






from typing import List

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


if is_torch_available():
			import torch


class _lowerCAmelCase	(						__UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Union[str, Any]   				=							['image_processor', 'tokenizer']
						SCREAMING_SNAKE_CASE_:				str   				=							'OneFormerImageProcessor'
						SCREAMING_SNAKE_CASE_:				Union[str, Any]   				=							('CLIPTokenizer', 'CLIPTokenizerFast')







						def __init__(						self      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_ = 7_7      ,							lowerCAmelCase_ = 7_7      ,							**lowerCAmelCase_       )		->       Any:
										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`.'       )

										_SCREAMING_SNAKE_CASE      :		Any							=						max_seq_length
										_SCREAMING_SNAKE_CASE      :		str							=						task_seq_length

										super().__init__(lowerCAmelCase_      ,							lowerCAmelCase_       )







						def  A			(						self      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=7_7       )		->       Any:
										if text_list is None:
														raise ValueError('tokens cannot be None.'       )

										_SCREAMING_SNAKE_CASE      :		int							=						self.tokenizer(lowerCAmelCase_      ,							padding='max_length'      ,							max_length=lowerCAmelCase_      ,							truncation=lowerCAmelCase_       )

										_SCREAMING_SNAKE_CASE      :		List[Any]							=						tokens['attention_mask'], tokens['input_ids']

										_SCREAMING_SNAKE_CASE      :		str							=						[]
										for attn_mask, input_id in zip(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		Tuple							=						torch.tensor(lowerCAmelCase_       ) * torch.tensor(lowerCAmelCase_       )
														token_inputs.append(token.unsqueeze(0       )       )

										_SCREAMING_SNAKE_CASE      :		str							=						torch.cat(lowerCAmelCase_      ,							dim=0       )
										return token_inputs







						def __call__(						self      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							**lowerCAmelCase_       )		->       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.'       )

										_SCREAMING_SNAKE_CASE      :		int							=						self.image_processor(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							**lowerCAmelCase_       )

										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		Dict							=						[task_inputs]

										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ) and all(isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ) for task_input in task_inputs       ):
														_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						[]
														for task in task_inputs:
																		_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						F"""the task is {task}"""
																		task_token_inputs.append(lowerCAmelCase_       )
														_SCREAMING_SNAKE_CASE      :		str							=						self._preprocess_text(lowerCAmelCase_      ,							max_length=self.task_seq_length       )
										else:
														raise TypeError('Task Inputs should be a string or a list of strings.'       )

										if hasattr(lowerCAmelCase_      ,							'text_inputs'       ):
														_SCREAMING_SNAKE_CASE      :		List[Any]							=						encoded_inputs.text_inputs

														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						[]
														for texts in texts_list:
																		_SCREAMING_SNAKE_CASE      :		int							=						self._preprocess_text(lowerCAmelCase_      ,							max_length=self.max_seq_length       )
																		text_inputs.append(text_input_list.unsqueeze(0       )       )

														_SCREAMING_SNAKE_CASE      :		List[str]							=						torch.cat(lowerCAmelCase_      ,							dim=0       )

										return encoded_inputs







						def  A			(						self      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							lowerCAmelCase_=None      ,							**lowerCAmelCase_       )		->       List[str]:

										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.'       )

										_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						self.image_processor.encode_inputs(lowerCAmelCase_      ,							lowerCAmelCase_      ,							lowerCAmelCase_      ,							**lowerCAmelCase_       )

										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ):
														_SCREAMING_SNAKE_CASE      :		Tuple							=						[task_inputs]

										if isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ) and all(isinstance(lowerCAmelCase_      ,							lowerCAmelCase_       ) for task_input in task_inputs       ):
														_SCREAMING_SNAKE_CASE      :		str							=						[]
														for task in task_inputs:
																		_SCREAMING_SNAKE_CASE      :		Union[str, Any]							=						F"""the task is {task}"""
																		task_token_inputs.append(lowerCAmelCase_       )
														_SCREAMING_SNAKE_CASE      :		str							=						self._preprocess_text(lowerCAmelCase_      ,							max_length=self.task_seq_length       )
										else:
														raise TypeError('Task Inputs should be a string or a list of strings.'       )

										if hasattr(lowerCAmelCase_      ,							'text_inputs'       ):
														_SCREAMING_SNAKE_CASE      :		Any							=						encoded_inputs.text_inputs

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

														_SCREAMING_SNAKE_CASE      :		Optional[int]							=						torch.cat(lowerCAmelCase_      ,							dim=0       )

										return encoded_inputs







						def  A			(						self      ,							*lowerCAmelCase_      ,							**lowerCAmelCase_       )		->       Union[str, Any]:
										return self.image_processor.post_process_semantic_segmentation(*lowerCAmelCase_      ,							**lowerCAmelCase_       )







						def  A			(						self      ,							*lowerCAmelCase_      ,							**lowerCAmelCase_       )		->       str:
										return self.image_processor.post_process_instance_segmentation(*lowerCAmelCase_      ,							**lowerCAmelCase_       )







						def  A			(						self      ,							*lowerCAmelCase_      ,							**lowerCAmelCase_       )		->       Tuple:
										return self.image_processor.post_process_panoptic_segmentation(*lowerCAmelCase_      ,							**lowerCAmelCase_       )









"""simple docstring"""
from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims

