






'''simple docstring'''
from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


a_					=	logging.get_logger(__name__)






class        __SCREAMING_SNAKE_CASE    (     lowerCamelCase			):




 def        __magic_name__		( self		:							Tuple   , __lowercase		:							int )		->  int:
       if isinstance(__lowercase   , __lowercase ):
             SCREAMING_SNAKE_CASE__       :			int													=[label.strip() for label in labels.split(''',''' ) if label.strip()]
       return labels




 def __call__( self		:							List[str]   , __lowercase		:							Tuple   , __lowercase		:							Tuple   , __lowercase		:							int )		->  Any:
       if len(__lowercase ) == 0 or len(__lowercase ) == 0:
             raise ValueError('''You must include at least one label and at least one sequence.''' )
       if hypothesis_template.format(labels[0] ) == hypothesis_template:
             raise ValueError(
                 (
                     '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
                     '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
                 ).format(__lowercase ) )

       if isinstance(__lowercase   , __lowercase ):
             SCREAMING_SNAKE_CASE__       :			Any													=[sequences]

       SCREAMING_SNAKE_CASE__       :			Tuple													=[]
       for sequence in sequences:
             sequence_pairs.extend([[sequence, hypothesis_template.format(__lowercase )] for label in labels] )

       return sequence_pairs, sequences






@add_end_docstrings(lowerCamelCase			)
class        __SCREAMING_SNAKE_CASE    (     lowerCamelCase			):




 def __init__( self		:							Union[str, Any]   , __lowercase		:							Dict=ZeroShotClassificationArgumentHandler()   , *__lowercase		:							Any   , **__lowercase		:							str )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=args_parser
       super().__init__(*__lowercase   , **__lowercase )
       if self.entailment_id == -1:
             logger.warning(
                 '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
                 '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.''' )




 @property
 def        __magic_name__		( self		:							Dict )		->  Optional[int]:
       for label, ind in self.model.config.labelaid.items():
             if label.lower().startswith('''entail''' ):
                   return ind
       return -1




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							str   , __lowercase		:							List[str]=True   , __lowercase		:							List[Any]=True   , __lowercase		:							int=TruncationStrategy.ONLY_FIRST   , **__lowercase		:							Dict )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.framework
       if self.tokenizer.pad_token is None:
             # Override for tokenizers not supporting padding
             logger.error(
                 '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
                 ''' `pad_token=eos_token`''' )
             SCREAMING_SNAKE_CASE__       :			List[str]													=self.tokenizer.eos_token
       try:
             SCREAMING_SNAKE_CASE__       :			Dict													=self.tokenizer(
                 __lowercase   , add_special_tokens=__lowercase   , return_tensors=__lowercase   , padding=__lowercase   , truncation=__lowercase   , )
       except Exception as e:
             if "too short" in str(__lowercase ):
                   # tokenizers might yell that we want to truncate
                   # to a value that is not even reached by the input.
                   # In that case we don't want to truncate.
                   # It seems there's not a really better way to catch that
                   # exception.

                   SCREAMING_SNAKE_CASE__       :			List[str]													=self.tokenizer(
                       __lowercase   , add_special_tokens=__lowercase   , return_tensors=__lowercase   , padding=__lowercase   , truncation=TruncationStrategy.DO_NOT_TRUNCATE   , )
             else:
                   raise e

       return inputs




 def        __magic_name__		( self		:							Optional[int]   , **__lowercase		:							Union[str, Any] )		->  List[Any]:
       if kwargs.get('''multi_class'''   , __lowercase ) is not None:
             SCREAMING_SNAKE_CASE__       :			Tuple													=kwargs['''multi_class''']
             logger.warning(
                 '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
                 '''`multi_class` will be removed in a future version of Transformers.''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													={}
       if "candidate_labels" in kwargs:
             SCREAMING_SNAKE_CASE__       :			int													=self._args_parser._parse_labels(kwargs['''candidate_labels'''] )
       if "hypothesis_template" in kwargs:
             SCREAMING_SNAKE_CASE__       :			List[Any]													=kwargs['''hypothesis_template''']

