



"""simple docstring"""






import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def 	snake_case_  (				A_ :		Dict,    A_ :		Dict=False						):


       '''simple docstring'''





       try:
              _lowerCamelCase						:    Optional[Any]         =			os.environ[key]
       except KeyError:
              # KEY isn't set, default to `default`.
              _lowerCamelCase						:    str         =			default
       else:
              # KEY is set, convert it to True or False.
              try:
                     _lowerCamelCase						:    int         =			strtobool(A_						)
              except ValueError:
                     # More values are supported, but let's keep the message simple.
                     raise ValueError(F'''If set, {key} must be yes or no.'''						)
       return _value


lowerCAmelCase__      =    parse_flag_from_env('''RUN_SLOW''', default=False)






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       return unittest.skip('''Test was skipped'''						)(A_						)






def 	snake_case_  (				A_ :		Tuple						):


       '''simple docstring'''





       return unittest.skipUnless(_run_slow_tests,    '''test is slow'''						)(A_						)






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return unittest.skipUnless(not torch.cuda.is_available(),    '''test requires only a CPU'''						)(A_						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       return unittest.skipUnless(torch.cuda.is_available(),    '''test requires a GPU'''						)(A_						)






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       return unittest.skipUnless(is_xpu_available(),    '''test requires a XPU'''						)(A_						)






def 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       return unittest.skipUnless(is_mps_available(),    '''test requires a `mps` backend support in `torch`'''						)(A_						)






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       return unittest.skipUnless(
           is_transformers_available() and is_datasets_available(),    '''test requires the Hugging Face suite'''						)(A_						)






def 	snake_case_  (				A_ :		Dict						):


       '''simple docstring'''





       return unittest.skipUnless(is_bnb_available(),    '''test requires the bitsandbytes library'''						)(A_						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       return unittest.skipUnless(is_tpu_available(),    '''test requires TPU'''						)(A_						)






def 	snake_case_  (				A_ :		Tuple						):


       '''simple docstring'''





       return unittest.skipUnless(torch.cuda.device_count() == 1,    '''test requires a GPU'''						)(A_						)






def 	snake_case_  (				A_ :		str						):


       '''simple docstring'''





       return unittest.skipUnless(torch.xpu.device_count() == 1,    '''test requires a XPU'''						)(A_						)






def 	snake_case_  (				A_ :		Optional[Any]						):


       '''simple docstring'''





       return unittest.skipUnless(torch.cuda.device_count() > 1,    '''test requires multiple GPUs'''						)(A_						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       return unittest.skipUnless(torch.xpu.device_count() > 1,    '''test requires multiple XPUs'''						)(A_						)






def 	snake_case_  (				A_ :		List[str]						):


       '''simple docstring'''





       return unittest.skipUnless(is_safetensors_available(),    '''test requires safetensors'''						)(A_						)






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       return unittest.skipUnless(is_deepspeed_available(),    '''test requires DeepSpeed'''						)(A_						)






def 	snake_case_  (				A_ :		Union[str, Any]						):


       '''simple docstring'''





       return unittest.skipUnless(is_torch_version('''>=''',    '''1.12.0'''						),    '''test requires torch version >= 1.12.0'''						)(A_						)






def 	snake_case_  (				A_ :		str=None,    A_ :		Any=None						):


       '''simple docstring'''





       if test_case is None:
              return partial(A_,    version=A_						)
       return unittest.skipUnless(is_torch_version('''>=''',    A_						),    F'''test requires torch version >= {version}'''						)(A_						)






def 	snake_case_  (				A_ :		Optional[Any]						):


       '''simple docstring'''





       return unittest.skipUnless(is_tensorboard_available(),    '''test requires Tensorboard'''						)(A_						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       return unittest.skipUnless(is_wandb_available(),    '''test requires wandb'''						)(A_						)






def 	snake_case_  (				A_ :		Any						):


       '''simple docstring'''





       return unittest.skipUnless(is_comet_ml_available(),    '''test requires comet_ml'''						)(A_						)


lowerCAmelCase__      =    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def 	snake_case_  (				A_ :		List[Any]						):


