

"""simple docstring"""






import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{
    "artists_file": "artists.json",
    "lyrics_file": "lyrics.json",
    "genres_file": "genres.json",
}

_lowerCAmelCase						=		{
    "artists_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json",
    },
    "genres_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json",
    },
    "lyrics_file": {
        "jukebox": "https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json",
    },
}

_lowerCAmelCase						=		{
    "jukebox": 5_1_2,
}







class 	__UpperCamelCase    (    a__						):
		_UpperCAmelCase          =		VOCAB_FILES_NAMES
		_UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
		_UpperCAmelCase          =		PRETRAINED_LYRIC_TOKENS_SIZES
		_UpperCAmelCase          =		["input_ids", "attention_mask"]

		def __init__(		self    ,_A    ,_A    ,_A    ,_A=["v3", "v2", "v2"]    ,_A=512    ,_A=5    ,_A="<|endoftext|>"    ,**_A    ,):
						'''simple docstring'''

						_lowerCAmelCase       :   Dict  =    AddedToken(UpperCAmelCase__    ,lstrip=UpperCAmelCase__    ,rstrip=UpperCAmelCase__      ) if isinstance(UpperCAmelCase__    ,UpperCAmelCase__      ) else unk_token
						super().__init__(
						    unk_token=UpperCAmelCase__    ,n_genres=UpperCAmelCase__    ,version=UpperCAmelCase__    ,max_n_lyric_tokens=UpperCAmelCase__    ,**UpperCAmelCase__    ,)
						_lowerCAmelCase       :   int  =    version
						_lowerCAmelCase       :   Optional[Any]  =    max_n_lyric_tokens
						_lowerCAmelCase       :   List[str]  =    n_genres

						with open(UpperCAmelCase__    ,encoding='utf-8'      ) as vocab_handle:
										_lowerCAmelCase       :   Dict  =    json.load(UpperCAmelCase__      )

						with open(UpperCAmelCase__    ,encoding='utf-8'      ) as vocab_handle:
										_lowerCAmelCase       :   Optional[int]  =    json.load(UpperCAmelCase__      )

						with open(UpperCAmelCase__    ,encoding='utf-8'      ) as vocab_handle:
										_lowerCAmelCase       :   Union[str, Any]  =    json.load(UpperCAmelCase__      )

						_lowerCAmelCase       :   Optional[Any]  =    r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
						# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
						if len(self.lyrics_encoder      ) == 79:
										_lowerCAmelCase       :   Optional[Any]  =    oov.replace(r'\-\''    ,r'\-+\''      )

						_lowerCAmelCase       :   str  =    regex.compile(UpperCAmelCase__      )
						_lowerCAmelCase       :   Dict  =    {v: k for k, v in self.artists_encoder.items()}
						_lowerCAmelCase       :   str  =    {v: k for k, v in self.genres_encoder.items()}
						_lowerCAmelCase       :   Dict  =    {v: k for k, v in self.lyrics_encoder.items()}

		@property
		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						return len(self.artists_encoder      ) + len(self.genres_encoder      ) + len(self.lyrics_encoder      )

		def __lowerCamelCase		(		self      ):
						'''simple docstring'''

						return dict(self.artists_encoder    ,self.genres_encoder    ,self.lyrics_encoder      )

		def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
						'''simple docstring'''

						_lowerCAmelCase       :   int  =    [self.artists_encoder.get(UpperCAmelCase__    ,0      ) for artist in list_artists]
						for genres in range(len(UpperCAmelCase__      )      ):
										_lowerCAmelCase       :   List[str]  =    [self.genres_encoder.get(UpperCAmelCase__    ,0      ) for genre in list_genres[genres]]
										_lowerCAmelCase       :   Any  =    list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]      ))

						_lowerCAmelCase       :   int  =    [[self.lyrics_encoder.get(UpperCAmelCase__    ,0      ) for character in list_lyrics[0]], [], []]
						return artists_id, list_genres, lyric_ids

		def __lowerCamelCase		(		self    ,_A      ):
						'''simple docstring'''

						return list(UpperCAmelCase__      )

		def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,**_A      ):
						'''simple docstring'''

