


"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase				:  List[str] 					=     logging.get_logger(__name__)

_lowerCAmelCase				:  str 					=     {
    '''mnaylor/mega-base-wikitext''': '''https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json''',
}




class 				A_	(    a_      ):
  lowerCAmelCase__	       =     'mega'


  def __init__(       self:		Optional[Any]				,__lowerCAmelCase:		Any=30_522				,__lowerCAmelCase:		str=128				,__lowerCAmelCase:		str=4				,__lowerCAmelCase:		List[str]=256				,__lowerCAmelCase:		Any=16				,__lowerCAmelCase:		Dict=True				,__lowerCAmelCase:		Union[str, Any]=64				,__lowerCAmelCase:		Dict=False				,__lowerCAmelCase:		Union[str, Any]=-1				,__lowerCAmelCase:		Optional[int]=None				,__lowerCAmelCase:		Any=True				,__lowerCAmelCase:		Optional[int]="scalenorm"				,__lowerCAmelCase:		Optional[Any]=True				,__lowerCAmelCase:		Union[str, Any]="silu"				,__lowerCAmelCase:		str="softmax"				,__lowerCAmelCase:		List[str]=0.1				,__lowerCAmelCase:		Union[str, Any]=0.1				,__lowerCAmelCase:		int=0.1				,__lowerCAmelCase:		Any=False				,__lowerCAmelCase:		Optional[int]=True				,__lowerCAmelCase:		Union[str, Any]=256				,__lowerCAmelCase:		Optional[int]=True				,__lowerCAmelCase:		Tuple=0.1				,__lowerCAmelCase:		List[str]=2_048				,__lowerCAmelCase:		Dict=False				,__lowerCAmelCase:		List[str]=2				,__lowerCAmelCase:		Any=0.02				,__lowerCAmelCase:		int=0.2				,__lowerCAmelCase:		List[str]=0.02				,__lowerCAmelCase:		List[str]=1.0				,__lowerCAmelCase:		Dict=1				,__lowerCAmelCase:		List[Any]=0				,__lowerCAmelCase:		Optional[Any]=2				,__lowerCAmelCase:		Dict="rotary"				,__lowerCAmelCase:		Union[str, Any]=None				,__lowerCAmelCase:		Dict=True				,__lowerCAmelCase:		Any=True				,**__lowerCAmelCase:		Dict				,):







     '''simple docstring'''







     super().__init__(pad_token_id=__lowerCAmelCase				,bos_token_id=__lowerCAmelCase				,eos_token_id=__lowerCAmelCase				,**__lowerCAmelCase  )

     _lowerCamelCase					:							Optional[int]				=			vocab_size
     _lowerCamelCase					:							Dict				=			hidden_size
     _lowerCamelCase					:							int				=			num_hidden_layers
     _lowerCamelCase					:							Dict				=			activation
     _lowerCamelCase					:							Any				=			attention_activation
     _lowerCamelCase					:							Dict				=			intermediate_size
     _lowerCamelCase					:							Tuple				=			ema_projection_size
     _lowerCamelCase					:							Dict				=			bidirectional
     _lowerCamelCase					:							List[str]				=			shared_representation_size
     _lowerCamelCase					:							Union[str, Any]				=			use_chunking
     _lowerCamelCase					:							List[str]				=			chunk_size
     _lowerCamelCase					:							str				=			truncation
     _lowerCamelCase					:							Any				=			normalize_before_mega
     _lowerCamelCase					:							str				=			normalization_type
     _lowerCamelCase					:							List[str]				=			norm_affine
     _lowerCamelCase					:							int				=			dropout_prob
     _lowerCamelCase					:							str				=			hidden_dropout_prob
     _lowerCamelCase					:							Dict				=			attention_probs_dropout_prob
     _lowerCamelCase					:							Union[str, Any]				=			use_feature_dropout
     _lowerCamelCase					:							Union[str, Any]				=			use_normalized_ffn
     _lowerCamelCase					:							Optional[Any]				=			nffn_hidden_size
     _lowerCamelCase					:							List[Any]				=			normalize_before_ffn
     _lowerCamelCase					:							Optional[int]				=			nffn_activation_dropout_prob
     _lowerCamelCase					:							int				=			max_positions
     _lowerCamelCase					:							Union[str, Any]				=			add_token_type_embeddings
     _lowerCamelCase					:							List[Any]				=			type_vocab_size
     _lowerCamelCase					:							Union[str, Any]				=			initializer_range
     _lowerCamelCase					:							int				=			ema_delta_alpha_range
     _lowerCamelCase					:							int				=			ema_beta_range
     _lowerCamelCase					:							int				=			ema_gamma_omega_range
     _lowerCamelCase					:							int				=			relative_positional_bias
     _lowerCamelCase					:							Tuple				=			use_cache
     _lowerCamelCase					:							List[str]				=			classifier_dropout
     _lowerCamelCase					:							Tuple				=			add_lm_hidden_dense_layer
     _lowerCamelCase					:							Dict				=			1  # not used but required by Hugging Face




class 				A_	(    a_      ):


