





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        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       torch.FloatTensor




class        _lowerCAmelCase    (			UpperCAmelCase_							,    UpperCAmelCase_							):


  '''simple docstring'''







  @register_to_config
  def __init__(      self      :					str      ,				UpperCamelCase      :					int = 32      ,				UpperCamelCase      :					int = 64      ,				UpperCamelCase      :					int = 20      ,				UpperCamelCase      :					int = 7_68      ,				UpperCamelCase      :					Optional[int]=77      ,				UpperCamelCase      :					int=4      ,				UpperCamelCase      :					float = 0.0      ,				UpperCamelCase      :					str = "silu"      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = None      ,				UpperCamelCase      :					Optional[str] = "linear"      ,				UpperCamelCase      :					Optional[str] = "prd"      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				UpperCamelCase      :					Optional[int] = None      ,				):


    '''simple docstring'''





    super().__init__()
    _snake_case   :				str														=	num_attention_heads
    _snake_case   :				Optional[int]														=	attention_head_dim
    _snake_case   :				Any														=	num_attention_heads * attention_head_dim
    _snake_case   :				List[Any]														=	additional_embeddings

    _snake_case   :				List[str]														=	time_embed_dim or inner_dim
    _snake_case   :				int														=	embedding_proj_dim or embedding_dim
    _snake_case   :				List[Any]														=	clip_embed_dim or embedding_dim

    _snake_case   :				Optional[Any]														=	Timesteps(UpperCamelCase      ,				UpperCamelCase      ,				0							)
    _snake_case   :				List[Any]														=	TimestepEmbedding(UpperCamelCase      ,				UpperCamelCase      ,				out_dim=UpperCamelCase      ,				act_fn=UpperCamelCase							)

    _snake_case   :				Optional[int]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

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

    _snake_case   :				str														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    if encoder_hid_proj_type is None:
      _snake_case   :				Any														=	None
    elif encoder_hid_proj_type == "linear":
      _snake_case   :				Optional[int]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)
    else:
      raise ValueError(f"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""							)

    _snake_case   :				List[str]														=	nn.Parameter(torch.zeros(1      ,				num_embeddings + additional_embeddings      ,				UpperCamelCase							)							)

    if added_emb_type == "prd":
      _snake_case   :				str														=	nn.Parameter(torch.zeros(1      ,				1      ,				UpperCamelCase							)							)
    elif added_emb_type is None:
      _snake_case   :				Dict														=	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   :				Optional[int]														=	nn.ModuleList(
        [
            BasicTransformerBlock(
                UpperCamelCase      ,				UpperCamelCase      ,				UpperCamelCase      ,				dropout=UpperCamelCase      ,				activation_fn='gelu'      ,				attention_bias=UpperCamelCase      ,				)
            for d in range(UpperCamelCase							)
        ]							)

    if norm_in_type == "layer":
      _snake_case   :				Optional[int]														=	nn.LayerNorm(UpperCamelCase							)
    elif norm_in_type is None:
      _snake_case   :				Optional[Any]														=	None
    else:
      raise ValueError(f"""Unsupported norm_in_type: {norm_in_type}."""							)

    _snake_case   :				Optional[Any]														=	nn.LayerNorm(UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	nn.Linear(UpperCamelCase      ,				UpperCamelCase							)

    _snake_case   :				List[Any]														=	torch.full(
        [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]      ,				-1_00_00.0							)
    causal_attention_mask.triu_(1							)
    _snake_case   :				Optional[Any]														=	causal_attention_mask[None, ...]
    self.register_buffer('causal_attention_mask'      ,				UpperCamelCase      ,				persistent=UpperCamelCase							)

    _snake_case   :				str														=	nn.Parameter(torch.zeros(1      ,				UpperCamelCase							)							)
    _snake_case   :				List[str]														=	nn.Parameter(torch.zeros(1      ,				UpperCamelCase							)							)







  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	{}

    def fn_recursive_add_processors(UpperCamelCase      :					str      ,				UpperCamelCase      :					torch.nn.Module      ,				UpperCamelCase      :					Dict[str, AttentionProcessor]							):
      if hasattr(UpperCamelCase      ,				'set_processor'							):
        _snake_case   :				Tuple														=	module.processor

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

      return processors

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

    return processors







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Union[AttentionProcessor, Dict[str, AttentionProcessor]]							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	len(self.attn_processors.keys()							)

