


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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return 1.0 / (1.0 + np.exp(-_outputs						))
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						np.max(_outputs      ,	axis=-1      ,	keepdims=SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Tuple		 =						np.exp(_outputs - maxes						)
 return shifted_exp / shifted_exp.sum(axis=-1      ,	keepdims=SCREAMING_SNAKE_CASE__						)


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """sigmoid"""
      __snake_case 	=       """softmax"""
      __snake_case 	=       """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 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       False
      __snake_case 	=       ClassificationFunction.NONE




      def __init__(    self:	Any						,				**a:	str    ):
       super().__init__(**a    )

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




      def 					_snake_case     (    self:	Dict						,				a:	Tuple=None						,				a:	str=None						,				a:	Tuple=""						,				**a:	Dict    ):
       # Using "" as default argument because we're going to use `top_k=None` in user code to declare
       # "No top_k"
       __lowerCamelCase		:							Optional[int]		 =						tokenizer_kwargs

       __lowerCamelCase		:							Union[str, Any]		 =						{}
       if hasattr(self.model.config						,				'return_all_scores'    ) and return_all_scores is None:
        __lowerCamelCase		:							Optional[int]		 =						self.model.config.return_all_scores

       if isinstance(a						,				a    ) or top_k is None:
        __lowerCamelCase		:							str		 =						top_k
        __lowerCamelCase		:							Any		 =						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`.'						,				a						,				)
        if return_all_scores:
         __lowerCamelCase		:							Optional[Any]		 =						None
        else:
         __lowerCamelCase		:							str		 =						1

       if isinstance(a						,				a    ):
        __lowerCamelCase		:							Any		 =						ClassificationFunction[function_to_apply.upper()]

       if function_to_apply is not None:
        __lowerCamelCase		:							List[str]		 =						function_to_apply
       return preprocess_params, {}, postprocess_params




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




      def 					_snake_case     (    self:	Optional[int]						,				a:	List[str]						,				**a:	Optional[Any]    ):
       __lowerCamelCase		:							str		 =						self.framework
       if isinstance(a						,				a    ):
        return self.tokenizer(**a						,				return_tensors=a						,				**a    )
       elif isinstance(a						,				a    ) and len(a    ) == 1 and isinstance(inputs[0]						,				a    ) 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=a						,				**a    )
       elif isinstance(a						,				a    ):
        # 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(a						,				return_tensors=a						,				**a    )




      def 					_snake_case     (    self:	Tuple						,				a:	List[str]    ):
       return self.model(**a    )




      def 					_snake_case     (    self:	List[Any]						,				a:	int						,				a:	List[Any]=None						,				a:	List[Any]=1						,				a:	Union[str, Any]=True    ):
       # `_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		:							Optional[int]		 =						ClassificationFunction.SIGMOID
        elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
         __lowerCamelCase		:							Optional[int]		 =						ClassificationFunction.SOFTMAX
        elif hasattr(self.model.config						,				'function_to_apply'    ) and function_to_apply is None:
         __lowerCamelCase		:							Optional[Any]		 =						self.model.config.function_to_apply
        else:
         __lowerCamelCase		:							List[Any]		 =						ClassificationFunction.NONE

       __lowerCamelCase		:							Dict		 =						model_outputs['logits'][0]
       __lowerCamelCase		:							Union[str, Any]		 =						outputs.numpy()

       if function_to_apply == ClassificationFunction.SIGMOID:
        __lowerCamelCase		:							int		 =						sigmoid(a    )
       elif function_to_apply == ClassificationFunction.SOFTMAX:
        __lowerCamelCase		:							Tuple		 =						softmax(a    )
       elif function_to_apply == ClassificationFunction.NONE:
        __lowerCamelCase		:							Optional[int]		 =						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		:							Union[str, Any]		 =						[
           {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a    )
       ]
       if not _legacy:
        dict_scores.sort(key=lambda a    : x["score"]						,				reverse=a    )
        if top_k is not None:
         __lowerCamelCase		:							Any		 =						dict_scores[:top_k]
       return dict_scores





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
       import torch

if is_torch_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm


lowercase_	   =     logging.get_logger(__name__)


@dataclass
class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       [
          """no_inference""",
          """no_cuda""",
          """no_tpu""",
          """no_speed""",
          """no_memory""",
          """no_env_print""",
          """no_multi_process""",
      ]




      def __init__(    self:	Any						,				**a:	Optional[Any]    ):
       for deprecated_arg in self.deprecated_args:
        if deprecated_arg in kwargs:
         __lowerCamelCase		:							str		 =						deprecated_arg[3:]
         setattr(self						,				a						,				not kwargs.pop(a    )    )
         logger.warning(
             F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
             F' {positive_arg}={kwargs[positive_arg]}'    )

       __lowerCamelCase		:							str		 =						kwargs.pop('torchscript'						,				self.torchscript    )
       __lowerCamelCase		:							int		 =						kwargs.pop('torch_xla_tpu_print_metrics'						,				self.torch_xla_tpu_print_metrics    )
       __lowerCamelCase		:							Dict		 =						kwargs.pop('fp16_opt_level'						,				self.fpaa_opt_level    )
       super().__init__(**a    )

      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Trace the models using torchscript"""}   )
      __snake_case 	=       field(default=__UpperCamelCase ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}   )
      __snake_case 	=       field(
          default="""O1""" ,				metadata={
              """help""": (
                  """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
                  """See details at https://nvidia.github.io/apex/amp.html"""
              )
          } ,				)




      @cached_property
      def 					_snake_case     (    self:	Dict    ):
       requires_backends(self						,				['torch']    )
       logger.info('PyTorch: setting up devices'    )
       if not self.cuda:
        __lowerCamelCase		:							Dict		 =						torch.device('cpu'    )
        __lowerCamelCase		:							str		 =						0
       elif is_torch_tpu_available():
        __lowerCamelCase		:							Optional[int]		 =						xm.xla_device()
        __lowerCamelCase		:							Dict		 =						0
       else:
        __lowerCamelCase		:							Any		 =						torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
        __lowerCamelCase		:							Union[str, Any]		 =						torch.cuda.device_count()
       return device, n_gpu




      @property
      def 					_snake_case     (    self:	Optional[Any]    ):
       return is_torch_tpu_available() and self.tpu




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       # TODO(PVP): currently only single GPU is supported
       return torch.cuda.current_device()




      @property
      def 					_snake_case     (    self:	int    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[0]




      @property
      def 					_snake_case     (    self:	Union[str, Any]    ):
       requires_backends(self						,				['torch']    )
       return self._setup_devices[1]




      @property
      def 					_snake_case     (    self:	List[Any]    ):
       return self.n_gpu > 0

