






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

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


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)  # pylint: disable=invalid-name
class 							__SCREAMING_SNAKE_CASE       (							__A     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=768      )							->str:



         '''simple docstring'''
         super().__init__(UpperCamelCase__      )
         __a				     =    proj_size

         __a				     =    CLIPVisionModel(UpperCamelCase__      )
         __a				     =    PaintByExampleMapper(UpperCamelCase__      )
         __a				     =    nn.LayerNorm(config.hidden_size      )
         __a				     =    nn.Linear(config.hidden_size ,   self.proj_size      )

         # uncondition for scaling
         __a				     =    nn.Parameter(torch.randn((1, 1, self.proj_size)      )      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=False      )							->List[str]:



         '''simple docstring'''
         __a				     =    self.model(pixel_values=UpperCamelCase__      )
         __a				     =    clip_output.pooler_output
         __a				     =    self.mapper(latent_states[:, None]      )
         __a				     =    self.final_layer_norm(UpperCamelCase__      )
         __a				     =    self.proj_out(UpperCamelCase__      )
         if return_uncond_vector:
            return latent_states, self.uncond_vector

         return latent_states







class 							__SCREAMING_SNAKE_CASE       (							nn.Module     ):




      def __init__(  self ,   lowerCamelCase      )							->Union[str, Any]:



         '''simple docstring'''
         super().__init__()
         __a				     =    (config.num_hidden_layers + 1) // 5
         __a				     =    config.hidden_size
         __a				     =    1
         __a				     =    nn.ModuleList(
             [
                 BasicTransformerBlock(UpperCamelCase__ ,   UpperCamelCase__ ,   UpperCamelCase__ ,   activation_fn='gelu' ,   attention_bias=UpperCamelCase__      )
                 for _ in range(UpperCamelCase__      )
             ]      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         for block in self.blocks:
            __a				     =    block(UpperCamelCase__      )

         return hidden_states







'''simple docstring'''



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
 import torch

if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm


__UpperCamelCase      :       Optional[Any]      			=      logging.get_logger(__name__)
@dataclass
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					=[
          "no_inference",
          "no_cuda",
          "no_tpu",
          "no_speed",
          "no_memory",
          "no_env_print",
          "no_multi_process",
      ]




      def __init__(  self ,   **lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
               __a				     =    deprecated_arg[3:]
               setattr(self ,   lowerCamelCase ,   not kwargs.pop(lowerCamelCase      )      )
               logger.warning(
                   F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                   F""" {positive_arg}={kwargs[positive_arg]}"""      )

         __a				     =    kwargs.pop('torchscript' ,   self.torchscript      )
         __a				     =    kwargs.pop('torch_xla_tpu_print_metrics' ,   self.torch_xla_tpu_print_metrics      )
         __a				     =    kwargs.pop('fp16_opt_level' ,   self.fpaa_opt_level      )
         super().__init__(**lowerCamelCase      )

      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Trace the models using torchscript"}     )
      __a  					=field(default=_lowerCAmelCase      ,      metadata={"help": "Print Xla/PyTorch tpu metrics"}     )
      __a  					=field(
          default="O1"      ,      metadata={
              "help": (
                  "For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
                  "See details at https://nvidia.github.io/apex/amp.html"
              )
          }      ,      )




      @cached_property
      def  __UpperCamelCase							(  self      )							->Tuple["torch.device", int]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         logger.info('PyTorch: setting up devices'      )
         if not self.cuda:
            __a				     =    torch.device('cpu'      )
            __a				     =    0
         elif is_torch_tpu_available():
            __a				     =    xm.xla_device()
            __a				     =    0
         else:
            __a				     =    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
            __a				     =    torch.cuda.device_count()
         return device, n_gpu




      @property
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         return is_torch_tpu_available() and self.tpu




      @property
      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         # TODO(PVP): currently only single GPU is supported
         return torch.cuda.current_device()




      @property
      def  __UpperCamelCase							(  self      )							->"torch.device":



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[0]




      @property
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         requires_backends(self ,   ['torch']      )
         return self._setup_devices[1]






      @property
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         return self.n_gpu > 0