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

    def fn_recursive_attn_processor(UpperCamelCase      :					str      ,				UpperCamelCase      :					torch.nn.Module      ,				UpperCamelCase      :					Union[str, Any]							):
      if hasattr(UpperCamelCase      ,				'set_processor'							):
        if not isinstance(UpperCamelCase      ,				UpperCamelCase							):
          module.set_processor(UpperCamelCase							)
        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}"""      ,				UpperCamelCase      ,				UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    self.set_attn_processor(AttnProcessor()							)







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					Union[torch.Tensor, float, int]      ,				UpperCamelCase      :					torch.FloatTensor      ,				UpperCamelCase      :					Optional[torch.FloatTensor] = None      ,				UpperCamelCase      :					Optional[torch.BoolTensor] = None      ,				UpperCamelCase      :					bool = True      ,				):


    '''simple docstring'''





    _snake_case   :				Dict														=	hidden_states.shape[0]

    _snake_case   :				str														=	timestep
    if not torch.is_tensor(UpperCamelCase							):
      _snake_case   :				Dict														=	torch.tensor([timesteps]      ,				dtype=torch.long      ,				device=hidden_states.device							)
    elif torch.is_tensor(UpperCamelCase							) and len(timesteps.shape							) == 0:
      _snake_case   :				Tuple														=	timesteps[None].to(hidden_states.device							)

    # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
    _snake_case   :				Optional[int]														=	timesteps * torch.ones(UpperCamelCase      ,				dtype=timesteps.dtype      ,				device=timesteps.device							)

    _snake_case   :				Union[str, Any]														=	self.time_proj(UpperCamelCase							)

    # 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   :				Tuple														=	timesteps_projected.to(dtype=self.dtype							)
    _snake_case   :				List[Any]														=	self.time_embedding(UpperCamelCase							)

    if self.embedding_proj_norm is not None:
      _snake_case   :				Optional[Any]														=	self.embedding_proj_norm(UpperCamelCase							)

    _snake_case   :				Union[str, Any]														=	self.embedding_proj(UpperCamelCase							)
    if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
      _snake_case   :				Dict														=	self.encoder_hidden_states_proj(UpperCamelCase							)
    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   :				str														=	self.proj_in(UpperCamelCase							)

    _snake_case   :				int														=	self.positional_embedding.to(hidden_states.dtype							)

    _snake_case   :				Optional[int]														=	[]
    _snake_case   :				List[Any]														=	0

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

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

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

    _snake_case   :				str														=	additional_embeds + [
        proj_embeddings,
        time_embeddings[:, None, :],
        hidden_states,
    ]

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

    _snake_case   :				Optional[int]														=	torch.cat(
        UpperCamelCase      ,				dim=1      ,				)

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

    _snake_case   :				Optional[Any]														=	hidden_states + positional_embeddings

    if attention_mask is not None:
      _snake_case   :				Any														=	(1 - attention_mask.to(hidden_states.dtype							)) * -1_00_00.0
      _snake_case   :				Tuple														=	F.pad(UpperCamelCase      ,				(0, self.additional_embeddings)      ,				value=0.0							)
      _snake_case   :				int														=	(attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype							)
      _snake_case   :				str														=	attention_mask.repeat_interleave(self.config.num_attention_heads      ,				dim=0							)

    if self.norm_in is not None:
      _snake_case   :				Tuple														=	self.norm_in(UpperCamelCase							)

    for block in self.transformer_blocks:
      _snake_case   :				Any														=	block(UpperCamelCase      ,				attention_mask=UpperCamelCase							)

    _snake_case   :				Dict														=	self.norm_out(UpperCamelCase							)

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

    _snake_case   :				List[Any]														=	self.proj_to_clip_embeddings(UpperCamelCase							)

    if not return_dict:
      return (predicted_image_embedding,)

    return PriorTransformerOutput(predicted_image_embedding=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	(prior_latents * self.clip_std) + self.clip_mean
    return prior_latents











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase_						   =    {
    """vocab_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
        ),
        """google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
        """google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """google/realm-cc-news-pretrained-embedder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
        ),
        """google/realm-cc-news-pretrained-encoder""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-scorer""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
        ),
        """google/realm-cc-news-pretrained-openqa""": (
            """https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-openqa""": (
            """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-nq-reader""": (
            """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-openqa""": (
            """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
        ),
        """google/realm-orqa-wq-reader""": (
            """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase_						   =    {
    """google/realm-cc-news-pretrained-embedder""": 512,
    """google/realm-cc-news-pretrained-encoder""": 512,
    """google/realm-cc-news-pretrained-scorer""": 512,
    """google/realm-cc-news-pretrained-openqa""": 512,
    """google/realm-orqa-nq-openqa""": 512,
    """google/realm-orqa-nq-reader""": 512,
    """google/realm-orqa-wq-openqa""": 512,
    """google/realm-orqa-wq-reader""": 512,
}

lowerCAmelCase_						   =    {
    """google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
    """google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
    """google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=VOCAB_FILES_NAMES
  a_							:       List[str]      					=PRETRAINED_VOCAB_FILES_MAP
  a_							:       str      					=PRETRAINED_INIT_CONFIGURATION
  a_							:       Optional[Any]      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_							:       List[Any]      					=RealmTokenizer







