

from typing import List

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


if is_torch_available():
       import torch







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     List[str]    =       ['image_processor', 'tokenizer']
   lowerCamelCase__			:     Optional[Any]    =       'OneFormerImageProcessor'
   lowerCamelCase__			:     int    =       ('CLIPTokenizer', 'CLIPTokenizerFast')



   def __init__(    self,   lowercase_=None,   lowercase_=None,   lowercase_ = 77,   lowercase_ = 77,   **lowercase_	)							->   Any:






     """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`.'''	)

     a__  =max_seq_length
     a__  =task_seq_length

     super().__init__(lowercase_,   lowercase_	)



   def 			_UpperCAmelCase			(    self,   lowercase_=None,   lowercase_=77	)							->   str:






     """simple docstring"""






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

     a__  =self.tokenizer(lowercase_,   padding='''max_length''',   max_length=lowercase_,   truncation=lowercase_	)

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

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

     a__  =torch.cat(lowercase_,   dim=0	)
     return token_inputs



   def __call__(    self,   lowercase_=None,   lowercase_=None,   lowercase_=None,   **lowercase_	)							->   str:






     """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.'''	)

     a__  =self.image_processor(lowercase_,   lowercase_,   lowercase_,   **lowercase_	)

     if isinstance(lowercase_,   lowercase_	):
       a__  =[task_inputs]

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

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

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

       a__  =torch.cat(lowercase_,   dim=0	)

     return encoded_inputs



   def 			_UpperCAmelCase			(    self,   lowercase_=None,   lowercase_=None,   lowercase_=None,   **lowercase_	)							->   str:






     """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.'''	)

     a__  =self.image_processor.encode_inputs(lowercase_,   lowercase_,   lowercase_,   **lowercase_	)

     if isinstance(lowercase_,   lowercase_	):
       a__  =[task_inputs]

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

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

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

       a__  =torch.cat(lowercase_,   dim=0	)

     return encoded_inputs



   def 			_UpperCAmelCase			(    self,   *lowercase_,   **lowercase_	)							->   str:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   *lowercase_,   **lowercase_	)							->   List[Any]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self,   *lowercase_,   **lowercase_	)							->   str:






     """simple docstring"""






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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase									= {
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''FlaubertForMultipleChoice''',
           '''FlaubertForQuestionAnswering''',
           '''FlaubertForQuestionAnsweringSimple''',
           '''FlaubertForSequenceClassification''',
           '''FlaubertForTokenClassification''',
           '''FlaubertModel''',
           '''FlaubertWithLMHeadModel''',
           '''FlaubertPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase									= [
           '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFFlaubertForMultipleChoice''',
           '''TFFlaubertForQuestionAnsweringSimple''',
           '''TFFlaubertForSequenceClassification''',
           '''TFFlaubertForTokenClassification''',
           '''TFFlaubertModel''',
           '''TFFlaubertPreTrainedModel''',
           '''TFFlaubertWithLMHeadModel''',
       ]


if TYPE_CHECKING:
       from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
       from .tokenization_flaubert import FlaubertTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flaubert import (
                  FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FlaubertForMultipleChoice,
                  FlaubertForQuestionAnswering,
                  FlaubertForQuestionAnsweringSimple,
                  FlaubertForSequenceClassification,
                  FlaubertForTokenClassification,
                  FlaubertModel,
                  FlaubertPreTrainedModel,
                  FlaubertWithLMHeadModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_flaubert import (
                  TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFFlaubertForMultipleChoice,
                  TFFlaubertForQuestionAnsweringSimple,
                  TFFlaubertForSequenceClassification,
                  TFFlaubertForTokenClassification,
                  TFFlaubertModel,
                  TFFlaubertPreTrainedModel,
                  TFFlaubertWithLMHeadModel,
              )

else:
       import sys

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