




"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class   UpperCAmelCase_    (			__SCREAMING_SNAKE_CASE		):
		UpperCamelCase							=42


class   UpperCAmelCase_    (			__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE		):
		@register_to_config
		def __init__(						self				,						UpperCamelCase_ = 6_55_36				,						UpperCamelCase_ = None				,						UpperCamelCase_ = 2				,						UpperCamelCase_ = 2				,						UpperCamelCase_ = 0				,						UpperCamelCase_ = "fourier"				,						UpperCamelCase_ = True				,						UpperCamelCase_ = False				,						UpperCamelCase_ = 0.0				,						UpperCamelCase_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")				,						UpperCamelCase_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")				,						UpperCamelCase_ = "UNetMidBlock1D"				,						UpperCamelCase_ = None				,						UpperCamelCase_ = (32, 32, 64)				,						UpperCamelCase_ = None				,						UpperCamelCase_ = 8				,						UpperCamelCase_ = 1				,						UpperCamelCase_ = False				,						)						->       Dict:
					super().__init__()
					__lowercase			:  Union[str, Any]								=       sample_size

					# time
					if time_embedding_type == "fourier":
								__lowercase			:  Any								=       GaussianFourierProjection(
								    embedding_size=8				,						set_W_to_weight=A_				,						log=A_				,						flip_sin_to_cos=A_       )
								__lowercase			:  int								=       2 * block_out_channels[0]
					elif time_embedding_type == "positional":
								__lowercase			:  Tuple								=       Timesteps(
								    block_out_channels[0]				,						flip_sin_to_cos=A_				,						downscale_freq_shift=A_       )
								__lowercase			:  Union[str, Any]								=       block_out_channels[0]

					if use_timestep_embedding:
								__lowercase			:  List[Any]								=       block_out_channels[0] * 4
								__lowercase			:  Union[str, Any]								=       TimestepEmbedding(
								    in_channels=A_				,						time_embed_dim=A_				,						act_fn=A_				,						out_dim=block_out_channels[0]				,						)

					__lowercase			:  Dict								=       nn.ModuleList([]       )
					__lowercase			:  Tuple								=       None
					__lowercase			:  Union[str, Any]								=       nn.ModuleList([]       )
					__lowercase			:  Union[str, Any]								=       None

					# down
					__lowercase			:  Any								=       in_channels
					for i, down_block_type in enumerate(A_       ):
								__lowercase			:  Union[str, Any]								=       output_channel
								__lowercase			:  Optional[Any]								=       block_out_channels[i]

								if i == 0:
											input_channel += extra_in_channels

								__lowercase			:  Optional[Any]								=       i == len(A_       ) - 1

								__lowercase			:  Dict								=       get_down_block(
								    A_				,						num_layers=A_				,						in_channels=A_				,						out_channels=A_				,						temb_channels=block_out_channels[0]				,						add_downsample=not is_final_block or downsample_each_block				,						)
								self.down_blocks.append(A_       )

					# mid
					__lowercase			:  Any								=       get_mid_block(
					    A_				,						in_channels=block_out_channels[-1]				,						mid_channels=block_out_channels[-1]				,						out_channels=block_out_channels[-1]				,						embed_dim=block_out_channels[0]				,						num_layers=A_				,						add_downsample=A_				,						)

					# up
					__lowercase			:  int								=       list(reversed(A_       )       )
					__lowercase			:  Dict								=       reversed_block_out_channels[0]
					if out_block_type is None:
								__lowercase			:  Union[str, Any]								=       out_channels
					else:
								__lowercase			:  str								=       block_out_channels[0]

					for i, up_block_type in enumerate(A_       ):
								__lowercase			:  Optional[Any]								=       output_channel
								__lowercase			:  Tuple								=       (
								    reversed_block_out_channels[i + 1] if i < len(A_       ) - 1 else final_upsample_channels
								)

