





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

def     a_       (					lowerCAmelCase_ :     int      ):
   return 1.0 / (1.0 + np.exp(-_outputs      ))

def     a_       (					lowerCAmelCase_ :     List[str]      ):
   __lowerCAmelCase				      =      np.max(_outputs,      axis=-1,      keepdims=lowerCAmelCase_      )
   __lowerCAmelCase				      =      np.exp(_outputs - maxes      )
   return shifted_exp / shifted_exp.sum(axis=-1,      keepdims=lowerCAmelCase_      )



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""sigmoid"""
  a_              =					"""softmax"""
  a_              =					"""none"""





@add_end_docstrings(
    _UpperCamelCase						,       R"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """						,       )
class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					False
  a_              =					ClassificationFunction.NONE






  def __init__(							self					:						str   ,    **lowerCAmelCase_					:						Any	)				->		Any:
     super().__init__(**lowerCAmelCase_	)

     self.check_model_type(
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
         if self.framework == 'tf'
         else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING	)






  def 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						Union[str, Any]=None   ,    lowerCAmelCase_					:						Any=""   ,    **lowerCAmelCase_					:						List[Any]	)				->		Union[str, Any]:
     # Using "" as default argument because we're going to use `top_k=None` in user code to declare
     # "No top_k"
     __lowerCAmelCase				      =      tokenizer_kwargs

     __lowerCAmelCase				      =      {}
     if hasattr(self.model.config   ,    'return_all_scores'	) and return_all_scores is None:
        __lowerCAmelCase				      =      self.model.config.return_all_scores

     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) or top_k is None:
        __lowerCAmelCase				      =      top_k
        __lowerCAmelCase				      =      False
     elif return_all_scores is not None:
        warnings.warn(
            '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
            ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'   ,    lowerCAmelCase_   ,    )
        if return_all_scores:
           __lowerCAmelCase				      =      None
        else:
           __lowerCAmelCase				      =      1

     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        __lowerCAmelCase				      =      ClassificationFunction[function_to_apply.upper()]

     if function_to_apply is not None:
        __lowerCAmelCase				      =      function_to_apply
     return preprocess_params, {}, postprocess_params






  def __call__(							self					:						Union[str, Any]   ,    *lowerCAmelCase_					:						Tuple   ,    **lowerCAmelCase_					:						Any	)				->		List[Any]:
     __lowerCAmelCase				      =      super().__call__(*lowerCAmelCase_   ,    **lowerCAmelCase_	)
     # TODO try and retrieve it in a nicer way from _sanitize_parameters.
     __lowerCAmelCase				      =      'top_k' not in kwargs
     if isinstance(args[0]   ,    lowerCAmelCase_	) and _legacy:
        # This pipeline is odd, and return a list when single item is run
        return [result]
     else:
        return result






  def 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						Any   ,    **lowerCAmelCase_					:						Optional[Any]	)				->		Dict[str, GenericTensor]:
     __lowerCAmelCase				      =      self.framework
     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        return self.tokenizer(**lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_	)
     elif isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) and len(lowerCAmelCase_	) == 1 and isinstance(inputs[0]   ,    lowerCAmelCase_	) and len(inputs[0]	) == 2:
        # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
        return self.tokenizer(
            text=inputs[0][0]   ,    text_pair=inputs[0][1]   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_	)
     elif isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        # This is likely an invalid usage of the pipeline attempting to pass text pairs.
        raise ValueError(
            'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
            ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'	)
     return self.tokenizer(lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Any	)				->		Union[str, Any]:
     return self.model(**lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Tuple=None   ,    lowerCAmelCase_					:						Optional[Any]=1   ,    lowerCAmelCase_					:						List[str]=True	)				->		Optional[Any]:
     # `_legacy` is used to determine if we're running the naked pipeline and in backward
     # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
     # the more natural result containing the list.
     # Default value before `set_parameters`
     if function_to_apply is None:
        if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
           __lowerCAmelCase				      =      ClassificationFunction.SIGMOID
        elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
           __lowerCAmelCase				      =      ClassificationFunction.SOFTMAX
        elif hasattr(self.model.config   ,    'function_to_apply'	) and function_to_apply is None:
           __lowerCAmelCase				      =      self.model.config.function_to_apply
        else:
           __lowerCAmelCase				      =      ClassificationFunction.NONE

