



"""simple docstring"""
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
						import torch
class 							lowerCamelCase    (     _lowerCAmelCase							):






					'''simple docstring'''





					_A						:     Any		=	["""image_processor""", """tokenizer"""]
					_A						:     Optional[int]		=	"""OneFormerImageProcessor"""
					_A						:     Any		=	("""CLIPTokenizer""", """CLIPTokenizerFast""")






					def __init__(			self:				List[Any]		,	snake_case:				Tuple=None		,	snake_case:				str=None		,	snake_case:				int = 77		,	snake_case:				int = 77		,	**snake_case:				Optional[Any]		)  ->      List[str]:
											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`."""		)

											snake_case_					:List[str]		=     max_seq_length
											snake_case_					:Optional[Any]		=     task_seq_length

											super().__init__(snake_case		,	snake_case		)






					def   lowerCAmelCase_							(			self:				List[str]		,	snake_case:				List[str]=None		,	snake_case:				Any=77		)  ->      Tuple:
											if text_list is None:
																	raise ValueError("""tokens cannot be None."""		)

											snake_case_					:List[str]		=     self.tokenizer(snake_case		,	padding="""max_length"""		,	max_length=snake_case		,	truncation=snake_case		)

											snake_case_,			snake_case_					:List[Any]		=     tokens["""attention_mask"""], tokens["""input_ids"""]

											snake_case_					:int		=     []
											for attn_mask, input_id in zip(snake_case		,	snake_case		):
																	snake_case_					:List[Any]		=     torch.tensor(snake_case		) * torch.tensor(snake_case		)
																	token_inputs.append(token.unsqueeze(0		)		)

											snake_case_					:Tuple		=     torch.cat(snake_case		,	dim=0		)
											return token_inputs






					def __call__(			self:				Union[str, Any]		,	snake_case:				str=None		,	snake_case:				Optional[int]=None		,	snake_case:				str=None		,	**snake_case:				Union[str, Any]		)  ->      Optional[Any]:

											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."""		)

											snake_case_					:List[Any]		=     self.image_processor(snake_case		,	snake_case		,	snake_case		,	**snake_case		)

											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:Tuple		=     [task_inputs]

											if isinstance(snake_case		,	snake_case		) and all(isinstance(snake_case		,	snake_case		) for task_input in task_inputs		):
																	snake_case_					:List[str]		=     []
																	for task in task_inputs:
																							snake_case_					:Tuple		=     f"""the task is {task}"""
																							task_token_inputs.append(snake_case		)
																	snake_case_					:Dict		=     self._preprocess_text(snake_case		,	max_length=self.task_seq_length		)
											else:
																	raise TypeError("""Task Inputs should be a string or a list of strings."""		)

											if hasattr(snake_case		,	"""text_inputs"""		):
																	snake_case_					:List[Any]		=     encoded_inputs.text_inputs

																	snake_case_					:Union[str, Any]		=     []
																	for texts in texts_list:
																							snake_case_					:Optional[int]		=     self._preprocess_text(snake_case		,	max_length=self.max_seq_length		)
																							text_inputs.append(text_input_list.unsqueeze(0		)		)

																	snake_case_					:int		=     torch.cat(snake_case		,	dim=0		)

											return encoded_inputs






					def   lowerCAmelCase_							(			self:				Any		,	snake_case:				Union[str, Any]=None		,	snake_case:				Optional[int]=None		,	snake_case:				Tuple=None		,	**snake_case:				Optional[Any]		)  ->      Optional[int]:

											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."""		)

											snake_case_					:List[str]		=     self.image_processor.encode_inputs(snake_case		,	snake_case		,	snake_case		,	**snake_case		)

											if isinstance(snake_case		,	snake_case		):
																	snake_case_					:str		=     [task_inputs]

											if isinstance(snake_case		,	snake_case		) and all(isinstance(snake_case		,	snake_case		) for task_input in task_inputs		):
																	snake_case_					:Dict		=     []
																	for task in task_inputs:
																							snake_case_					:List[Any]		=     f"""the task is {task}"""
																							task_token_inputs.append(snake_case		)
																	snake_case_					:Dict		=     self._preprocess_text(snake_case		,	max_length=self.task_seq_length		)
											else:
																	raise TypeError("""Task Inputs should be a string or a list of strings."""		)

