



"""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 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					42




class 			snake_case_						(						a_     ,a_				):






							@register_to_config
							def __init__(   self			,     a_ = 6_5_5_3_6			,     a_ = None			,     a_ = 2			,     a_ = 2			,     a_ = 0			,     a_ = "fourier"			,     a_ = True			,     a_ = False			,     a_ = 0.0			,     a_ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")			,     a_ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")			,     a_ = "UNetMidBlock1D"			,     a_ = None			,     a_ = (3_2, 3_2, 6_4)			,     a_ = None			,     a_ = 8			,     a_ = 1			,     a_ = False			,     ):
									super().__init__()
									a_     :      Union[str, Any] 						=     sample_size

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

									if use_timestep_embedding:
											a_     :      Dict 						=     block_out_channels[0] * 4
											a_     :      str 						=     TimestepEmbedding(
											    in_channels=a_			,     time_embed_dim=a_			,     act_fn=a_			,     out_dim=block_out_channels[0]			,     )

									a_     :      int 						=     nn.ModuleList([]      )
									a_     :      Dict 						=     None
									a_     :      Optional[Any] 						=     nn.ModuleList([]      )
									a_     :      List[Any] 						=     None

									# down
									a_     :      Dict 						=     in_channels
									for i, down_block_type in enumerate(a_      ):
											a_     :      Optional[int] 						=     output_channel
											a_     :      Optional[int] 						=     block_out_channels[i]

											if i == 0:
													input_channel += extra_in_channels

											a_     :      List[str] 						=     i == len(a_      ) - 1

											a_     :      Union[str, Any] 						=     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
									a_     :      Tuple 						=     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
									a_     :      List[str] 						=     list(reversed(a_      )      )
									a_     :      str 						=     reversed_block_out_channels[0]
									if out_block_type is None:
											a_     :      Union[str, Any] 						=     out_channels
									else:
											a_     :      Optional[Any] 						=     block_out_channels[0]

									for i, up_block_type in enumerate(a_      ):
											a_     :      int 						=     output_channel
											a_     :      Tuple 						=     (
											    reversed_block_out_channels[i + 1] if i < len(a_      ) - 1 else final_upsample_channels
											)

											a_     :      Tuple 						=     i == len(a_      ) - 1

											a_     :      List[str] 						=     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_      )
											a_     :      Any 						=     output_channel

									# out
									a_     :      Any 						=     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4			,     3_2      )
									a_     :      int 						=     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 snake_case_  (   self			,     a_			,     a_			,     a_ = True			,     ):
									a_     :      Dict 						=     timestep
									if not torch.is_tensor(a_      ):
											a_     :      List[Any] 						=     torch.tensor([timesteps]			,     dtype=torch.long			,     device=sample.device      )
									elif torch.is_tensor(a_      ) and len(timesteps.shape      ) == 0:
											a_     :      str 						=     timesteps[None].to(sample.device      )

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

									# 2. down
									a_     :      List[Any] 						=     ()
									for downsample_block in self.down_blocks:
											a_						,				a_     :      Dict 						=     downsample_block(hidden_states=a_			,     temb=a_      )
											down_block_res_samples += res_samples

									# 3. mid
									if self.mid_block:
											a_     :      List[Any] 						=     self.mid_block(a_			,     a_      )

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

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

									if not return_dict:
											return (sample,)

									return UNetaDOutput(sample=a_      )




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)