     __lowerCAmelCase				      =      model_outputs['logits'][0]
     __lowerCAmelCase				      =      outputs.numpy()

     if function_to_apply == ClassificationFunction.SIGMOID:
        __lowerCAmelCase				      =      sigmoid(lowerCAmelCase_	)
     elif function_to_apply == ClassificationFunction.SOFTMAX:
        __lowerCAmelCase				      =      softmax(lowerCAmelCase_	)
     elif function_to_apply == ClassificationFunction.NONE:
        __lowerCAmelCase				      =      outputs
     else:
        raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""	)

     if top_k == 1 and _legacy:
        return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

     __lowerCAmelCase				      =      [
         {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(lowerCAmelCase_	)
     ]
     if not _legacy:
        dict_scores.sort(key=lambda lowerCAmelCase_	: x["score"]   ,    reverse=lowerCAmelCase_	)
        if top_k is not None:
           __lowerCAmelCase				      =      dict_scores[:top_k]
     return dict_scores







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
   from ...onnx.config import PatchingSpec
   from ...tokenization_utils_base import PreTrainedTokenizerBase


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Dict      							=				{
    'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
    'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
    'allenai/longformer-large-4096-finetuned-triviaqa': (
        'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
    ),
    'allenai/longformer-base-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
    'allenai/longformer-large-4096-extra.pos.embd.only': (
        'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
    ),
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "longformer"



 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[List[int], int] = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 1							,							_lowerCAmelCase    :						int = 0							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 30_522							,							_lowerCAmelCase    :						int = 768							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 3_072							,							_lowerCAmelCase    :						str = "gelu"							,							_lowerCAmelCase    :						float = 0.1							,							_lowerCAmelCase    :						float = 0.1							,							_lowerCAmelCase    :						int = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						float = 0.02							,							_lowerCAmelCase    :						float = 1E-12							,							_lowerCAmelCase    :						bool = False							,							**_lowerCAmelCase    :						Union[str, Any]							,							):
       super().__init__(pad_token_id=_lowerCAmelCase							,							**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     attention_window
       SCREAMING_SNAKE_CASE_      		=     sep_token_id
       SCREAMING_SNAKE_CASE_      		=     bos_token_id
       SCREAMING_SNAKE_CASE_      		=     eos_token_id
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     hidden_size
       SCREAMING_SNAKE_CASE_      		=     num_hidden_layers
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     intermediate_size
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     type_vocab_size
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       SCREAMING_SNAKE_CASE_      		=     onnx_export





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						"PretrainedConfig"							,							_lowerCAmelCase    :						str = "default"							,							_lowerCAmelCase    :						"List[PatchingSpec]" = None		):
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     True



 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('global_attention_mask', dynamic_axis),
           ]		)



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     super().outputs
       if self.task == "default":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch'}
       return outputs



 @property
 def  lowerCAmelCase_    (				self    :						str		):
       return 1E-4



 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset							,							14		)




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						"PreTrainedTokenizerBase"							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						Optional[TensorType] = None							,							):
       SCREAMING_SNAKE_CASE_      		=     super().generate_dummy_inputs(
           preprocessor=_lowerCAmelCase							,							batch_size=_lowerCAmelCase							,							seq_length=_lowerCAmelCase							,							is_pair=_lowerCAmelCase							,							framework=_lowerCAmelCase		)
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE_      		=     torch.zeros_like(inputs['input_ids']		)
       # make every second token global
       SCREAMING_SNAKE_CASE_      		=     1

       return inputs