'''simple docstring'''




from typing import List

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


if is_torch_available():
     import torch

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  ['''image_processor''', '''tokenizer''']
   __lowerCamelCase    =  '''OneFormerImageProcessor'''
   __lowerCamelCase    =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





   def __init__(							self:    int			,a:    List[Any]=None			,a:    int=None			,a:    int = 77			,a:    int = 77			,**a:    Dict	):
    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         =					max_seq_length
    __UpperCAmelCase         =					task_seq_length

    super().__init__(a			,a	)





   def 			snake_case	(							self:    Union[str, Any]			,a:    str=None			,a:    str=77	):
    if text_list is None:
     raise ValueError('tokens cannot be None.'	)

    __UpperCAmelCase         =					self.tokenizer(a			,padding='max_length'			,max_length=a			,truncation=a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					tokens['attention_mask'], tokens['input_ids']

    __UpperCAmelCase         =					[]
    for attn_mask, input_id in zip(a			,a	):
     __UpperCAmelCase         =					torch.tensor(a	) * torch.tensor(a	)
     token_inputs.append(token.unsqueeze(0	)	)

    __UpperCAmelCase         =					torch.cat(a			,dim=0	)
    return token_inputs





   def __call__(							self:    Union[str, Any]			,a:    str=None			,a:    int=None			,a:    int=None			,**a:    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.'	)

    __UpperCAmelCase         =					self.image_processor(a			,a			,a			,**a	)

    if isinstance(a			,a	):
     __UpperCAmelCase         =					[task_inputs]

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

    if hasattr(a			,'text_inputs'	):
     __UpperCAmelCase         =					encoded_inputs.text_inputs

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

     __UpperCAmelCase         =					torch.cat(a			,dim=0	)

    return encoded_inputs





   def 			snake_case	(							self:    Tuple			,a:    List[str]=None			,a:    Dict=None			,a:    int=None			,**a:    Dict	):

    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         =					self.image_processor.encode_inputs(a			,a			,a			,**a	)

    if isinstance(a			,a	):
     __UpperCAmelCase         =					[task_inputs]

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

    if hasattr(a			,'text_inputs'	):
     __UpperCAmelCase         =					encoded_inputs.text_inputs

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

     __UpperCAmelCase         =					torch.cat(a			,dim=0	)

    return encoded_inputs





   def 			snake_case	(							self:    Union[str, Any]			,*a:    str			,**a:    Tuple	):
    return self.image_processor.post_process_semantic_segmentation(*a			,**a	)





   def 			snake_case	(							self:    Any			,*a:    int			,**a:    List[str]	):
    return self.image_processor.post_process_instance_segmentation(*a			,**a	)





   def 			snake_case	(							self:    Any			,*a:    str			,**a:    Any	):
    return self.image_processor.post_process_panoptic_segmentation(*a			,**a	)




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:		Dict       			=		{
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		['MarianTokenizer']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		[
         'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MarianForCausalLM',
         'MarianModel',
         'MarianMTModel',
         'MarianPreTrainedModel',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Dict       			=		['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
     from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_marian import MarianTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_marian import (
              MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              MarianForCausalLM,
              MarianModel,
              MarianMTModel,
              MarianPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
     import sys

     _UpperCamelCase				:		List[Any]       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



