
import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


_lowercase				:						Union[str, Any]												= [nn.LayerNorm]

_lowercase				:						List[Any]												= logging.get_logger(__name__)

_lowercase				:						Dict												= version.parse(version.parse(torch.__version__).base_version)

_lowercase				:						List[str]												= parsed_torch_version_base >= version.parse("2.0")
_lowercase				:						Optional[Any]												= parsed_torch_version_base >= version.parse("1.12")
_lowercase				:						Optional[int]												= parsed_torch_version_base >= version.parse("1.11")
_lowercase				:						Dict												= parsed_torch_version_base < version.parse("1.11")
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				str   ,   self      :				Tuple							):



						"""simple docstring"""





						from torch import _softmax_backward_data

						if is_torch_less_than_1_11:
												return _softmax_backward_data(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   parent.dim   ,   self							)
						else:
												return _softmax_backward_data(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   parent.dim   ,   self.dtype							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Union[str, Any]   ,   __SCREAMING_SNAKE_CASE      :				Union[str, Any] = 0							):



						"""simple docstring"""





						lowercase_			:  Union[str, Any]									= index.to(layer.weight.device							)
						lowercase_			:  int									= layer.weight.index_select(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							).clone().detach()
						if layer.bias is not None:
												if dim == 1:
																		lowercase_			:  Tuple									= layer.bias.clone().detach()
												else:
																		lowercase_			:  Dict									= layer.bias[index].clone().detach()
						lowercase_			:  Optional[Any]									= list(layer.weight.size()							)
						lowercase_			:  Optional[int]									= len(__SCREAMING_SNAKE_CASE							)
						lowercase_			:  List[str]									= nn.Linear(new_size[1]   ,   new_size[0]   ,   bias=layer.bias is not None							).to(layer.weight.device							)
						lowercase_			:  str									= False
						new_layer.weight.copy_(W.contiguous()							)
						lowercase_			:  Tuple									= True
						if layer.bias is not None:
												lowercase_			:  Tuple									= False
												new_layer.bias.copy_(b.contiguous()							)
												lowercase_			:  Tuple									= True
						return new_layer






class 				lowerCAmelCase__     (						nn.Module  ):






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											super().__init__()
											lowercase_			:  List[str]									= nf
											lowercase_			:  Any									= nn.Parameter(torch.empty(lowerCamelCase__       ,							lowerCamelCase__	)	)
											lowercase_			:  Union[str, Any]									= nn.Parameter(torch.zeros(lowerCamelCase__	)	)
											nn.init.normal_(self.weight       ,							std=0.02	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  int									= x.size()[:-1] + (self.nf,)
											lowercase_			:  int									= torch.addmm(self.bias       ,							x.view(-1       ,							x.size(-1	)	)       ,							self.weight	)
											lowercase_			:  Any									= x.view(lowerCamelCase__	)
											return x







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[Any]   ,   __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				Tuple = 1							):



						"""simple docstring"""





						lowercase_			:  List[str]									= index.to(layer.weight.device							)
						lowercase_			:  Optional[Any]									= layer.weight.index_select(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							).clone().detach()
						if dim == 0:
												lowercase_			:  Union[str, Any]									= layer.bias.clone().detach()
						else:
												lowercase_			:  List[str]									= layer.bias[index].clone().detach()
						lowercase_			:  Any									= list(layer.weight.size()							)
						lowercase_			:  List[str]									= len(__SCREAMING_SNAKE_CASE							)
						lowercase_			:  Union[str, Any]									= ConvaD(new_size[1]   ,   new_size[0]							).to(layer.weight.device							)
						lowercase_			:  List[str]									= False
						new_layer.weight.copy_(W.contiguous()							)
						lowercase_			:  Optional[Any]									= True
						lowercase_			:  str									= False
						new_layer.bias.copy_(b.contiguous()							)
						lowercase_			:  List[Any]									= True
						return new_layer
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Optional[int]   ,   __SCREAMING_SNAKE_CASE      :				List[Any]   ,   __SCREAMING_SNAKE_CASE      :				Any = None							):



						"""simple docstring"""





						if isinstance(__SCREAMING_SNAKE_CASE   ,   nn.Linear							):
												return prune_linear_layer(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   dim=0 if dim is None else dim							)
						elif isinstance(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							):
												return prune_convad_layer(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE   ,   dim=1 if dim is None else dim							)
						else:
												raise ValueError(F'''Can\'t prune layer of class {layer.__class__}'''							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				Tuple   ,   *__SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						assert len(__SCREAMING_SNAKE_CASE							) > 0, F'''{input_tensors} has to be a tuple/list of tensors'''

						# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
						lowercase_			:  Optional[Any]									= len(inspect.signature(__SCREAMING_SNAKE_CASE							).parameters							)
						if num_args_in_forward_chunk_fn != len(__SCREAMING_SNAKE_CASE							):
												raise ValueError(
												    F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(__SCREAMING_SNAKE_CASE							)} input '''
												    '''tensors are given'''							)

						if chunk_size > 0:
												lowercase_			:  Dict									= input_tensors[0].shape[chunk_dim]
												for input_tensor in input_tensors:
																		if input_tensor.shape[chunk_dim] != tensor_shape:
																								raise ValueError(
																								    F'''All input tenors have to be of the same shape: {tensor_shape}, '''
																								    F'''found shape {input_tensor.shape[chunk_dim]}'''							)

												if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
																		raise ValueError(
																		    F'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
																		    F'''size {chunk_size}'''							)

												lowercase_			:  List[str]									= input_tensors[0].shape[chunk_dim] // chunk_size

												# chunk input tensor into tuples
												lowercase_			:  List[Any]									= tuple(input_tensor.chunk(__SCREAMING_SNAKE_CASE   ,   dim=__SCREAMING_SNAKE_CASE							) for input_tensor in input_tensors							)
												# apply forward fn to every tuple
												lowercase_			:  Optional[Any]									= tuple(forward_fn(*__SCREAMING_SNAKE_CASE							) for input_tensors_chunk in zip(*__SCREAMING_SNAKE_CASE							)							)
												# concatenate output at same dimension
												return torch.cat(__SCREAMING_SNAKE_CASE   ,   dim=__SCREAMING_SNAKE_CASE							)

						return forward_fn(*__SCREAMING_SNAKE_CASE							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str   ,   __SCREAMING_SNAKE_CASE      :				Dict   ,   __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				List[Any]							):



						"""simple docstring"""





						lowercase_			:  Optional[int]									= torch.ones(__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE							)
						lowercase_			:  int									= set(__SCREAMING_SNAKE_CASE							) - already_pruned_heads  # Convert to set and remove already pruned heads
						for head in heads:
												# Compute how many pruned heads are before the head and move the index accordingly
												lowercase_			:  Any									= head - sum(1 if h < head else 0 for h in already_pruned_heads							)
												lowercase_			:  Optional[Any]									= 0
						lowercase_			:  Tuple									= mask.view(-1							).contiguous().eq(1							)
						lowercase_			:  torch.LongTensor									= torch.arange(len(__SCREAMING_SNAKE_CASE							)							)[mask].long()
						return heads, index
def       snake_case_	(       *__SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				Dict = None							):



						"""simple docstring"""





						return torch.meshgrid(*__SCREAMING_SNAKE_CASE   ,   indexing=__SCREAMING_SNAKE_CASE							)




def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[str]							):



						"""simple docstring"""





						return tensor.device, storage_ptr(__SCREAMING_SNAKE_CASE							), storage_size(__SCREAMING_SNAKE_CASE							)





'''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_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""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`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""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.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

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

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""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.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

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

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