       '''simple docstring'''





       return unittest.skipUnless(
           _atleast_one_tracker_available,    '''test requires at least one tracker to be available and for `comet_ml` to not be installed''',    )(A_						)






class 	__snake_case	(     unittest.TestCase):
  snake_case__    :       List[str]        =						True




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						List[Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			tempfile.mkdtemp()




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Tuple		):






         """simple docstring"""






         if os.path.exists(cls.tmpdir		):
                shutil.rmtree(cls.tmpdir		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]		):






         """simple docstring"""






         if self.clear_on_setup:
                for path in Path(self.tmpdir		).glob('''**/*'''		):
                       if path.is_file():
                              path.unlink()
                       elif path.is_dir():
                              shutil.rmtree(__lowerCAmelCase		)






class 	__snake_case	(     unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         super().tearDown()
         # Reset the state of the AcceleratorState singleton.
         AcceleratorState._reset_state()
         PartialState._reset_state()






class 	__snake_case	(     unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Union[mock.Mock, List[mock.Mock]]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			mocks if isinstance(__lowerCAmelCase						, (tuple, list)		) else [mocks]
         for m in self.mocks:
                m.start()
                self.addCleanup(m.stop		)






def 	snake_case_  (				A_ :		Tuple						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			AcceleratorState()
       _lowerCamelCase						:    List[Any]         =			tensor[None].clone().to(state.device						)
       _lowerCamelCase						:    Union[str, Any]         =			gather(A_						).cpu()
       _lowerCamelCase						:    List[str]         =			tensor[0].cpu()
       for i in range(tensors.shape[0]						):
              if not torch.equal(tensors[i],    A_						):
                     return False
       return True






class 	__snake_case	:




  def __init__(			self					:						Union[str, Any]						, __lowerCAmelCase					:						Union[str, Any]						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			returncode
         _lowerCamelCase						:    int         =			stdout
         _lowerCamelCase						:    Union[str, Any]         =			stderr






async def 	snake_case_  (				A_ :		List[str],    A_ :		int						):


       '''simple docstring'''





       while True:
              _lowerCamelCase						:    Dict         =			await stream.readline()
              if line:
                     callback(A_						)
              else:
                     break






async def 	snake_case_  (				A_ :		str,    A_ :		Tuple=None,    A_ :		Optional[int]=None,    A_ :		Any=None,    A_ :		Union[str, Any]=False,    A_ :		Dict=False						):


       '''simple docstring'''





       if echo:
              print('''\nRunning: ''',    ''' '''.join(A_						)						)

       _lowerCamelCase						:    Tuple         =			await asyncio.create_subprocess_exec(
           cmd[0],    *cmd[1:],    stdin=A_,    stdout=asyncio.subprocess.PIPE,    stderr=asyncio.subprocess.PIPE,    env=A_,    )

       # note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
       # https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
       #
       # If it starts hanging, will need to switch to the following code. The problem is that no data
       # will be seen until it's done and if it hangs for example there will be no debug info.
       # out, err = await p.communicate()
       # return _RunOutput(p.returncode, out, err)

       _lowerCamelCase						:    Any         =			[]
       _lowerCamelCase						:    Tuple         =			[]

       def tee(A_ :		str,    A_ :		int,    A_ :		Dict,    A_ :		Any=""						):
              _lowerCamelCase						:    int         =			line.decode('''utf-8'''						).rstrip()
              sink.append(A_						)
              if not quiet:
                     print(A_,    A_,    file=A_						)

    # XXX: the timeout doesn't seem to make any difference here
       await asyncio.wait(
           [
               asyncio.create_task(_read_stream(p.stdout,    lambda A_						: tee(A_,    A_,    sys.stdout,    label='''stdout:'''						)						)						),
               asyncio.create_task(_read_stream(p.stderr,    lambda A_						: tee(A_,    A_,    sys.stderr,    label='''stderr:'''						)						)						),
           ],    timeout=A_,    )
       return _RunOutput(await p.wait(),    A_,    A_						)






def 	snake_case_  (				A_ :		List[Any],    A_ :		Tuple=None,    A_ :		int=None,    A_ :		Any=1_80,    A_ :		int=False,    A_ :		List[str]=True						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			asyncio.get_event_loop()
       _lowerCamelCase						:    Optional[int]         =			loop.run_until_complete(
           _stream_subprocess(A_,    env=A_,    stdin=A_,    timeout=A_,    quiet=A_,    echo=A_						)						)

