





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  _UpperCAmelCase				(		_SCREAMING_SNAKE_CASE  ):
      a    							=				42






class  _UpperCAmelCase				(		_SCREAMING_SNAKE_CASE      ,							_SCREAMING_SNAKE_CASE  ):
      @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   ,							):
         super().__init__()
         A_  :					Optional[int]         =						num_attention_heads
         A_  :					Optional[int]         =						attention_head_dim
         A_  :					int         =						num_attention_heads * attention_head_dim
         A_  :					int         =						additional_embeddings

         A_  :					int         =						time_embed_dim or inner_dim
         A_  :					str         =						embedding_proj_dim or embedding_dim
         A_  :					str         =						clip_embed_dim or embedding_dim

         A_  :					Dict         =						Timesteps(a__   ,							a__   ,							0							)
         A_  :					Any         =						TimestepEmbedding(a__   ,							a__   ,							out_dim=a__   ,							act_fn=a__							)

         A_  :					List[Any]         =						nn.Linear(a__   ,							a__							)

         if embedding_proj_norm_type is None:
            A_  :					List[str]         =						None
         elif embedding_proj_norm_type == "layer":
            A_  :					Any         =						nn.LayerNorm(a__							)
         else:
            raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""							)

         A_  :					Any         =						nn.Linear(a__   ,							a__							)

         if encoder_hid_proj_type is None:
            A_  :					Dict         =						None
         elif encoder_hid_proj_type == "linear":
            A_  :					Union[str, Any]         =						nn.Linear(a__   ,							a__							)
         else:
            raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""							)

         A_  :					Tuple         =						nn.Parameter(torch.zeros(1   ,							num_embeddings + additional_embeddings   ,							a__							)							)

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

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

         if norm_in_type == "layer":
            A_  :					List[str]         =						nn.LayerNorm(a__							)
         elif norm_in_type is None:
            A_  :					Tuple         =						None
         else:
            raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""							)

         A_  :					List[Any]         =						nn.LayerNorm(a__							)

         A_  :					List[str]         =						nn.Linear(a__   ,							a__							)

         A_  :					Tuple         =						torch.full(
             [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]   ,							-10000.0							)
         causal_attention_mask.triu_(1							)
         A_  :					Dict         =						causal_attention_mask[None, ...]
         self.register_buffer("""causal_attention_mask"""   ,							a__   ,							persistent=a__							)

         A_  :					List[str]         =						nn.Parameter(torch.zeros(1   ,							a__							)							)
         A_  :					Optional[int]         =						nn.Parameter(torch.zeros(1   ,							a__							)							)
      @property
      # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
      def 		_lowerCamelCase   (		self							):
         A_  :					Union[str, Any]         =						{}

         def fn_recursive_add_processors(a__   ,							a__   ,							a__							):
            if hasattr(a__   ,							"""set_processor"""							):
               A_  :					List[str]         =						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__							):
         A_  :					Optional[Any]         =						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							):
         self.set_attn_processor(AttnProcessor()							)
      def 		_lowerCamelCase   (		self   ,							a__   ,							a__   ,							a__   ,							a__ = None   ,							a__ = None   ,							a__ = True   ,							):
         A_  :					Any         =						hidden_states.shape[0]

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

         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         A_  :					Union[str, Any]         =						timesteps * torch.ones(a__   ,							dtype=timesteps.dtype   ,							device=timesteps.device							)

         A_  :					str         =						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.
         A_  :					Dict         =						timesteps_projected.to(dtype=self.dtype							)
         A_  :					Union[str, Any]         =						self.time_embedding(a__							)

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

         A_  :					Optional[Any]         =						self.embedding_proj(a__							)
         if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
            A_  :					List[Any]         =						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"""							)

         A_  :					int         =						self.proj_in(a__							)

         A_  :					str         =						self.positional_embedding.to(hidden_states.dtype							)

         A_  :					Optional[int]         =						[]
         A_  :					Any         =						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:
            A_  :					Optional[Any]         =						proj_embeddings[:, None, :]

         if len(hidden_states.shape							) == 2:
            A_  :					Union[str, Any]         =						hidden_states[:, None, :]

         A_  :					Dict         =						additional_embeds + [
             proj_embeddings,
             time_embeddings[:, None, :],
             hidden_states,
         ]

         if self.prd_embedding is not None:
            A_  :					Optional[int]         =						self.prd_embedding.to(hidden_states.dtype							).expand(a__   ,							-1   ,							-1							)
            additional_embeds.append(a__							)

         A_  :					str         =						torch.cat(
             a__   ,							dim=1   ,							)

         # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
         A_  :					List[Any]         =						additional_embeddings_len + proj_embeddings.shape[1] + 1
         if positional_embeddings.shape[1] < hidden_states.shape[1]:
            A_  :					str         =						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   ,							)

         A_  :					Optional[Any]         =						hidden_states + positional_embeddings

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

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

         for block in self.transformer_blocks:
            A_  :					List[Any]         =						block(a__   ,							attention_mask=a__							)

         A_  :					str         =						self.norm_out(a__							)

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

         A_  :					Dict         =						self.proj_to_clip_embeddings(a__							)

         if not return_dict:
            return (predicted_image_embedding,)

         return PriorTransformerOutput(predicted_image_embedding=a__							)






      def 		_lowerCamelCase   (		self   ,							a__							):
         A_  :					Union[str, Any]         =						(prior_latents * self.clip_std) + self.clip_mean
         return prior_latents










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A   :    str											= {
    "configuration_roc_bert": ["ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoCBertConfig"],
    "tokenization_roc_bert": ["RoCBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  pass

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Optional[int]											= [
      "ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoCBertForCausalLM",
      "RoCBertForMaskedLM",
      "RoCBertForMultipleChoice",
      "RoCBertForPreTraining",
      "RoCBertForQuestionAnswering",
      "RoCBertForSequenceClassification",
      "RoCBertForTokenClassification",
      "RoCBertLayer",
      "RoCBertModel",
      "RoCBertPreTrainedModel",
      "load_tf_weights_in_roc_bert",
  ]

if TYPE_CHECKING:
  from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
  from .tokenization_roc_bert import RoCBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    raise OptionalDependencyNotAvailable()

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roc_bert import (
        ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoCBertForCausalLM,
        RoCBertForMaskedLM,
        RoCBertForMultipleChoice,
        RoCBertForPreTraining,
        RoCBertForQuestionAnswering,
        RoCBertForSequenceClassification,
        RoCBertForTokenClassification,
        RoCBertLayer,
        RoCBertModel,
        RoCBertPreTrainedModel,
        load_tf_weights_in_roc_bert,
    )


else:
  import sys

  __A   :    Optional[int]											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