  @property
  def 				_lowercase (       self:		str  ):







     '''simple docstring'''







     if self.task == "multiple-choice":
        _lowerCamelCase					:							int				=			{0: 'batch', 1: 'choice', 2: 'sequence'}
     else:
        _lowerCamelCase					:							Tuple				=			{0: 'batch', 1: 'sequence'}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]  )
from typing import List

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


if is_torch_available():
 import torch




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Dict 			=       ['image_processor', 'tokenizer']
 _lowerCamelCase  :		Tuple 			=       'OneFormerImageProcessor'
 _lowerCamelCase  :		Dict 			=       ('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__(	self				:						str				,	lowerCamelCase				:						Any=None				,	lowerCamelCase				:						List[Any]=None				,	lowerCamelCase				:						int = 7_7				,	lowerCamelCase				:						int = 7_7				,	**lowerCamelCase				:						Union[str, Any]     )       ->							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`.'     )

        _UpperCamelCase   :				int       =     max_seq_length
        _UpperCamelCase   :				Optional[Any]       =     task_seq_length

        super().__init__(lowerCamelCase				,	lowerCamelCase     )

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						Optional[int]=7_7     )       ->							Tuple:
        if text_list is None:
               raise ValueError('tokens cannot be None.'     )

        _UpperCamelCase   :				Dict       =     self.tokenizer(lowerCamelCase				,	padding='max_length'				,	max_length=lowerCamelCase				,	truncation=lowerCamelCase     )

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

        _UpperCamelCase   :				Optional[int]       =     []
        for attn_mask, input_id in zip(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     torch.tensor(lowerCamelCase     ) * torch.tensor(lowerCamelCase     )
               token_inputs.append(token.unsqueeze(0     )     )

        _UpperCamelCase   :				Dict       =     torch.cat(lowerCamelCase				,	dim=0     )
        return token_inputs

 def __call__(	self				:						Union[str, Any]				,	lowerCamelCase				:						int=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						Any=None				,	**lowerCamelCase				:						List[Any]     )       ->							List[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.'     )

        _UpperCamelCase   :				Optional[Any]       =     self.image_processor(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				str       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				Optional[Any]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				Dict       =     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'     ):
               _UpperCamelCase   :				str       =     encoded_inputs.text_inputs

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

               _UpperCamelCase   :				Optional[int]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						List[str]				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						int=None				,	**lowerCamelCase				:						List[str]     )       ->							Tuple:

        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.'     )

        _UpperCamelCase   :				Any       =     self.image_processor.encode_inputs(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				,	**lowerCamelCase     )

        if isinstance(lowerCamelCase				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     [task_inputs]

        if isinstance(lowerCamelCase				,	lowerCamelCase     ) and all(isinstance(lowerCamelCase				,	lowerCamelCase     ) for task_input in task_inputs     ):
               _UpperCamelCase   :				int       =     []
               for task in task_inputs:
                      _UpperCamelCase   :				List[str]       =     f"the task is {task}"
                      task_token_inputs.append(lowerCamelCase     )
               _UpperCamelCase   :				int       =     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'     ):
               _UpperCamelCase   :				Optional[int]       =     encoded_inputs.text_inputs

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

               _UpperCamelCase   :				Optional[Any]       =     torch.cat(lowerCamelCase				,	dim=0     )

        return encoded_inputs

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						Tuple				,	**lowerCamelCase				:						Any     )       ->							str:
        return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Union[str, Any]				,	*lowerCamelCase				:						Optional[int]				,	**lowerCamelCase				:						Any     )       ->							Any:
        return self.image_processor.post_process_instance_segmentation(*lowerCamelCase				,	**lowerCamelCase     )

 def     __snake_case    (	self				:						Dict				,	*lowerCamelCase				:						List[str]				,	**lowerCamelCase				:						int     )       ->							List[Any]:
        return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase				,	**lowerCamelCase     )



