool]]					   =							False
			a	:			Tuple[int]					   =							(3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
			a	:			int					   =							2
			a	:			Union[int, Tuple[int]]					   =							8
			a	:			Optional[Union[int, Tuple[int]]]					   =							None
			a	:			int					   =							1_2_8_0
			a	:			float					   =							0.0
			a	:			bool					   =							False
			a	:			jnp.dtype					   =							jnp.floataa
			a	:			bool					   =							True
			a	:			int					   =							0
			a	:			str					   =							"rgb"
			a	:			Tuple[int]					   =							(1_6, 3_2, 9_6, 2_5_6)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									# init input tensors
									_lowercase					       =					(1, self.in_channels, self.sample_size, self.sample_size)
									_lowercase					       =					jnp.zeros(__UpperCamelCase				,			dtype=jnp.floataa  )
									_lowercase					       =					jnp.ones((1,)				,			dtype=jnp.intaa  )
									_lowercase					       =					jnp.zeros((1, 1, self.cross_attention_dim)				,			dtype=jnp.floataa  )
									_lowercase					       =					(1, 3, self.sample_size * 8, self.sample_size * 8)
									_lowercase					       =					jnp.zeros(__UpperCamelCase				,			dtype=jnp.floataa  )

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

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.block_out_channels
									_lowercase					       =					block_out_channels[0] * 4

									# 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.
									_lowercase					       =					self.num_attention_heads or self.attention_head_dim

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

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

									_lowercase					       =					FlaxControlNetConditioningEmbedding(
									    conditioning_embedding_channels=block_out_channels[0]				,			block_out_channels=self.conditioning_embedding_out_channels				,			)

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

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

									# down
									_lowercase					       =					[]
									_lowercase					       =					[]

									_lowercase					       =					block_out_channels[0]

									_lowercase					       =					nn.Conv(
									    __UpperCamelCase				,			kernel_size=(1, 1)				,			padding="""VALID"""				,			kernel_init=nn.initializers.zeros_init()				,			bias_init=nn.initializers.zeros_init()				,			dtype=self.dtype				,			)
									controlnet_down_blocks.append(__UpperCamelCase  )

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

															if down_block_type == "CrossAttnDownBlock2D":
																					_lowercase					       =					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]				,			dtype=self.dtype				,			)
															else:
																					_lowercase					       =					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  )

															for _ in range(self.layers_per_block  ):
																					_lowercase					       =					nn.Conv(
																					    __UpperCamelCase				,			kernel_size=(1, 1)				,			padding="""VALID"""				,			kernel_init=nn.initializers.zeros_init()				,			bias_init=nn.initializers.zeros_init()				,			dtype=self.dtype				,			)
																					controlnet_down_blocks.append(__UpperCamelCase  )

															if not is_final_block:
																					_lowercase					       =					nn.Conv(
																					    __UpperCamelCase				,			kernel_size=(1, 1)				,			padding="""VALID"""				,			kernel_init=nn.initializers.zeros_init()				,			bias_init=nn.initializers.zeros_init()				,			dtype=self.dtype				,			)
																					controlnet_down_blocks.append(__UpperCamelCase  )

									_lowercase					       =					down_blocks
									_lowercase					       =					controlnet_down_blocks

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

									_lowercase					       =					nn.Conv(
									    __UpperCamelCase				,			kernel_size=(1, 1)				,			padding="""VALID"""				,			kernel_init=nn.initializers.zeros_init()				,			bias_init=nn.initializers.zeros_init()				,			dtype=self.dtype				,			)






			def __call__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase = 1.0				,			__UpperCamelCase = True				,			__UpperCamelCase = False				,			):
									_lowercase					       =					self.controlnet_conditioning_channel_order
									if channel_order == "bgr":
															_lowercase					       =					jnp.flip(__UpperCamelCase				,			axis=1  )

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

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

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

									_lowercase					       =					jnp.transpose(__UpperCamelCase				,			(0, 2, 3, 1)  )
									_lowercase					       =					self.controlnet_cond_embedding(__UpperCamelCase  )
									sample += controlnet_cond

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

									# 4. mid
									_lowercase					       =					self.mid_block(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			deterministic=not train  )

									# 5. contronet blocks
									_lowercase					       =					()
									for down_block_res_sample, controlnet_block in zip(__UpperCamelCase				,			self.controlnet_down_blocks  ):
															_lowercase					       =					controlnet_block(__UpperCamelCase  )
															controlnet_down_block_res_samples += (down_block_res_sample,)

									_lowercase					       =					controlnet_down_block_res_samples

									_lowercase					       =					self.controlnet_mid_block(__UpperCamelCase  )

									# 6. scaling
									_lowercase					       =					[sample * conditioning_scale for sample in down_block_res_samples]
									mid_block_res_sample *= conditioning_scale

									if not return_dict:
															return (down_block_res_samples, mid_block_res_sample)

									return FlaxControlNetOutput(
									    down_block_res_samples=__UpperCamelCase				,			mid_block_res_sample=__UpperCamelCase  )

from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[int]:
						return {key.lstrip("""-"""       ): value for key, value in zip(unknown_args[::2]		,   unknown_args[1::2]       )}




def 	UpperCamelCase__  (     )  ->     Dict:
						_lowercase					       =					ArgumentParser(
						    """HuggingFace Datasets CLI tool"""		,   usage="""datasets-cli <command> [<args>]"""		,   allow_abbrev=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					parser.add_subparsers(help="""datasets-cli command helpers"""       )
						set_verbosity_info()

						# Register commands
						ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )

						# Parse args
						_lowercase       ,						_lowercase					       =					parser.parse_known_args()
						if not hasattr(SCREAMING_SNAKE_CASE_		,   """func"""       ):
												parser.print_help()
												exit(1       )
						_lowercase					       =					parse_unknown_args(SCREAMING_SNAKE_CASE_       )

						# Run
						_lowercase					       =					args.func(SCREAMING_SNAKE_CASE_		,   **SCREAMING_SNAKE_CASE_       )
						service.run()


if __name__ == "__main__":
		main()