

"""simple docstring"""






import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


_lowerCAmelCase						=		logging.get_logger(__name__)

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
            ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
            ' instructions.'   )
        raise

    if not is_sharded:
        _lowerCAmelCase       :   Optional[Any]  =    os.path.abspath(_lowerCamelCase   )
        logger.info(f"""Loading PyTorch weights from {pt_path}"""   )

        _lowerCAmelCase       :   Dict  =    torch.load(_lowerCamelCase ,   map_location='cpu'   )
        logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."""   )

        _lowerCAmelCase       :   Optional[Any]  =    convert_pytorch_state_dict_to_flax(_lowerCamelCase ,   _lowerCamelCase   )
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        _lowerCAmelCase       :   List[str]  =    convert_pytorch_sharded_state_dict_to_flax(_lowerCamelCase ,   _lowerCamelCase   )
    return flax_state_dict

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    def is_key_or_prefix_key_in_dict(_lowerCamelCase   ) -> bool:
        return len(set(_lowerCamelCase   ) & {key, (model_prefix,) + key}   ) > 0

    # layer norm
    _lowerCAmelCase       :   int  =    pt_tuple_key[:-1] + ('scale',)
    if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCamelCase   ):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer mean
    _lowerCAmelCase       :   int  =    pt_tuple_key[:-1] + ('mean',)
    if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCamelCase   ):
        return renamed_pt_tuple_key, pt_tensor

    # batch norm layer var
    _lowerCAmelCase       :   Dict  =    pt_tuple_key[:-1] + ('var',)
    if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCamelCase   ):
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    _lowerCAmelCase       :   List[str]  =    pt_tuple_key[:-1] + ('embedding',)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCamelCase   ):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    _lowerCAmelCase       :   List[Any]  =    pt_tuple_key[:-1] + ('kernel',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCamelCase   ):
        _lowerCAmelCase       :   Optional[Any]  =    pt_tensor.transpose(2 ,   3 ,   1 ,   0   )
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    _lowerCAmelCase       :   Any  =    pt_tuple_key[:-1] + ('kernel',)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCamelCase   ):
        _lowerCAmelCase       :   List[Any]  =    pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    _lowerCAmelCase       :   Optional[Any]  =    pt_tuple_key[:-1] + ('weight',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    _lowerCAmelCase       :   int  =    pt_tuple_key[:-1] + ('bias',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    _lowerCAmelCase       :   Optional[int]  =    None
    if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
        _lowerCAmelCase       :   Union[str, Any]  =    pt_tuple_key[-2] + '_g'
    elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
        _lowerCAmelCase       :   List[str]  =    pt_tuple_key[-2] + '_v'
    if name is not None:
        _lowerCAmelCase       :   Any  =    pt_tuple_key[:-3] + (name,)
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    {k: v.numpy() for k, v in pt_state_dict.items()}

    _lowerCAmelCase       :   Optional[int]  =    flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers
    if "params" in flax_model.params:
        _lowerCAmelCase       :   Optional[int]  =    flax_model.params['params']
    else:
        _lowerCAmelCase       :   Union[str, Any]  =    flax_model.params
    _lowerCAmelCase       :   str  =    flatten_dict(_lowerCamelCase   )

    # add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
        _lowerCAmelCase       :   List[Any]  =    flatten_dict(flax_model.params['batch_stats']   )
        random_flax_state_dict.update(_lowerCamelCase   )

    _lowerCAmelCase       :   List[str]  =    {}

    _lowerCAmelCase       :   Any  =    (model_prefix not in flax_model_params) and (
        model_prefix in {k.split('.'   )[0] for k in pt_state_dict.keys()}
    )
    _lowerCAmelCase       :   Optional[Any]  =    (model_prefix in flax_model_params) and (
        model_prefix not in {k.split('.'   )[0] for k in pt_state_dict.keys()}
    )

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        _lowerCAmelCase       :   Any  =    tuple(pt_key.split('.'   )   )

        # remove base model prefix if necessary
        _lowerCAmelCase       :   List[Any]  =    pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            _lowerCAmelCase       :   List[str]  =    pt_tuple_key[1:]

        # Correctly rename weight parameters
        _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    rename_key_and_reshape_tensor(
            _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )

        # add model prefix if necessary
        _lowerCAmelCase       :   List[str]  =    (model_prefix,) + flax_key in random_flax_state_dict
        if load_base_model_into_model_with_head and require_base_model_prefix:
            _lowerCAmelCase       :   List[str]  =    (model_prefix,) + flax_key

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

        # add batch stats if the model contains batchnorm layers
        if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1] or "var" in flax_key[-1]:
                _lowerCAmelCase       :   Tuple  =    jnp.asarray(_lowerCamelCase   )
                continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
                flax_state_dict.pop(_lowerCamelCase ,   _lowerCamelCase   )
                continue

