
import torch

from transformers import AutoTokenizer





class 							UpperCAmelCase   (						A__						):




 '''simple docstring'''





 def __init__( self      :				Union[str, Any]							,A      :				Dict				):
     __A         =				AutoTokenizer.from_pretrained(_lowerCAmelCase				)






 def        UpperCamelCase_						( self      :				str							,A      :				Union[str, Any]				):

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

         __A         =				torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"				)				)
         __A         =				torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"				)				)

     elif isinstance(_lowerCAmelCase							,_lowerCAmelCase				) and all(isinstance(_x							,_lowerCAmelCase				) for _x in x				):
         __A         =				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        UpperCamelCase_						( self      :				Optional[int]							,A      :				Tuple							,A      :				Dict							,A      :				Optional[int]							,A      :				Dict							,A      :				Dict=0.70				):
     __A         =				[]
     for idx in range(len(W_query["input_ids"]				)				):
         __A         =				__A         =				range(p_start.shape[1]				)

         __A         =				[]
         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 A				: (tup[2] * tup[3])							,reverse=_lowerCAmelCase				)
         __A         =				[]
         for k in range(len(_lowerCAmelCase				)				):
             if output[k][2] * output[k][3] >= thresh:
                 __A    ,							__A         =				output[k][0], output[k][1]
                 __A         =				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							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

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