											if hasattr(snake_case		,	"""text_inputs"""		):
																	snake_case_					:Optional[Any]		=     encoded_inputs.text_inputs

																	snake_case_					:Tuple		=     []
																	for texts in texts_list:
																							snake_case_					:Dict		=     self._preprocess_text(snake_case		,	max_length=self.max_seq_length		)
																							text_inputs.append(text_input_list.unsqueeze(0		)		)

																	snake_case_					:Optional[int]		=     torch.cat(snake_case		,	dim=0		)

											return encoded_inputs






					def   lowerCAmelCase_							(			self:				str		,	*snake_case:				List[str]		,	**snake_case:				Tuple		)  ->      Dict:
											return self.image_processor.post_process_semantic_segmentation(*snake_case		,	**snake_case		)






					def   lowerCAmelCase_							(			self:				Dict		,	*snake_case:				str		,	**snake_case:				List[str]		)  ->      Dict:
											return self.image_processor.post_process_instance_segmentation(*snake_case		,	**snake_case		)






					def   lowerCAmelCase_							(			self:				Optional[Any]		,	*snake_case:				List[Any]		,	**snake_case:				List[Any]		)  ->      int:
											return self.image_processor.post_process_panoptic_segmentation(*snake_case		,	**snake_case		)


"""simple docstring"""




import argparse
import os


A__   :						Any 							=     {
    """pytorch""": {
        """1.13""": {
            """torch""": """1.13.1""",
            """torchvision""": """0.14.1""",
            """torchaudio""": """0.13.1""",
            """python""": 3.9,
            """cuda""": """cu116""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu116"""
            ),
            """base_image""": """nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.12""": {
            """torch""": """1.12.1""",
            """torchvision""": """0.13.1""",
            """torchaudio""": """0.12.1""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.11""": {
            """torch""": """1.11.0""",
            """torchvision""": """0.12.0""",
            """torchaudio""": """0.11.0""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """1.10""": {
            """torch""": """1.10.2""",
            """torchvision""": """0.11.3""",
            """torchaudio""": """0.10.2""",
            """python""": 3.9,
            """cuda""": """cu113""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"""
                """ --extra-index-url https://download.pytorch.org/whl/cu113"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        """1.9""": {
            """torch""": """1.9.1""",
            """torchvision""": """0.10.1""",
            """torchaudio""": """0.9.1""",
            """python""": 3.9,
            """cuda""": """cu111""",
            """install""": (
                """python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"""
                """ --extra-index-url https://download.pytorch.org/whl/cu111"""
            ),
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
    """tensorflow""": {
        """2.11""": {
            """tensorflow""": """2.11.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.11.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.10""": {
            """tensorflow""": """2.10.1""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.10.1""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.9""": {
            """tensorflow""": """2.9.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.9.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.8""": {
            """tensorflow""": """2.8.2""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.8.2""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.7""": {
            """tensorflow""": """2.7.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.7.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.6""": {
            """tensorflow""": """2.6.5""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.6.5""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
        """2.5""": {
            """tensorflow""": """2.5.3""",
            """install""": """python3 -m pip install --no-cache-dir -U tensorflow==2.5.3""",
            """base_image""": """nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04""",
        },
    },
}


if __name__ == "__main__":
    A__   :						str 							=     argparse.ArgumentParser("""Choose the framework and version to install""")
    parser.add_argument(
        """--framework""", help="""The framework to install. Should be `torch` or `tensorflow`""", type=str, required=True
    )
    parser.add_argument("""--version""", help="""The version of the framework to install.""", type=str, required=True)
    A__   :						Any 							=     parser.parse_args()

    A__   :						List[Any] 							=     past_versions_testing[args.framework][args.version]

    os.system(f"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")
    print(f"""echo \"export INSTALL_CMD=\'{info["install"]}\'\" >> ~/.profile""")

    A__   :						Any 							=     """"""
    if args.framework == "pytorch":
        A__   :						str 							=     info["""cuda"""]
    os.system(f"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")
    print(f"""echo \"export CUDA='{cuda}'\" >> ~/.profile""")