       _lowerCamelCase						:    Tuple         =			''' '''.join(A_						)
       if result.returncode > 0:
              _lowerCamelCase						:    Tuple         =			'''\n'''.join(result.stderr						)
              raise RuntimeError(
                  F'''\'{cmd_str}\' failed with returncode {result.returncode}\n\n'''
                  F'''The combined stderr from workers follows:\n{stderr}'''						)

       return result






class 	__snake_case	(     _lowercase):
  pass






def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[Any]=False						):


       '''simple docstring'''





       try:
              _lowerCamelCase						:    Tuple         =			subprocess.check_output(A_,    stderr=subprocess.STDOUT						)
              if return_stdout:
                     if hasattr(A_,    '''decode'''						):
                            _lowerCamelCase						:    int         =			output.decode('''utf-8'''						)
                     return output
       except subprocess.CalledProcessError as e:
              raise SubprocessCallException(
                  F'''Command `{" ".join(A_						)}` failed with the following error:\n\n{e.output.decode()}'''						) from e







"""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 	__snake_case	(     _lowercase):
  snake_case__    :       torch.FloatTensor






class 	__snake_case	(     _lowercase  ,					_lowercase):




  @register_to_config
  def __init__(			self					:						int						, __lowerCAmelCase					:						int = 3_2						, __lowerCAmelCase					:						int = 6_4						, __lowerCAmelCase					:						int = 2_0						, __lowerCAmelCase					:						int = 7_6_8						, __lowerCAmelCase					:						Tuple=7_7						, __lowerCAmelCase					:						Tuple=4						, __lowerCAmelCase					:						float = 0.0						, __lowerCAmelCase					:						str = "silu"						, __lowerCAmelCase					:						Optional[str] = None						, __lowerCAmelCase					:						Optional[str] = None						, __lowerCAmelCase					:						Optional[str] = "linear"						, __lowerCAmelCase					:						Optional[str] = "prd"						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[int] = None						, ):






         """simple docstring"""






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

         _lowerCamelCase						:    List[Any]         =			time_embed_dim or inner_dim
         _lowerCamelCase						:    Optional[Any]         =			embedding_proj_dim or embedding_dim
         _lowerCamelCase						:    Tuple         =			clip_embed_dim or embedding_dim

         _lowerCamelCase						:    int         =			Timesteps(__lowerCAmelCase						, __lowerCAmelCase						, 0		)
         _lowerCamelCase						:    str         =			TimestepEmbedding(__lowerCAmelCase						, __lowerCAmelCase						, out_dim=__lowerCAmelCase						, act_fn=__lowerCAmelCase		)

         _lowerCamelCase						:    Union[str, Any]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)

         if embedding_proj_norm_type is None:
                _lowerCamelCase						:    Dict         =			None
         elif embedding_proj_norm_type == "layer":
                _lowerCamelCase						:    Any         =			nn.LayerNorm(__lowerCAmelCase		)
         else:
                raise ValueError(f'''unsupported embedding_proj_norm_type: {embedding_proj_norm_type}'''		)

         _lowerCamelCase						:    List[str]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)

         if encoder_hid_proj_type is None:
                _lowerCamelCase						:    Any         =			None
         elif encoder_hid_proj_type == "linear":
                _lowerCamelCase						:    List[str]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)
         else:
                raise ValueError(f'''unsupported encoder_hid_proj_type: {encoder_hid_proj_type}'''		)

