


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""")



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		)