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					List[Any]=None      ,				UpperCamelCase      :					Optional[Any]=True      ,				UpperCamelCase      :					Optional[Any]="[UNK]"      ,				UpperCamelCase      :					Any="[SEP]"      ,				UpperCamelCase      :					Optional[Any]="[PAD]"      ,				UpperCamelCase      :					Optional[int]="[CLS]"      ,				UpperCamelCase      :					Optional[Any]="[MASK]"      ,				UpperCamelCase      :					Dict=True      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Union[str, Any]      ,				):


    '''simple docstring'''





    super().__init__(
        UpperCamelCase      ,				tokenizer_file=UpperCamelCase      ,				do_lower_case=UpperCamelCase      ,				unk_token=UpperCamelCase      ,				sep_token=UpperCamelCase      ,				pad_token=UpperCamelCase      ,				cls_token=UpperCamelCase      ,				mask_token=UpperCamelCase      ,				tokenize_chinese_chars=UpperCamelCase      ,				strip_accents=UpperCamelCase      ,				**UpperCamelCase      ,				)

    _snake_case   :				int														=	json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
    if (
        normalizer_state.get('lowercase'      ,				UpperCamelCase							) != do_lower_case
        or normalizer_state.get('strip_accents'      ,				UpperCamelCase							) != strip_accents
        or normalizer_state.get('handle_chinese_chars'      ,				UpperCamelCase							) != tokenize_chinese_chars
    ):
      _snake_case   :				int														=	getattr(UpperCamelCase      ,				normalizer_state.pop('type'							)							)
      _snake_case   :				List[str]														=	do_lower_case
      _snake_case   :				List[Any]														=	strip_accents
      _snake_case   :				Dict														=	tokenize_chinese_chars
      _snake_case   :				Any														=	normalizer_class(**UpperCamelCase							)

    _snake_case   :				Optional[int]														=	do_lower_case







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	PaddingStrategy.MAX_LENGTH

    _snake_case   :				Any														=	text
    _snake_case   :				List[str]														=	kwargs.pop('text_pair'      ,				UpperCamelCase							)
    _snake_case   :				int														=	kwargs.pop('return_tensors'      ,				UpperCamelCase							)

    _snake_case   :				Optional[int]														=	{
        'input_ids': [],
        'attention_mask': [],
        'token_type_ids': [],
    }

    for idx, candidate_text in enumerate(UpperCamelCase							):
      if batch_text_pair is not None:
        _snake_case   :				List[Any]														=	batch_text_pair[idx]
      else:
        _snake_case   :				Optional[Any]														=	None

      _snake_case   :				Optional[int]														=	super().__call__(UpperCamelCase      ,				UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

      _snake_case   :				str														=	encoded_candidates.get('input_ids'							)
      _snake_case   :				Tuple														=	encoded_candidates.get('attention_mask'							)
      _snake_case   :				List[str]														=	encoded_candidates.get('token_type_ids'							)

      if encoded_input_ids is not None:
        output_data["input_ids"].append(UpperCamelCase							)
      if encoded_attention_mask is not None:
        output_data["attention_mask"].append(UpperCamelCase							)
      if encoded_token_type_ids is not None:
        output_data["token_type_ids"].append(UpperCamelCase							)

    _snake_case   :				str														=	{key: item for key, item in output_data.items() if len(UpperCamelCase							) != 0}

    return BatchEncoding(UpperCamelCase      ,				tensor_type=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					str      ,				UpperCamelCase      :					Optional[Any]      ,				UpperCamelCase      :					Union[str, Any]=None							):


    '''simple docstring'''





    _snake_case   :				Dict														=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

    return output







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					List[int]      ,				UpperCamelCase      :					Optional[List[int]] = None							):


    '''simple docstring'''





    _snake_case   :				int														=	[self.sep_token_id]
    _snake_case   :				Dict														=	[self.cls_token_id]
    if token_ids_a is None:
      return len(cls + token_ids_a + sep							) * [0]
    return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[str] = None							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	self._tokenizer.model.save(UpperCamelCase      ,				name=UpperCamelCase							)
    return tuple(UpperCamelCase							)




