


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 		__a      (				snake_case_			):
		_a	:  Any				=	42



class 		__a      (				snake_case_    ,      snake_case_			):
		@register_to_config
		def __init__(			self			,    _SCREAMING_SNAKE_CASE = 32			,    _SCREAMING_SNAKE_CASE = 64			,    _SCREAMING_SNAKE_CASE = 20			,    _SCREAMING_SNAKE_CASE = 768			,    _SCREAMING_SNAKE_CASE=77			,    _SCREAMING_SNAKE_CASE=4			,    _SCREAMING_SNAKE_CASE = 0.0			,    _SCREAMING_SNAKE_CASE = "silu"			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = "linear"			,    _SCREAMING_SNAKE_CASE = "prd"			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = None			,    ) ->       Optional[Any]:




									"""simple docstring"""





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

									_UpperCAmelCase 		=			time_embed_dim or inner_dim
									_UpperCAmelCase 		=			embedding_proj_dim or embedding_dim
									_UpperCAmelCase 		=			clip_embed_dim or embedding_dim

									_UpperCAmelCase 		=			Timesteps(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    0						)
									_UpperCAmelCase 		=			TimestepEmbedding(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    out_dim=_SCREAMING_SNAKE_CASE			,    act_fn=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			nn.Linear(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)

									if embedding_proj_norm_type is None:
																_UpperCAmelCase 		=			None
									elif embedding_proj_norm_type == "layer":
																_UpperCAmelCase 		=			nn.LayerNorm(_SCREAMING_SNAKE_CASE						)
									else:
																raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''						)

									_UpperCAmelCase 		=			nn.Linear(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)

									if encoder_hid_proj_type is None:
																_UpperCAmelCase 		=			None
									elif encoder_hid_proj_type == "linear":
																_UpperCAmelCase 		=			nn.Linear(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									else:
																raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''						)

									_UpperCAmelCase 		=			nn.Parameter(torch.zeros(1			,    num_embeddings + additional_embeddings			,    _SCREAMING_SNAKE_CASE						)						)

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

									_UpperCAmelCase 		=			nn.ModuleList(
									    [
									        BasicTransformerBlock(
									            _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    dropout=_SCREAMING_SNAKE_CASE			,    activation_fn='gelu'			,    attention_bias=_SCREAMING_SNAKE_CASE			,    )
									        for d in range(_SCREAMING_SNAKE_CASE						)
									    ]						)

									if norm_in_type == "layer":
																_UpperCAmelCase 		=			nn.LayerNorm(_SCREAMING_SNAKE_CASE						)
									elif norm_in_type is None:
																_UpperCAmelCase 		=			None
									else:
																raise ValueError(f'''Unsupported norm_in_type: {norm_in_type}.'''						)

									_UpperCAmelCase 		=			nn.LayerNorm(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			nn.Linear(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			torch.full(
									    [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]			,    -10000.0						)
									causal_attention_mask.triu_(1						)
									_UpperCAmelCase 		=			causal_attention_mask[None, ...]
									self.register_buffer('causal_attention_mask'			,    _SCREAMING_SNAKE_CASE			,    persistent=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			nn.Parameter(torch.zeros(1			,    _SCREAMING_SNAKE_CASE						)						)
									_UpperCAmelCase 		=			nn.Parameter(torch.zeros(1			,    _SCREAMING_SNAKE_CASE						)						)
		@property
		# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
		def 	UpperCAmelCase__				(			self						) ->       Dict[str, AttentionProcessor]:




									"""simple docstring"""





									_UpperCAmelCase 		=			{}

									def fn_recursive_add_processors(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						):
																if hasattr(_SCREAMING_SNAKE_CASE			,    'set_processor'						):
																							_UpperCAmelCase 		=			module.processor

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

																return processors

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

									return processors
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       Any:




									"""simple docstring"""





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

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

									def fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						):
																if hasattr(_SCREAMING_SNAKE_CASE			,    'set_processor'						):
																							if not isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						):
																														module.set_processor(_SCREAMING_SNAKE_CASE						)
																							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}'''			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)

									for name, module in self.named_children():
																fn_recursive_attn_processor(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       int:




									"""simple docstring"""





									self.set_attn_processor(AttnProcessor()						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = None			,    _SCREAMING_SNAKE_CASE = True			,    ) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			hidden_states.shape[0]

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

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

									_UpperCAmelCase 		=			self.time_proj(_SCREAMING_SNAKE_CASE						)

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

									if self.embedding_proj_norm is not None:
																_UpperCAmelCase 		=			self.embedding_proj_norm(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			self.embedding_proj(_SCREAMING_SNAKE_CASE						)
									if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
																_UpperCAmelCase 		=			self.encoder_hidden_states_proj(_SCREAMING_SNAKE_CASE						)
									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'						)

									_UpperCAmelCase 		=			self.proj_in(_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase 		=			[]
									_UpperCAmelCase 		=			0

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

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

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

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

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

									_UpperCAmelCase 		=			torch.cat(
									    _SCREAMING_SNAKE_CASE			,    dim=1			,    )

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

									_UpperCAmelCase 		=			hidden_states + positional_embeddings

									if attention_mask is not None:
																_UpperCAmelCase 		=			(1 - attention_mask.to(hidden_states.dtype						)) * -10000.0
																_UpperCAmelCase 		=			F.pad(_SCREAMING_SNAKE_CASE			,    (0, self.additional_embeddings)			,    value=0.0						)
																_UpperCAmelCase 		=			(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype						)
																_UpperCAmelCase 		=			attention_mask.repeat_interleave(self.config.num_attention_heads			,    dim=0						)

									if self.norm_in is not None:
																_UpperCAmelCase 		=			self.norm_in(_SCREAMING_SNAKE_CASE						)

									for block in self.transformer_blocks:
																_UpperCAmelCase 		=			block(_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			self.norm_out(_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase 		=			self.proj_to_clip_embeddings(_SCREAMING_SNAKE_CASE						)

									if not return_dict:
																return (predicted_image_embedding,)

									return PriorTransformerOutput(predicted_image_embedding=_SCREAMING_SNAKE_CASE						)






		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       Optional[Any]:




									"""simple docstring"""





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

from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def    __lowercase     (						):
 UpperCamelCase_					:						Optional[Any] =					HfArgumentParser(lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					parser.parse_args_into_dataclasses()[0]
 UpperCamelCase_					:						Dict =					TensorFlowBenchmark(args=lowerCamelCase	)
 try:
  UpperCamelCase_					:						Any =					parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  UpperCamelCase_					:						Any =					'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  UpperCamelCase_					:						Optional[int] =					' '.join(str(lowerCamelCase	).split(' '	)[:-1]	)
  UpperCamelCase_					:						Any =					''
  UpperCamelCase_					:						Any =					eval(str(lowerCamelCase	).split(' '	)[-1]	)
  UpperCamelCase_					:						List[Any] =					[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in TensorFlowBenchmark.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]	)
   else:
    wrong_args.append(lowerCamelCase	)
  if len(lowerCamelCase	) > 0:
   UpperCamelCase_					:						List[str] =					full_error_msg + begin_error_msg + str(lowerCamelCase	)
  raise ValueError(lowerCamelCase	)
 benchmark.run()


if __name__ == "__main__":
   main()



