code: 
  '''simple docstring'''
  
  
  
  from typing import List, Optional, Tuple, Union
  
  from . import logging
  from .import_utils import is_torch_available, is_torch_version
  
  
  if is_torch_available():
  			import torch
  
  __A          =    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  try:
  			from torch._dynamo import allow_in_graph as maybe_allow_in_graph
  except (ImportError, ModuleNotFoundError):
  
  
  
  
  
  			def  _A		(							cls	):
  									return cls
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__ = None		,		lowercase__ = None		,		lowercase__ = None		,		lowercase__ = None		,		):
  						lowercase__							 =       device
  						lowercase__							 =       shape[0]
  
  						lowercase__							 =       layout or torch.strided
  						lowercase__							 =       device or torch.device("""cpu"""	)
  
  						if generator is not None:
  												lowercase__							 =       generator.device.type if not isinstance(lowercase__		,		lowercase__	) else generator[0].device.type
  												if gen_device_type != device.type and gen_device_type == "cpu":
  																		lowercase__							 =       """cpu"""
  																		if device != "mps":
  																								logger.info(
  																								    f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
  																								    f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
  																								    f''' slighly speed up this function by passing a generator that was created on the {device} device.'''	)
  												elif gen_device_type != device.type and gen_device_type == "cuda":
  																		raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''	)
  
  						if isinstance(lowercase__		,		lowercase__	):
  												lowercase__							 =       (1,) + shape[1:]
  												lowercase__							 =       [
  												    torch.randn(lowercase__		,		generator=generator[i]		,		device=lowercase__		,		dtype=lowercase__		,		layout=lowercase__	)
  												    for i in range(lowercase__	)
  												]
  												lowercase__							 =       torch.cat(lowercase__		,		dim=0	).to(lowercase__	)
  						else:
  												lowercase__							 =       torch.randn(lowercase__		,		generator=lowercase__		,		device=lowercase__		,		dtype=lowercase__		,		layout=lowercase__	).to(lowercase__	)
  
  						return latents
  
  
  
  
  
  
  
  def  _A		(							lowercase__	):
  						if is_torch_version("""<"""		,		"""2.0.0"""	) or not hasattr(lowercase__		,		"""_dynamo"""	):
  												return False
  						return isinstance(lowercase__		,		torch._dynamo.eval_frame.OptimizedModule	)
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  