            # also add unexpected weight so that warning is thrown
            _lowerCAmelCase       :   str  =    jnp.asarray(_lowerCamelCase   )

        else:
            # also add unexpected weight so that warning is thrown
            _lowerCAmelCase       :   int  =    jnp.asarray(_lowerCamelCase   )

    return unflatten_dict(_lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    import torch

    # Load the index
    _lowerCAmelCase       :   Optional[int]  =    {}
    for shard_file in shard_filenames:
        # load using msgpack utils
        _lowerCAmelCase       :   List[str]  =    torch.load(_lowerCamelCase   )
        _lowerCAmelCase       :   Optional[Any]  =    {k: v.numpy() for k, v in pt_state_dict.items()}

        _lowerCAmelCase       :   Dict  =    flax_model.base_model_prefix

        # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
        if "batch_stats" in flax_model.params:
            _lowerCAmelCase       :   Optional[Any]  =    flax_model.params['params']

            _lowerCAmelCase       :   Union[str, Any]  =    flatten_dict(_lowerCamelCase   )
            random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']   )   )
        else:
            _lowerCAmelCase       :   Optional[Any]  =    flax_model.params
            _lowerCAmelCase       :   str  =    flatten_dict(_lowerCamelCase   )

        _lowerCAmelCase       :   str  =    (model_prefix not in flax_model_params) and (
            model_prefix in {k.split('.'   )[0] for k in pt_state_dict.keys()}
        )
        _lowerCAmelCase       :   Tuple  =    (model_prefix in flax_model_params) and (
            model_prefix not in {k.split('.'   )[0] for k in pt_state_dict.keys()}
        )
        # Need to change some parameters name to match Flax names
        for pt_key, pt_tensor in pt_state_dict.items():
            _lowerCAmelCase       :   Tuple  =    tuple(pt_key.split('.'   )   )

            # remove base model prefix if necessary
            _lowerCAmelCase       :   List[Any]  =    pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                _lowerCAmelCase       :   List[Any]  =    pt_tuple_key[1:]

            # Correctly rename weight parameters
            _lowerCAmelCase,					_lowerCAmelCase       :   int  =    rename_key_and_reshape_tensor(
                _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
            # add model prefix if necessary
            _lowerCAmelCase       :   Dict  =    (model_prefix,) + flax_key in random_flax_state_dict
            if load_base_model_into_model_with_head and require_base_model_prefix:
                _lowerCAmelCase       :   List[str]  =    (model_prefix,) + flax_key

            if flax_key in random_flax_state_dict:
                if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                        f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

            # add batch stats if the model contains batchnorm layers
            if "batch_stats" in flax_model.params:
                if "mean" in flax_key[-1]:
                    _lowerCAmelCase       :   str  =    jnp.asarray(_lowerCamelCase   )
                    continue
                if "var" in flax_key[-1]:
                    _lowerCAmelCase       :   Optional[Any]  =    jnp.asarray(_lowerCamelCase   )
                    continue
                # remove num_batches_tracked key
                if "num_batches_tracked" in flax_key[-1]:
                    flax_state_dict.pop(_lowerCamelCase ,   _lowerCamelCase   )
                    continue

                # also add unexpected weight so that warning is thrown
                _lowerCAmelCase       :   Union[str, Any]  =    jnp.asarray(_lowerCamelCase   )

            else:
                # also add unexpected weight so that warning is thrown
                _lowerCAmelCase       :   List[str]  =    jnp.asarray(_lowerCamelCase   )
    return unflatten_dict(_lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    os.path.abspath(_lowerCamelCase   )
    logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""   )

    # import correct flax class
    _lowerCAmelCase       :   Union[str, Any]  =    getattr(_lowerCamelCase ,   'Flax' + model.__class__.__name__   )

    # load flax weight dict
    with open(_lowerCamelCase ,   'rb'   ) as state_f:
        try:
            _lowerCAmelCase       :   Any  =    from_bytes(_lowerCamelCase ,   state_f.read()   )
        except UnpicklingError:
            raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """   )

    return load_flax_weights_in_pytorch_model(_lowerCamelCase ,   _lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
            ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
            ' instructions.'   )
        raise

    # check if we have bf16 weights
    _lowerCAmelCase       :   Tuple  =    flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase   : x.dtype == jnp.bfloataa ,   _lowerCamelCase   )   ).values()
    if any(_lowerCamelCase   ):
        # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
        # and bf16 is not fully supported in PT yet.
        logger.warning(
            'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
            'before loading those in PyTorch model.'   )
        _lowerCAmelCase       :   List[Any]  =    jax.tree_util.tree_map(
            lambda _lowerCamelCase   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params ,   _lowerCamelCase   )

    _lowerCAmelCase       :   Optional[int]  =    flatten_dict(_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    pt_model.state_dict()

