code: 
  
  
  import math
  from collections import OrderedDict
  
  import torch
  from packaging import version
  from torch import Tensor, nn
  
  from .utils import logging
  
  
  A_    :					Any             =logging.get_logger(__name__)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().__init__()
  													if version.parse(torch.__version__		) < version.parse("""1.12.0"""		):
  																			raise ImportError(
  																			    f"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use "
  																			    """PytorchGELUTanh. Please upgrade torch."""		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return nn.functional.gelu(_UpperCAmelCase			,    approximate="""tanh"""		)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi		) * (input + 0.0_4_4_7_1_5 * torch.pow(_UpperCAmelCase			,    3.0		))		))
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase = False		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().__init__()
  													if use_gelu_python:
  																			a_					=		self._gelu_python
  													else:
  																			a_					=		nn.functional.gelu
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0		)		))
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.act(_UpperCAmelCase		)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return 0.5 * input * (1.0 + torch.tanh(input * 0.7_9_7_8_8_4_5_6_0_8 * (1.0 + 0.0_4_4_7_1_5 * input * input)		))
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return input * torch.sigmoid(1.7_0_2 * input		)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if min > max:
  																			raise ValueError(f"min should be < max (got min: {min}, max: {max})"		)
  
  													super().__init__()
  													a_					=		min
  													a_					=		max
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return torch.clip(gelu(_UpperCAmelCase		)			,    self.min			,    self.max		)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().__init__()
  													a_					=		math.sqrt(2 / math.pi		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.0_4_4_7_1_5 * torch.pow(_UpperCAmelCase			,    3		))		))
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return nn.functional.silu(_UpperCAmelCase		)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().__init__()
  													if version.parse(torch.__version__		) < version.parse("""1.9.0"""		):
  																			a_					=		self._mish_python
  													else:
  																			a_					=		nn.functional.mish
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return input * torch.tanh(nn.functional.softplus(_UpperCAmelCase		)		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.act(_UpperCAmelCase		)
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return input
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=0.7_0_7_1_0_7			,    _UpperCAmelCase=0.2_8_2_0_9_5		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		(input - mu).div(sigma * math.sqrt(2.0		)		)
  													return 0.5 * (1.0 + torch.erf(_UpperCAmelCase		))
  
  
  
  
  
  class        lowercase_				( nn.Module):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		nn.functional.relu(_UpperCAmelCase		)
  													a_					=		torch.square(_UpperCAmelCase		)
  													return squared
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __getitem__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		super().__getitem__(_UpperCAmelCase		)
  													a_			,   a_					=		content if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else (content, {})
  													return cls(**_UpperCAmelCase		)
  
  
  
  
  A_    :					int             ={
      """gelu""": GELUActivation,
      """gelu_10""": (ClippedGELUActivation, {"""min""": -10, """max""": 10}),
      """gelu_fast""": FastGELUActivation,
      """gelu_new""": NewGELUActivation,
      """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
      """gelu_pytorch_tanh""": PytorchGELUTanh,
      """gelu_accurate""": AccurateGELUActivation,
      """laplace""": LaplaceActivation,
      """linear""": LinearActivation,
      """mish""": MishActivation,
      """quick_gelu""": QuickGELUActivation,
      """relu""": nn.ReLU,
      """relu2""": ReLUSquaredActivation,
      """relu6""": nn.ReLUa,
      """sigmoid""": nn.Sigmoid,
      """silu""": SiLUActivation,
      """swish""": SiLUActivation,
      """tanh""": nn.Tanh,
  }
  A_    :					Dict             =ClassInstantier(ACTaCLS)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if activation_string in ACTaFN:
  												return ACTaFN[activation_string]
  						else:
  												raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()      )}"      )
  
  
  # For backwards compatibility with: from activations import gelu_python
  A_    :					Union[str, Any]             =get_activation("""gelu_python""")
  A_    :					Union[str, Any]             =get_activation("""gelu_new""")
  A_    :					List[str]             =get_activation("""gelu""")
  A_    :					str             =get_activation("""gelu_fast""")
  A_    :					List[Any]             =get_activation("""quick_gelu""")
  A_    :					str             =get_activation("""silu""")
  A_    :					List[str]             =get_activation("""mish""")
  A_    :					Optional[Any]             =get_activation("""linear""")
style_context: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if image_processor is None:
  																			raise ValueError("""You need to specify an `image_processor`."""		)
  													if tokenizer is None:
  																			raise ValueError("""You need to specify a `tokenizer`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)