



from typing import List

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


if is_torch_available():
    import torch

class 					lowercase						(	__snake_case ):




       '''simple docstring'''







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






       def __init__(						self		,       _snake_case=None		,       _snake_case=None		,       _snake_case = 77		,       _snake_case = 77		,       **_snake_case   )					->		Tuple:

           """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`.'''   )

           UpperCAmelCase     =    max_seq_length
           UpperCAmelCase     =    task_seq_length

           super().__init__(_snake_case		,       _snake_case   )






       def 				snake_case_ (						self		,       _snake_case=None		,       _snake_case=77   )					->		List[Any]:

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

           UpperCAmelCase     =    self.tokenizer(_snake_case		,       padding='''max_length'''		,       max_length=_snake_case		,       truncation=_snake_case   )

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

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

           UpperCAmelCase     =    torch.cat(_snake_case		,       dim=0   )
           return token_inputs






       def __call__(						self		,       _snake_case=None		,       _snake_case=None		,       _snake_case=None		,       **_snake_case   )					->		Optional[int]:

           """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.'''   )

           UpperCAmelCase     =    self.image_processor(_snake_case		,       _snake_case		,       _snake_case		,       **_snake_case   )

           if isinstance(_snake_case		,       _snake_case   ):
               UpperCAmelCase     =    [task_inputs]

           if isinstance(_snake_case		,       _snake_case   ) and all(isinstance(_snake_case		,       _snake_case   ) for task_input in task_inputs   ):
               UpperCAmelCase     =    []
               for task in task_inputs:
                   UpperCAmelCase     =    f"""the task is {task}"""
                   task_token_inputs.append(_snake_case   )
               UpperCAmelCase     =    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'''   ):
               UpperCAmelCase     =    encoded_inputs.text_inputs

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

               UpperCAmelCase     =    torch.cat(_snake_case		,       dim=0   )

           return encoded_inputs






       def 				snake_case_ (						self		,       _snake_case=None		,       _snake_case=None		,       _snake_case=None		,       **_snake_case   )					->		Dict:

           """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.'''   )

           UpperCAmelCase     =    self.image_processor.encode_inputs(_snake_case		,       _snake_case		,       _snake_case		,       **_snake_case   )

           if isinstance(_snake_case		,       _snake_case   ):
               UpperCAmelCase     =    [task_inputs]

           if isinstance(_snake_case		,       _snake_case   ) and all(isinstance(_snake_case		,       _snake_case   ) for task_input in task_inputs   ):
               UpperCAmelCase     =    []
               for task in task_inputs:
                   UpperCAmelCase     =    f"""the task is {task}"""
                   task_token_inputs.append(_snake_case   )
               UpperCAmelCase     =    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'''   ):
               UpperCAmelCase     =    encoded_inputs.text_inputs

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

               UpperCAmelCase     =    torch.cat(_snake_case		,       dim=0   )

           return encoded_inputs






       def 				snake_case_ (						self		,       *_snake_case		,       **_snake_case   )					->		List[str]:

           """simple docstring"""
           return self.image_processor.post_process_semantic_segmentation(*_snake_case		,       **_snake_case   )






       def 				snake_case_ (						self		,       *_snake_case		,       **_snake_case   )					->		Optional[int]:

           """simple docstring"""
           return self.image_processor.post_process_instance_segmentation(*_snake_case		,       **_snake_case   )






       def 				snake_case_ (						self		,       *_snake_case		,       **_snake_case   )					->		Tuple:

           """simple docstring"""
           return self.image_processor.post_process_panoptic_segmentation(*_snake_case		,       **_snake_case   )







from typing import TYPE_CHECKING

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


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)