import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


_lowercase							:      Union[str, Any]      							=logging.get_logger(__name__)  # pylint: disable=invalid-name



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __init__(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int=7_68     )    ->				List[Any]:
         super().__init__(UpperCamelCase__     )
         A						:     Any         =proj_size

         A						:     str         =CLIPVisionModel(UpperCamelCase__     )
         A						:     List[str]         =PaintByExampleMapper(UpperCamelCase__     )
         A						:     Any         =nn.LayerNorm(config.hidden_size     )
         A						:     Tuple         =nn.Linear(config.hidden_size      ,	self.proj_size     )

         # uncondition for scaling
         A						:     List[Any]         =nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False     )    ->				int:
         A						:     Optional[Any]         =self.model(pixel_values=UpperCamelCase__     )
         A						:     Optional[int]         =clip_output.pooler_output
         A						:     List[Any]         =self.mapper(latent_states[:, None]     )
         A						:     Union[str, Any]         =self.final_layer_norm(UpperCamelCase__     )
         A						:     List[str]         =self.proj_out(UpperCamelCase__     )
         if return_uncond_vector:
                return latent_states, self.uncond_vector

         return latent_states



class  SCREAMING_SNAKE_CASE_ (					nn.Module			):
  '''simple docstring'''


  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				Tuple:
         super().__init__()
         A						:     int         =(config.num_hidden_layers + 1) // 5
         A						:     Dict         =config.hidden_size
         A						:     Optional[int]         =1
         A						:     int         =nn.ModuleList(
             [
                 BasicTransformerBlock(UpperCamelCase__      ,	UpperCamelCase__      ,	UpperCamelCase__      ,	activation_fn='gelu'      ,	attention_bias=UpperCamelCase__     )
                 for _ in range(UpperCamelCase__     )
             ]     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				int:





         for block in self.blocks:
                A						:     Optional[Any]         =block(UpperCamelCase__     )

         return hidden_states







_lowercase							:      Dict      							='''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
    from .utils import rich