    _lowerCAmelCase       :   Any  =    (pt_model.base_model_prefix in flax_state) and (
        pt_model.base_model_prefix not in {k.split('.'   )[0] for k in pt_model_dict.keys()}
    )
    _lowerCAmelCase       :   List[str]  =    (pt_model.base_model_prefix not in flax_state) and (
        pt_model.base_model_prefix in {k.split('.'   )[0] for k in pt_model_dict.keys()}
    )

    # keep track of unexpected & missing keys
    _lowerCAmelCase       :   Tuple  =    []
    _lowerCAmelCase       :   List[str]  =    set(pt_model_dict.keys()   )

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        _lowerCAmelCase       :   List[str]  =    flax_key_tuple[0] == pt_model.base_model_prefix
        _lowerCAmelCase       :   List[Any]  =    '.'.join((pt_model.base_model_prefix,) + flax_key_tuple   ) in pt_model_dict

        # adapt flax_key to prepare for loading from/to base model only
        if load_model_with_head_into_base_model and has_base_model_prefix:
            _lowerCAmelCase       :   Tuple  =    flax_key_tuple[1:]
        elif load_base_model_into_model_with_head and require_base_model_prefix:
            _lowerCAmelCase       :   List[Any]  =    (pt_model.base_model_prefix,) + flax_key_tuple

        # rename flax weights to PyTorch format
        if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCamelCase   ) not in pt_model_dict:
            # conv layer
            _lowerCAmelCase       :   Tuple  =    flax_key_tuple[:-1] + ('weight',)
            _lowerCAmelCase       :   Tuple  =    jnp.transpose(_lowerCamelCase ,   (3, 2, 0, 1)   )
        elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase   ) not in pt_model_dict:
            # linear layer
            _lowerCAmelCase       :   List[Any]  =    flax_key_tuple[:-1] + ('weight',)
            _lowerCAmelCase       :   str  =    flax_tensor.T
        elif flax_key_tuple[-1] in ["scale", "embedding"]:
            _lowerCAmelCase       :   int  =    flax_key_tuple[:-1] + ('weight',)

        # adding batch stats from flax batch norm to pt
        elif "mean" in flax_key_tuple[-1]:
            _lowerCAmelCase       :   List[Any]  =    flax_key_tuple[:-1] + ('running_mean',)
        elif "var" in flax_key_tuple[-1]:
            _lowerCAmelCase       :   Optional[int]  =    flax_key_tuple[:-1] + ('running_var',)

        if "batch_stats" in flax_state:
            _lowerCAmelCase       :   Any  =    '.'.join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
        else:
            _lowerCAmelCase       :   Tuple  =    '.'.join(_lowerCamelCase   )

        # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
        _lowerCAmelCase       :   Any  =    {}
        # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
        for key in pt_model_dict:
            _lowerCAmelCase       :   Optional[Any]  =    key.split('.'   )
            _lowerCAmelCase       :   Dict  =    None
            if key_components[-3::2] == ["parametrizations", "original0"]:
                _lowerCAmelCase       :   List[Any]  =    key_components[-2] + '_g'
            elif key_components[-3::2] == ["parametrizations", "original1"]:
                _lowerCAmelCase       :   Tuple  =    key_components[-2] + '_v'
            if name is not None:
                _lowerCAmelCase       :   int  =    key_components[:-3] + [name]
                _lowerCAmelCase       :   Optional[Any]  =    '.'.join(_lowerCamelCase   )
                _lowerCAmelCase       :   Optional[Any]  =    key

        if flax_key in special_pt_names:
            _lowerCAmelCase       :   Tuple  =    special_pt_names[flax_key]

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                    f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )
            else:
                # add weight to pytorch dict
                _lowerCAmelCase       :   Union[str, Any]  =    np.asarray(_lowerCamelCase   ) if not isinstance(_lowerCamelCase ,   np.ndarray   ) else flax_tensor
                _lowerCAmelCase       :   Optional[int]  =    torch.from_numpy(_lowerCamelCase   )
                # remove from missing keys
                missing_keys.remove(_lowerCamelCase   )
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(_lowerCamelCase   )

    pt_model.load_state_dict(_lowerCamelCase   )

    # re-transform missing_keys to list
    _lowerCAmelCase       :   Dict  =    list(_lowerCamelCase   )

    if len(_lowerCamelCase   ) > 0:
        logger.warning(
            'Some weights of the Flax model were not used when initializing the PyTorch model'
            f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
            f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
            ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
            f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
            ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
            ' FlaxBertForSequenceClassification model).'   )
    else:
        logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""   )
    if len(_lowerCamelCase   ) > 0:
        logger.warning(
            f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
            f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
            ' use it for predictions and inference.'   )
    else:
        logger.warning(
            f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
            'If your task is similar to the task the model of the checkpoint was trained on, '
            f"""you can already use {pt_model.__class__.__name__} for predictions without further training."""   )

    return pt_model








"""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