         _lowerCamelCase						:    Optional[int]         =			nn.Parameter(torch.zeros(1						, num_embeddings + additional_embeddings						, __lowerCAmelCase		)		)

         if added_emb_type == "prd":
                _lowerCamelCase						:    Optional[Any]         =			nn.Parameter(torch.zeros(1						, 1						, __lowerCAmelCase		)		)
         elif added_emb_type is None:
                _lowerCamelCase						:    Union[str, 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						:    Tuple         =			nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __lowerCAmelCase						, __lowerCAmelCase						, __lowerCAmelCase						, dropout=__lowerCAmelCase						, activation_fn='''gelu'''						, attention_bias=__lowerCAmelCase						, )
                 for d in range(__lowerCAmelCase		)
             ]		)

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

         _lowerCamelCase						:    str         =			nn.LayerNorm(__lowerCAmelCase		)

         _lowerCamelCase						:    List[Any]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)

         _lowerCamelCase						:    Optional[int]         =			torch.full(
             [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]						, -1_00_00.0		)
         causal_attention_mask.triu_(1		)
         _lowerCamelCase						:    Dict         =			causal_attention_mask[None, ...]
         self.register_buffer('''causal_attention_mask'''						, __lowerCAmelCase						, persistent=__lowerCAmelCase		)

         _lowerCamelCase						:    int         =			nn.Parameter(torch.zeros(1						, __lowerCAmelCase		)		)
         _lowerCamelCase						:    Tuple         =			nn.Parameter(torch.zeros(1						, __lowerCAmelCase		)		)




  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			{}

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

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

                return processors

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

         return processors




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			len(self.attn_processors.keys()		)

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

         def fn_recursive_attn_processor(__lowerCAmelCase					:						str						, __lowerCAmelCase					:						torch.nn.Module						, __lowerCAmelCase					:						Tuple		):
                if hasattr(__lowerCAmelCase						, '''set_processor'''		):
                       if not isinstance(__lowerCAmelCase						, __lowerCAmelCase		):
                              module.set_processor(__lowerCAmelCase		)
                       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}'''						, __lowerCAmelCase						, __lowerCAmelCase		)

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




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         self.set_attn_processor(AttnProcessor()		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						List[str]						, __lowerCAmelCase					:						Union[torch.Tensor, float, int]						, __lowerCAmelCase					:						torch.FloatTensor						, __lowerCAmelCase					:						Optional[torch.FloatTensor] = None						, __lowerCAmelCase					:						Optional[torch.BoolTensor] = None						, __lowerCAmelCase					:						bool = True						, ):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			hidden_states.shape[0]

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

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

         _lowerCamelCase						:    Tuple         =			self.time_proj(__lowerCAmelCase		)

         # 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						:    Union[str, Any]         =			timesteps_projected.to(dtype=self.dtype		)
         _lowerCamelCase						:    Any         =			self.time_embedding(__lowerCAmelCase		)

         if self.embedding_proj_norm is not None:
                _lowerCamelCase						:    str         =			self.embedding_proj_norm(__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			self.embedding_proj(__lowerCAmelCase		)
         if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
                _lowerCamelCase						:    Optional[Any]         =			self.encoder_hidden_states_proj(__lowerCAmelCase		)
         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						:    str         =			self.proj_in(__lowerCAmelCase		)

         _lowerCamelCase						:    Tuple         =			self.positional_embedding.to(hidden_states.dtype		)

         _lowerCamelCase						:    Tuple         =			[]
         _lowerCamelCase						:    Dict         =			0

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

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

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

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

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

         _lowerCamelCase						:    str         =			torch.cat(
             __lowerCAmelCase						, dim=1						, )

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

         _lowerCamelCase						:    Any         =			hidden_states + positional_embeddings

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

         if self.norm_in is not None:
                _lowerCamelCase						:    List[str]         =			self.norm_in(__lowerCAmelCase		)

         for block in self.transformer_blocks:
                _lowerCamelCase						:    List[str]         =			block(__lowerCAmelCase						, attention_mask=__lowerCAmelCase		)

         _lowerCamelCase						:    Optional[Any]         =			self.norm_out(__lowerCAmelCase		)

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

         _lowerCamelCase						:    str         =			self.proj_to_clip_embeddings(__lowerCAmelCase		)

         if not return_dict:
                return (predicted_image_embedding,)

         return PriorTransformerOutput(predicted_image_embedding=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			(prior_latents * self.clip_std) + self.clip_mean
         return prior_latents


