




'''simple docstring'''





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

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin




@dataclass
class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      torch.FloatTensor




class  _snake_case		(	lowercase_						,						lowercase_							):




			@register_to_config
			def __init__(					self				,							a__ = 32				,							a__ = 64				,							a__ = 20				,							a__ = 768				,							a__=77				,							a__=4				,							a__ = 0.0				,							a__ = "silu"				,							a__ = None				,							a__ = None				,							a__ = "linear"				,							a__ = "prd"				,							a__ = None				,							a__ = None				,							a__ = None				,							)				->				Tuple:

										'''simple docstring'''




										super().__init__()
										snake_case_						=      num_attention_heads
										snake_case_						=      attention_head_dim
										snake_case_						=      num_attention_heads * attention_head_dim
										snake_case_						=      additional_embeddings

										snake_case_						=      time_embed_dim or inner_dim
										snake_case_						=      embedding_proj_dim or embedding_dim
										snake_case_						=      clip_embed_dim or embedding_dim

										snake_case_						=      Timesteps(a__				,							a__				,							0							)
										snake_case_						=      TimestepEmbedding(a__				,							a__				,							out_dim=a__				,							act_fn=a__							)

										snake_case_						=      nn.Linear(a__				,							a__							)

										if embedding_proj_norm_type is None:
																	snake_case_						=      None
										elif embedding_proj_norm_type == "layer":
																	snake_case_						=      nn.LayerNorm(a__							)
										else:
																	raise ValueError(F'unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'							)

										snake_case_						=      nn.Linear(a__				,							a__							)

										if encoder_hid_proj_type is None:
																	snake_case_						=      None
										elif encoder_hid_proj_type == "linear":
																	snake_case_						=      nn.Linear(a__				,							a__							)
										else:
																	raise ValueError(F'unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'							)

										snake_case_						=      nn.Parameter(torch.zeros(1				,							num_embeddings + additional_embeddings				,							a__							)							)

										if added_emb_type == "prd":
																	snake_case_						=      nn.Parameter(torch.zeros(1				,							1				,							a__							)							)
										elif added_emb_type is None:
																	snake_case_						=      None
										else:
																	raise ValueError(
																	    F'`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `\'prd\'` or `None`.'							)

										snake_case_						=      nn.ModuleList(
										    [
										        BasicTransformerBlock(
										            a__				,							a__				,							a__				,							dropout=a__				,							activation_fn="gelu"				,							attention_bias=a__				,							)
										        for d in range(a__							)
										    ]							)

										if norm_in_type == "layer":
																	snake_case_						=      nn.LayerNorm(a__							)
										elif norm_in_type is None:
																	snake_case_						=      None
										else:
																	raise ValueError(F'Unsupported norm_in_type: {norm_in_type}.'							)

										snake_case_						=      nn.LayerNorm(a__							)

										snake_case_						=      nn.Linear(a__				,							a__							)

										snake_case_						=      torch.full(
										    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]				,							-1_0_0_0_0.0							)
										causal_attention_mask.triu_(1							)
										snake_case_						=      causal_attention_mask[None, ...]
										self.register_buffer("causal_attention_mask"				,							a__				,							persistent=a__							)

										snake_case_						=      nn.Parameter(torch.zeros(1				,							a__							)							)
										snake_case_						=      nn.Parameter(torch.zeros(1				,							a__							)							)




			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def    lowerCAmelCase__				(					self							)				->				Dict[str, AttentionProcessor]:

										'''simple docstring'''




										snake_case_						=      {}

										def fn_recursive_add_processors(a__				,							a__				,							a__							):
																	if hasattr(a__				,							"set_processor"							):
																								snake_case_						=      module.processor

																	for sub_name, child in module.named_children():
																								fn_recursive_add_processors(F'{name}.{sub_name}'				,							a__				,							a__							)

																	return processors

										for name, module in self.named_children():
																	fn_recursive_add_processors(a__				,							a__				,							a__							)

										return processors




			def    lowerCAmelCase__				(					self				,							a__							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      len(self.attn_processors.keys()							)

										if isinstance(a__				,							a__							) and len(a__							) != count:
																	raise ValueError(
																	    F'A dict of processors was passed, but the number of processors {len(a__							)} does not match the'
																	    F' number of attention layers: {count}. Please make sure to pass {count} processor classes.'							)

										def fn_recursive_attn_processor(a__				,							a__				,							a__							):
																	if hasattr(a__				,							"set_processor"							):
																								if not isinstance(a__				,							a__							):
																															module.set_processor(a__							)
																								else:
																															module.set_processor(processor.pop(F'{name}.processor'							)							)

																	for sub_name, child in module.named_children():
																								fn_recursive_attn_processor(F'{name}.{sub_name}'				,							a__				,							a__							)

										for name, module in self.named_children():
																	fn_recursive_attn_processor(a__				,							a__				,							a__							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										self.set_attn_processor(AttnProcessor()							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__				,							a__ = None				,							a__ = None				,							a__ = True				,							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      hidden_states.shape[0]

										snake_case_						=      timestep
										if not torch.is_tensor(a__							):
																	snake_case_						=      torch.tensor([timesteps]				,							dtype=torch.long				,							device=hidden_states.device							)
										elif torch.is_tensor(a__							) and len(timesteps.shape							) == 0:
																	snake_case_						=      timesteps[None].to(hidden_states.device							)

										# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
										snake_case_						=      timesteps * torch.ones(a__				,							dtype=timesteps.dtype				,							device=timesteps.device							)

										snake_case_						=      self.time_proj(a__							)

										# timesteps does not contain any weights and will always return f32 tensors
										# but time_embedding might be fp16, so we need to cast here.
										snake_case_						=      timesteps_projected.to(dtype=self.dtype							)
										snake_case_						=      self.time_embedding(a__							)

										if self.embedding_proj_norm is not None:
																	snake_case_						=      self.embedding_proj_norm(a__							)

										snake_case_						=      self.embedding_proj(a__							)
										if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																	snake_case_						=      self.encoder_hidden_states_proj(a__							)
										elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
																	raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set"							)

										snake_case_						=      self.proj_in(a__							)

										snake_case_						=      self.positional_embedding.to(hidden_states.dtype							)

										snake_case_						=      []
										snake_case_						=      0

										if encoder_hidden_states is not None:
																	additional_embeds.append(a__							)
																	additional_embeddings_len += encoder_hidden_states.shape[1]

										if len(proj_embeddings.shape							) == 2:
																	snake_case_						=      proj_embeddings[:, None, :]

										if len(hidden_states.shape							) == 2:
																	snake_case_						=      hidden_states[:, None, :]

										snake_case_						=      additional_embeds + [
										    proj_embeddings,
										    time_embeddings[:, None, :],
										    hidden_states,
										]

										if self.prd_embedding is not None:
																	snake_case_						=      self.prd_embedding.to(hidden_states.dtype							).expand(a__				,							-1				,							-1							)
																	additional_embeds.append(a__							)

										snake_case_						=      torch.cat(
										    a__				,							dim=1				,							)

										# Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
										snake_case_						=      additional_embeddings_len + proj_embeddings.shape[1] + 1
										if positional_embeddings.shape[1] < hidden_states.shape[1]:
																	snake_case_						=      F.pad(
																	    a__				,							(
																	        0,
																	        0,
																	        additional_embeddings_len,
																	        self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
																	    )				,							value=0.0				,							)

										snake_case_						=      hidden_states + positional_embeddings

										if attention_mask is not None:
																	snake_case_						=      (1 - attention_mask.to(hidden_states.dtype							)) * -1_0_0_0_0.0
																	snake_case_						=      F.pad(a__				,							(0, self.additional_embeddings)				,							value=0.0							)
																	snake_case_						=      (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
																	snake_case_						=      attention_mask.repeat_interleave(self.config.num_attention_heads				,							dim=0							)

										if self.norm_in is not None:
																	snake_case_						=      self.norm_in(a__							)

										for block in self.transformer_blocks:
																	snake_case_						=      block(a__				,							attention_mask=a__							)

										snake_case_						=      self.norm_out(a__							)

										if self.prd_embedding is not None:
																	snake_case_						=      hidden_states[:, -1]
										else:
																	snake_case_						=      hidden_states[:, additional_embeddings_len:]

										snake_case_						=      self.proj_to_clip_embeddings(a__							)

										if not return_dict:
																	return (predicted_image_embedding,)

										return PriorTransformerOutput(predicted_image_embedding=a__							)




			def    lowerCAmelCase__				(					self				,							a__							)				->				str:

										'''simple docstring'''




										snake_case_						=      (prior_latents * self.clip_std) + self.clip_mean
										return prior_latents







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 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    return {key.lstrip('-'      ): value for key, value in zip(unknown_args[::2]    ,			unknown_args[1::2]      )}
def 						_snake_case(    )							->      Dict:





    '''simple docstring'''






    A__          =   ArgumentParser(
        'HuggingFace Datasets CLI tool'    ,			usage='datasets-cli <command> [<args>]'    ,			allow_abbrev=SCREAMING_SNAKE_CASE__      )
    A__          =   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
    A__      ,						A__          =   parser.parse_known_args()
    if not hasattr(SCREAMING_SNAKE_CASE__    ,			'func'      ):
        parser.print_help()
        exit(1      )
    A__          =   parse_unknown_args(SCREAMING_SNAKE_CASE__      )

    # Run
    A__          =   args.func(SCREAMING_SNAKE_CASE__    ,			**SCREAMING_SNAKE_CASE__      )
    service.run()


if __name__ == "__main__":
     main()