						_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    self.prepare_for_tokenization(UpperCAmelCase__    ,UpperCAmelCase__    ,UpperCAmelCase__      )
						_lowerCAmelCase       :   str  =    self._tokenize(UpperCAmelCase__      )
						return artist, genre, lyrics

		def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = False      ):
						'''simple docstring'''

						for idx in range(len(self.version      )      ):
										if self.version[idx] == "v3":
														_lowerCAmelCase       :   str  =    artists[idx].lower()
														_lowerCAmelCase       :   Dict  =    [genres[idx].lower()]
										else:
														_lowerCAmelCase       :   List[Any]  =    self._normalize(artists[idx]      ) + '.v2'
														_lowerCAmelCase       :   List[Any]  =    [
														    self._normalize(UpperCAmelCase__      ) + '.v2' for genre in genres[idx].split('_'      )
														]  # split is for the full dictionary with combined genres

						if self.version[0] == "v2":
										_lowerCAmelCase       :   Optional[Any]  =    regex.compile(r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'      )
										_lowerCAmelCase       :   Tuple  =    'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
										_lowerCAmelCase       :   Optional[Any]  =    {vocab[index]: index + 1 for index in range(len(UpperCAmelCase__      )      )}
										_lowerCAmelCase       :   Optional[int]  =    0
										_lowerCAmelCase       :   Union[str, Any]  =    len(UpperCAmelCase__      ) + 1
										_lowerCAmelCase       :   int  =    self.vocab
										_lowerCAmelCase       :   Optional[Any]  =    {v: k for k, v in self.vocab.items()}
										_lowerCAmelCase       :   Dict  =    ''
						else:
										_lowerCAmelCase       :   int  =    regex.compile(r'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'      )

						_lowerCAmelCase       :   List[str]  =    self._run_strip_accents(UpperCAmelCase__      )
						_lowerCAmelCase       :   List[Any]  =    lyrics.replace('\\'    ,'\n'      )
						_lowerCAmelCase       :   List[Any]  =    self.out_of_vocab.sub(''    ,UpperCAmelCase__      ), [], []
						return artists, genres, lyrics

		def __lowerCamelCase		(		self    ,_A      ):
						'''simple docstring'''

						_lowerCAmelCase       :   str  =    unicodedata.normalize('NFD'    ,UpperCAmelCase__      )
						_lowerCAmelCase       :   Optional[Any]  =    []
						for char in text:
										_lowerCAmelCase       :   List[str]  =    unicodedata.category(UpperCAmelCase__      )
										if cat == "Mn":
														continue
										output.append(UpperCAmelCase__      )
						return "".join(UpperCAmelCase__      )

		def __lowerCamelCase		(		self    ,_A      ):
						'''simple docstring'''

						_lowerCAmelCase       :   int  =    (
						    [chr(UpperCAmelCase__      ) for i in range(ord('a'      )    ,ord('z'      ) + 1      )]
						    + [chr(UpperCAmelCase__      ) for i in range(ord('A'      )    ,ord('Z'      ) + 1      )]
						    + [chr(UpperCAmelCase__      ) for i in range(ord('0'      )    ,ord('9'      ) + 1      )]
						    + ['.']
						)
						_lowerCAmelCase       :   List[str]  =    frozenset(UpperCAmelCase__      )
						_lowerCAmelCase       :   List[str]  =    re.compile(r'_+'      )
						_lowerCAmelCase       :   int  =    ''.join([c if c in accepted else '_' for c in text.lower()]      )
						_lowerCAmelCase       :   int  =    pattern.sub('_'    ,UpperCAmelCase__      ).strip('_'      )
						return text

		def __lowerCamelCase		(		self    ,_A      ):
						'''simple docstring'''

						return " ".join(UpperCAmelCase__      )

		def __lowerCamelCase		(		self    ,_A    ,_A = None    ,_A = False      ):
						'''simple docstring'''

						if not isinstance(UpperCAmelCase__    ,UpperCAmelCase__      ):
										_lowerCAmelCase       :   List[str]  =    TensorType(UpperCAmelCase__      )

						# Get a function reference for the correct framework
						if tensor_type == TensorType.TENSORFLOW:
										if not is_tf_available():
														raise ImportError(
														    'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'      )
										import tensorflow as tf

										_lowerCAmelCase       :   Optional[Any]  =    tf.constant
										_lowerCAmelCase       :   Tuple  =    tf.is_tensor
						elif tensor_type == TensorType.PYTORCH:
										if not is_torch_available():
														raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'      )
										import torch