								__lowercase			:  Optional[Any]								=       i == len(A_       ) - 1

								__lowercase			:  int								=       get_up_block(
								    A_				,						num_layers=A_				,						in_channels=A_				,						out_channels=A_				,						temb_channels=block_out_channels[0]				,						add_upsample=not is_final_block				,						)
								self.up_blocks.append(A_       )
								__lowercase			:  Optional[Any]								=       output_channel

					# out
					__lowercase			:  int								=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4				,						32       )
					__lowercase			:  List[Any]								=       get_out_block(
					    out_block_type=A_				,						num_groups_out=A_				,						embed_dim=block_out_channels[0]				,						out_channels=A_				,						act_fn=A_				,						fc_dim=block_out_channels[-1] // 4				,						)
		def 	_lowerCamelCase							(						self				,						UpperCamelCase_				,						UpperCamelCase_				,						UpperCamelCase_ = True				,						)						->       Optional[Any]:
					__lowercase			:  Any								=       timestep
					if not torch.is_tensor(A_       ):
								__lowercase			:  Dict								=       torch.tensor([timesteps]				,						dtype=torch.long				,						device=sample.device       )
					elif torch.is_tensor(A_       ) and len(timesteps.shape       ) == 0:
								__lowercase			:  List[Any]								=       timesteps[None].to(sample.device       )

					__lowercase			:  List[str]								=       self.time_proj(A_       )
					if self.config.use_timestep_embedding:
								__lowercase			:  Optional[int]								=       self.time_mlp(A_       )
					else:
								__lowercase			:  Optional[Any]								=       timestep_embed[..., None]
								__lowercase			:  List[Any]								=       timestep_embed.repeat([1, 1, sample.shape[2]]       ).to(sample.dtype       )
								__lowercase			:  List[str]								=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])       )

					# 2. down
					__lowercase			:  Union[str, Any]								=       ()
					for downsample_block in self.down_blocks:
								__lowercase			,__lowercase			:  int								=       downsample_block(hidden_states=A_				,						temb=A_       )
								down_block_res_samples += res_samples

					# 3. mid
					if self.mid_block:
								__lowercase			:  Optional[int]								=       self.mid_block(A_				,						A_       )

					# 4. up
					for i, upsample_block in enumerate(self.up_blocks       ):
								__lowercase			:  str								=       down_block_res_samples[-1:]
								__lowercase			:  Optional[Any]								=       down_block_res_samples[:-1]
								__lowercase			:  str								=       upsample_block(A_				,						res_hidden_states_tuple=A_				,						temb=A_       )

					# 5. post-process
					if self.out_block:
								__lowercase			:  Union[str, Any]								=       self.out_block(A_				,						A_       )

					if not return_dict:
								return (sample,)

					return UNetaDOutput(sample=A_       )










import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_A					:				Optional[int]				=			object()

# For specifying empty leaf dict `{}`
_A					:				Tuple				=			object()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			tuple((re.compile(x + '''$'''	) for x in qs)	)
    for i in range(len(lowerCAmelCase_	) - len(lowerCAmelCase_	) + 1	):
        SCREAMING_SNAKE_CASE__											=			[x.match(lowerCAmelCase_	) for x, y in zip(lowerCAmelCase_ ,							ks[i:]	)]
        if matches and all(lowerCAmelCase_	):
            return True
    return False


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    def replace(lowerCAmelCase_ ,							lowerCAmelCase_	):
        for rule, replacement in rules:
            if _match(lowerCAmelCase_ ,							lowerCAmelCase_	):
                return replacement
        return val

    return replace


def 							__snake_case							(    )				->						Optional[int]:
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        (("transformer", "wte", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("attention", "out_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("mlp", "c_fc", "bias"), P('''mp'''	)),
        (("mlp", "c_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]



def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			_get_partition_rules()
    SCREAMING_SNAKE_CASE__											=			_replacement_rules(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			{k: _unmatched for k in flatten_dict(lowerCAmelCase_	)}
    SCREAMING_SNAKE_CASE__											=			{k: replace(lowerCAmelCase_ ,							lowerCAmelCase_	) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(lowerCAmelCase_	)	)
