
from typing import List

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


if is_torch_available():
   import torch

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     Optional[int]								=						['image_processor', 'tokenizer']
      a__   :     List[Any]								=						'OneFormerImageProcessor'
      a__   :     Union[str, Any]								=						('CLIPTokenizer', 'CLIPTokenizerFast')






      def __init__(     self					:   str,		__lowerCamelCase					:   Union[str, Any]=None,		__lowerCamelCase					:   Optional[int]=None,		__lowerCamelCase					:   int = 77,		__lowerCamelCase					:   int = 77,		**__lowerCamelCase					:   int   )  -> Tuple:
           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__							:  Optional[Any]								=     max_seq_length
           UpperCamelCase__							:  str								=     task_seq_length

           super().__init__(__lowerCamelCase,		__lowerCamelCase   )






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   Optional[int]=None,		__lowerCamelCase					:   Union[str, Any]=77   )  -> List[Any]:
           if text_list is None:
                raise ValueError('''tokens cannot be None.'''   )

           UpperCamelCase__							:  Optional[Any]								=     self.tokenizer(__lowerCamelCase,		padding='''max_length''',		max_length=__lowerCamelCase,		truncation=__lowerCamelCase   )

           UpperCamelCase__							:  Union[str, Any]								=     tokens['''attention_mask'''], tokens['''input_ids''']

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

           UpperCamelCase__							:  Union[str, Any]								=     torch.cat(__lowerCamelCase,		dim=0   )
           return token_inputs






      def __call__(     self					:   Any,		__lowerCamelCase					:   int=None,		__lowerCamelCase					:   str=None,		__lowerCamelCase					:   List[Any]=None,		**__lowerCamelCase					:   List[str]   )  -> 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__							:  Dict								=     self.image_processor(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		**__lowerCamelCase   )

           if isinstance(__lowerCamelCase,		__lowerCamelCase   ):
                UpperCamelCase__							:  Optional[Any]								=     [task_inputs]

           if isinstance(__lowerCamelCase,		__lowerCamelCase   ) and all(isinstance(__lowerCamelCase,		__lowerCamelCase   ) for task_input in task_inputs   ):
                UpperCamelCase__							:  Tuple								=     []
                for task in task_inputs:
                     UpperCamelCase__							:  Optional[int]								=     f'the task is {task}'
                     task_token_inputs.append(__lowerCamelCase   )
                UpperCamelCase__							:  Optional[Any]								=     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__							:  List[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 	__lowercase(     self					:   Optional[int],		__lowerCamelCase					:   Dict=None,		__lowerCamelCase					:   str=None,		__lowerCamelCase					:   Tuple=None,		**__lowerCamelCase					:   Optional[int]   )  -> 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__							:  Optional[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__							:  Optional[int]								=     []
                for task in task_inputs:
                     UpperCamelCase__							:  Dict								=     f'the task is {task}'
                     task_token_inputs.append(__lowerCamelCase   )
                UpperCamelCase__							:  Union[str, Any]								=     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__							:  List[str]								=     encoded_inputs.text_inputs

                UpperCamelCase__							:  Union[str, Any]								=     []
                for texts in texts_list:
                     UpperCamelCase__							:  int								=     self._preprocess_text(__lowerCamelCase,		max_length=self.max_seq_length   )
                     text_inputs.append(text_input_list.unsqueeze(0   )   )

                UpperCamelCase__							:  int								=     torch.cat(__lowerCamelCase,		dim=0   )

           return encoded_inputs






      def 	__lowercase(     self					:   Tuple,		*__lowerCamelCase					:   List[str],		**__lowerCamelCase					:   Optional[Any]   )  -> Tuple:
           return self.image_processor.post_process_semantic_segmentation(*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   List[str],		*__lowerCamelCase					:   Any,		**__lowerCamelCase					:   Dict   )  -> int:
           return self.image_processor.post_process_instance_segmentation(*__lowerCamelCase,		**__lowerCamelCase   )






      def 	__lowercase(     self					:   Any,		*__lowerCamelCase					:   Any,		**__lowerCamelCase					:   Union[str, Any]   )  -> int:
           return self.image_processor.post_process_panoptic_segmentation(*__lowerCamelCase,		**__lowerCamelCase   )








from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE :					Optional[Any]			=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	["""LayoutLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					List[str]			=	[
       """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """LayoutLMForMaskedLM""",
       """LayoutLMForSequenceClassification""",
       """LayoutLMForTokenClassification""",
       """LayoutLMForQuestionAnswering""",
       """LayoutLMModel""",
       """LayoutLMPreTrainedModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _SCREAMING_SNAKE_CASE :					str			=	[
       """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFLayoutLMForMaskedLM""",
       """TFLayoutLMForSequenceClassification""",
       """TFLayoutLMForTokenClassification""",
       """TFLayoutLMForQuestionAnswering""",
       """TFLayoutLMMainLayer""",
       """TFLayoutLMModel""",
       """TFLayoutLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
   from .tokenization_layoutlm import LayoutLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )

else:
   import sys

   _SCREAMING_SNAKE_CASE :					Any			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