										_lowerCAmelCase       :   Optional[Any]  =    torch.tensor
										_lowerCAmelCase       :   List[Any]  =    torch.is_tensor
						elif tensor_type == TensorType.JAX:
										if not is_flax_available():
														raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'      )
										import jax.numpy as jnp  # noqa: F811

										_lowerCAmelCase       :   int  =    jnp.array
										_lowerCAmelCase       :   Dict  =    _is_jax
						else:
										_lowerCAmelCase       :   Optional[int]  =    np.asarray
										_lowerCAmelCase       :   Union[str, Any]  =    _is_numpy

						# Do the tensor conversion in batch

						try:
										if prepend_batch_axis:
														_lowerCAmelCase       :   Any  =    [inputs]

										if not is_tensor(UpperCAmelCase__      ):
														_lowerCAmelCase       :   Dict  =    as_tensor(UpperCAmelCase__      )
						except:  # noqa E722
										raise ValueError(
										    'Unable to create tensor, you should probably activate truncation and/or padding '
										    'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'      )

						return inputs

		def __call__(		self    ,_A    ,_A    ,_A=""    ,_A="pt"      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Optional[int]  =    [0, 0, 0]
						_lowerCAmelCase       :   List[str]  =    [artist] * len(self.version      )
						_lowerCAmelCase       :   str  =    [genres] * len(self.version      )

						_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   str  =    self.tokenize(UpperCAmelCase__    ,UpperCAmelCase__    ,UpperCAmelCase__      )
						_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    self._convert_token_to_id(UpperCAmelCase__    ,UpperCAmelCase__    ,UpperCAmelCase__      )

						_lowerCAmelCase       :   Dict  =    [-INFINITY] * len(full_tokens[-1]      )
						_lowerCAmelCase       :   Optional[int]  =    [
						    self.convert_to_tensors(
						        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,tensor_type=UpperCAmelCase__      )
						    for i in range(len(self.version      )      )
						]
						return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}      )

		def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
						'''simple docstring'''

						if not os.path.isdir(UpperCAmelCase__      ):
										logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
										return

						_lowerCAmelCase       :   Any  =    os.path.join(
						    UpperCAmelCase__    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']      )
						with open(UpperCAmelCase__    ,'w'    ,encoding='utf-8'      ) as f:
										f.write(json.dumps(self.artists_encoder    ,ensure_ascii=UpperCAmelCase__      )      )

						_lowerCAmelCase       :   int  =    os.path.join(
						    UpperCAmelCase__    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']      )
						with open(UpperCAmelCase__    ,'w'    ,encoding='utf-8'      ) as f:
										f.write(json.dumps(self.genres_encoder    ,ensure_ascii=UpperCAmelCase__      )      )

						_lowerCAmelCase       :   List[str]  =    os.path.join(
						    UpperCAmelCase__    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']      )
						with open(UpperCAmelCase__    ,'w'    ,encoding='utf-8'      ) as f:
										f.write(json.dumps(self.lyrics_encoder    ,ensure_ascii=UpperCAmelCase__      )      )

						return (artists_file, genres_file, lyrics_file)







		def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
						'''simple docstring'''

						_lowerCAmelCase       :   Optional[int]  =    self.artists_decoder.get(UpperCAmelCase__      )
						_lowerCAmelCase       :   Dict  =    [self.genres_decoder.get(UpperCAmelCase__      ) for genre in genres_index]
						_lowerCAmelCase       :   List[Any]  =    [self.lyrics_decoder.get(UpperCAmelCase__      ) for character in lyric_index]
						return artist, genres, lyrics








"""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 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		42







class 	__UpperCamelCase    (    a__							,							a__						):

  @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    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    attention_head_dim
      _lowerCAmelCase       :   Tuple  =    num_attention_heads * attention_head_dim
      _lowerCAmelCase       :   Optional[Any]  =    additional_embeddings

      _lowerCAmelCase       :   Union[str, Any]  =    time_embed_dim or inner_dim
      _lowerCAmelCase       :   Union[str, Any]  =    embedding_proj_dim or embedding_dim
      _lowerCAmelCase       :   Optional[int]  =    clip_embed_dim or embedding_dim