       SCREAMING_SNAKE_CASE__       :			Any													={}
       if "multi_label" in kwargs:
             SCREAMING_SNAKE_CASE__       :			Dict													=kwargs['''multi_label''']
       return preprocess_params, {}, postprocess_params




 def __call__( self		:							Union[str, Any]   , __lowercase		:							Union[str, List[str]]   , *__lowercase		:							Optional[int]   , **__lowercase		:							Optional[int]   , )		->  str:
       if len(__lowercase ) == 0:
             pass
       elif len(__lowercase ) == 1 and "candidate_labels" not in kwargs:
             SCREAMING_SNAKE_CASE__       :			int													=args[0]
       else:
             raise ValueError(F"Unable to understand extra arguments {args}" )

       return super().__call__(__lowercase   , **__lowercase )




 def        __magic_name__		( self		:							Optional[Any]   , __lowercase		:							List[Any]   , __lowercase		:							str=None   , __lowercase		:							Union[str, Any]="This example is {}." )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self._args_parser(__lowercase   , __lowercase   , __lowercase )

       for i, (candidate_label, sequence_pair) in enumerate(zip(__lowercase   , __lowercase ) ):
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self._parse_and_tokenize([sequence_pair] )

             yield {
                 "candidate_label": candidate_label,
                 "sequence": sequences[0],
                 "is_last": i == len(__lowercase ) - 1,
                 **model_input,
             }




 def        __magic_name__		( self		:							int   , __lowercase		:							Tuple )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=inputs['''candidate_label''']
       SCREAMING_SNAKE_CASE__       :			List[str]													=inputs['''sequence''']
       SCREAMING_SNAKE_CASE__       :			List[Any]													={k: inputs[k] for k in self.tokenizer.model_input_names}
       SCREAMING_SNAKE_CASE__       :			Dict													=self.model(**__lowercase )

       SCREAMING_SNAKE_CASE__       :			int													={
           '''candidate_label''': candidate_label,
           '''sequence''': sequence,
           '''is_last''': inputs['''is_last'''],
           **outputs,
       }
       return model_outputs




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							Union[str, Any]   , __lowercase		:							Union[str, Any]=False )		->  Union[str, Any]:
       SCREAMING_SNAKE_CASE__       :			Tuple													=[outputs['''candidate_label'''] for outputs in model_outputs]
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[outputs['''sequence'''] for outputs in model_outputs]
       SCREAMING_SNAKE_CASE__       :			List[Any]													=np.concatenate([output['''logits'''].numpy() for output in model_outputs] )
       SCREAMING_SNAKE_CASE__       :			int													=logits.shape[0]
       SCREAMING_SNAKE_CASE__       :			Dict													=len(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=N // n
       SCREAMING_SNAKE_CASE__       :			List[Any]													=logits.reshape((num_sequences, n, -1) )

       if multi_label or len(__lowercase ) == 1:
             # softmax over the entailment vs. contradiction dim for each label independently
             SCREAMING_SNAKE_CASE__       :			Any													=self.entailment_id
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=-1 if entailment_id == 0 else 0
             SCREAMING_SNAKE_CASE__       :			List[Any]													=reshaped_outputs[..., [contradiction_id, entailment_id]]
             SCREAMING_SNAKE_CASE__       :			int													=np.exp(__lowercase ) / np.exp(__lowercase ).sum(-1   , keepdims=__lowercase )
             SCREAMING_SNAKE_CASE__       :			Tuple													=scores[..., 1]
       else:
             # softmax the "entailment" logits over all candidate labels
             SCREAMING_SNAKE_CASE__       :			Tuple													=reshaped_outputs[..., self.entailment_id]
             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=np.exp(__lowercase ) / np.exp(__lowercase ).sum(-1   , keepdims=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=list(reversed(scores[0].argsort() ) )
       return {
           "sequence": sequences[0],
           "labels": [candidate_labels[i] for i in top_inds],
           "scores": scores[0, top_inds].tolist(),
       }







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					=	{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

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