
import torch

from transformers import AutoTokenizer

class  UpperCamelCase__ ( A__			):






      def __init__(     self					:   Union[str, Any],		__lowerCamelCase					:   Dict   )  -> Union[str, Any]:
           UpperCamelCase__							:  Any								=     AutoTokenizer.from_pretrained(_lowerCAmelCase   )






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   Union[str, Any]   )  -> Any:

           if isinstance(_lowerCAmelCase,		_lowerCAmelCase   ) and all(isinstance(_x,		_lowerCAmelCase   ) for _x in x   ):
                UpperCamelCase__							:  Optional[Any]								=     None
                for l in x:
                     UpperCamelCase__							:  Any								=     self.tokenizer(
                         _lowerCAmelCase,		padding='''max_length''',		max_length=3_84,		truncation=_lowerCAmelCase,		return_tensors='''pt''',		)
                     UpperCamelCase__							:  Any								=     torch.tensor([len(_lowerCAmelCase   )]   )
                     if d is not None:
                          for k in d.keys():
                               UpperCamelCase__							:  str								=     torch.cat((d[k], t[k]),		0   )
                     else:
                          UpperCamelCase__							:  Union[str, Any]								=     t

                UpperCamelCase__							:  Tuple								=     torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''   )   )
                UpperCamelCase__							:  str								=     torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''   )   )

           elif isinstance(_lowerCAmelCase,		_lowerCAmelCase   ) and all(isinstance(_x,		_lowerCAmelCase   ) for _x in x   ):
                UpperCamelCase__							:  Dict								=     self.tokenizer(
                    _lowerCAmelCase,		padding='''max_length''',		max_length=3_84,		truncation=_lowerCAmelCase,		return_tensors='''pt''',		)

           else:
                raise Exception(
                    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
                    ''' strings` for supports are supported.'''   )

           return d






      def 	__lowercase(     self					:   Optional[int],		__lowerCamelCase					:   Tuple,		__lowerCamelCase					:   Dict,		__lowerCamelCase					:   Optional[int],		__lowerCamelCase					:   Dict,		__lowerCamelCase					:   Dict=0.70   )  -> List[Any]:
           UpperCamelCase__							:  str								=     []
           for idx in range(len(W_query['''input_ids''']   )   ):
                UpperCamelCase__							:  Any								=     range(p_start.shape[1]   )

                UpperCamelCase__							:  Tuple								=     []
                for start_id in start_indexes:
                     for end_id in end_indexes:
                          if start_id < end_id:
                               output.append(
                                   (
                                       start_id,
                                       end_id,
                                       p_start[idx][start_id].item(),
                                       p_end[idx][end_id].item(),
                                   )   )

                output.sort(key=lambda __lowerCamelCase   : (tup[2] * tup[3]),		reverse=_lowerCAmelCase   )
                UpperCamelCase__							:  Union[str, Any]								=     []
                for k in range(len(_lowerCAmelCase   )   ):
                     if output[k][2] * output[k][3] >= thresh:
                          UpperCamelCase__	,UpperCamelCase__							:  List[str]								=     output[k][0], output[k][1]
                          UpperCamelCase__							:  str								=     self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]   )
                          temp.append((decoded, output[k][2] * output[k][3])   )

                final_outputs.append(_lowerCAmelCase   )

           return final_outputs








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__)