      _lowerCAmelCase       :   int  =    Timesteps(_A    ,_A    ,0      )
      _lowerCAmelCase       :   int  =    TimestepEmbedding(_A    ,_A    ,out_dim=_A    ,act_fn=_A      )

      _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )

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

      _lowerCAmelCase       :   Tuple  =    nn.Linear(_A    ,_A      )

      if encoder_hid_proj_type is None:
          _lowerCAmelCase       :   int  =    None
      elif encoder_hid_proj_type == "linear":
          _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )
      else:
          raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""      )

      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,num_embeddings + additional_embeddings    ,_A      )      )

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

      _lowerCAmelCase       :   List[Any]  =    nn.ModuleList(
          [
              BasicTransformerBlock(
                  _A    ,_A    ,_A    ,dropout=_A    ,activation_fn='gelu'    ,attention_bias=_A    ,)
              for d in range(_A      )
          ]      )

      if norm_in_type == "layer":
          _lowerCAmelCase       :   Any  =    nn.LayerNorm(_A      )
      elif norm_in_type is None:
          _lowerCAmelCase       :   Any  =    None
      else:
          raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""      )

      _lowerCAmelCase       :   Union[str, Any]  =    nn.LayerNorm(_A      )

      _lowerCAmelCase       :   int  =    nn.Linear(_A    ,_A      )

      _lowerCAmelCase       :   Any  =    torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,-1_0_0_0_0.0      )
      causal_attention_mask.triu_(1      )
      _lowerCAmelCase       :   Tuple  =    causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'    ,_A    ,persistent=_A      )

      _lowerCAmelCase       :   Tuple  =    nn.Parameter(torch.zeros(1    ,_A      )      )
      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,_A      )      )

  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    {}

      def fn_recursive_add_processors(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              _lowerCAmelCase       :   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      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    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      ):
      '''simple docstring'''

      self.set_attn_processor(AttnProcessor()      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = None    ,_A = None    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    hidden_states.shape[0]

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

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

      _lowerCAmelCase       :   Dict  =    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.
      _lowerCAmelCase       :   Any  =    timesteps_projected.to(dtype=self.dtype      )
      _lowerCAmelCase       :   Optional[Any]  =    self.time_embedding(_A      )

      if self.embedding_proj_norm is not None:
          _lowerCAmelCase       :   int  =    self.embedding_proj_norm(_A      )

      _lowerCAmelCase       :   str  =    self.embedding_proj(_A      )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          _lowerCAmelCase       :   str  =    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'      )

      _lowerCAmelCase       :   Any  =    self.proj_in(_A      )

      _lowerCAmelCase       :   Dict  =    self.positional_embedding.to(hidden_states.dtype      )

      _lowerCAmelCase       :   List[Any]  =    []
      _lowerCAmelCase       :   Optional[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:
          _lowerCAmelCase       :   int  =    proj_embeddings[:, None, :]

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

      _lowerCAmelCase       :   int  =    additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   List[str]  =    self.prd_embedding.to(hidden_states.dtype      ).expand(_A    ,-1    ,-1      )
          additional_embeds.append(_A      )

      _lowerCAmelCase       :   List[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
      _lowerCAmelCase       :   Tuple  =    additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
          _lowerCAmelCase       :   Any  =    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    ,)

      _lowerCAmelCase       :   int  =    hidden_states + positional_embeddings

      if attention_mask is not None:
          _lowerCAmelCase       :   Optional[Any]  =    (1 - attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
          _lowerCAmelCase       :   Union[str, Any]  =    F.pad(_A    ,(0, self.additional_embeddings)    ,value=0.0      )
          _lowerCAmelCase       :   Tuple  =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
          _lowerCAmelCase       :   Optional[Any]  =    attention_mask.repeat_interleave(self.config.num_attention_heads    ,dim=0      )

      if self.norm_in is not None:
          _lowerCAmelCase       :   Any  =    self.norm_in(_A      )

      for block in self.transformer_blocks:
          _lowerCAmelCase       :   int  =    block(_A    ,attention_mask=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.norm_out(_A      )

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states[:, -1]
      else:
          _lowerCAmelCase       :   Any  =    hidden_states[:, additional_embeddings_len:]

      _lowerCAmelCase       :   Optional[int]  =    self.proj_to_clip_embeddings(_A      )

      if not return_dict:
          return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents





