

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_      (    _UpperCAmelCase    ):
						return 1.0 / (1.0 + np.exp(-_outputs    ))







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       np.max(_outputs				,	axis=-1				,	keepdims=_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: Dict  		=       np.exp(_outputs - maxes    )
						return shifted_exp / shifted_exp.sum(axis=-1				,	keepdims=_UpperCAmelCase    )



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     '''sigmoid'''
			_UpperCAmelCase   :							str										=     '''softmax'''
			_UpperCAmelCase   :							List[Any]										=     '''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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Optional[int]										=     False
			_UpperCAmelCase   :							Tuple										=     ClassificationFunction.NONE



			def __init__( self  :     Union[str, Any]   ,						**lowerCAmelCase__  :     int):
									super().__init__(**lowerCAmelCase__)

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



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

									SCREAMING_SNAKE_CASE_: Dict  		=       {}
									if hasattr(self.model.config   ,						"return_all_scores") and return_all_scores is None:
															SCREAMING_SNAKE_CASE_: List[str]  		=       self.model.config.return_all_scores

									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__) or top_k is None:
															SCREAMING_SNAKE_CASE_: Any  		=       top_k
															SCREAMING_SNAKE_CASE_: Union[str, 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`."   ,						lowerCAmelCase__   ,						)
															if return_all_scores:
																					SCREAMING_SNAKE_CASE_: int  		=       None
															else:
																					SCREAMING_SNAKE_CASE_: str  		=       1

									if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: List[str]  		=       ClassificationFunction[function_to_apply.upper()]

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



			def __call__( self  :     int   ,						*lowerCAmelCase__  :     List[str]   ,						**lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       super().__call__(*lowerCAmelCase__   ,						**lowerCAmelCase__)
									# TODO try and retrieve it in a nicer way from _sanitize_parameters.
									SCREAMING_SNAKE_CASE_: Tuple  		=       "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        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Optional[Any]   ,						**lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     List[Any]):
									return self.model(**lowerCAmelCase__)




			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Union[str, Any]=None   ,						lowerCAmelCase__  :     Dict=1   ,						lowerCAmelCase__  :     Optional[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:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       ClassificationFunction.SIGMOID
															elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       ClassificationFunction.SOFTMAX
															elif hasattr(self.model.config   ,						"function_to_apply") and function_to_apply is None:
																					SCREAMING_SNAKE_CASE_: Tuple  		=       self.model.config.function_to_apply
															else:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       ClassificationFunction.NONE

									SCREAMING_SNAKE_CASE_: List[Any]  		=       model_outputs["logits"][0]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       outputs.numpy()

									if function_to_apply == ClassificationFunction.SIGMOID:
															SCREAMING_SNAKE_CASE_: List[str]  		=       sigmoid(lowerCAmelCase__)
									elif function_to_apply == ClassificationFunction.SOFTMAX:
															SCREAMING_SNAKE_CASE_: Tuple  		=       softmax(lowerCAmelCase__)
									elif function_to_apply == ClassificationFunction.NONE:
															SCREAMING_SNAKE_CASE_: List[str]  		=       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()}

									SCREAMING_SNAKE_CASE_: Dict  		=       [
									    {"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:
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       dict_scores[:top_k]
									return dict_scores












# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch




class     snake_case__							(TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Any							)					->     Optional[Any]:
							super().__init__(features=__lowerCamelCase							)
							a    =			torch_tensor_kwargs
							import torch  # noqa import torch at initialization





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							import torch

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and column:
										if all(
										    isinstance(__lowerCamelCase					,      torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
										    for x in column							):
													return torch.stack(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							import torch

							if isinstance(__lowerCamelCase					,      (str, bytes, type(__lowerCamelCase							))							):
										return value
							elif isinstance(__lowerCamelCase					,      (np.character, np.ndarray)							) and np.issubdtype(value.dtype					,      np.character							):
										return value.tolist()

							a    =			{}

							if isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.integer							):
										a    =			{"dtype": torch.intaa}
							elif isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.floating							):
										a    =			{"dtype": torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(__lowerCamelCase					,      PIL.Image.Image							):
													a    =			np.asarray(__lowerCamelCase							)
							return torch.tensor(__lowerCamelCase					,      **{**default_dtype, **self.torch_tensor_kwargs}							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(__lowerCamelCase					,      "__array__"							) and not isinstance(__lowerCamelCase					,      torch.Tensor							):
										a    =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(__lowerCamelCase					,      np.ndarray							):
										if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							elif isinstance(__lowerCamelCase					,      (list, tuple)							):
										return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							return self._tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     dict							)					->     str:
							return map_nested(self._recursive_tensorize					,      __lowerCamelCase					,      map_list=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_row(__lowerCamelCase							)
							return self.recursive_tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     pa.Table							)					->     "torch.Tensor":
							a    =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_column(__lowerCamelCase					,      pa_table.column_names[0]							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							a    =			self._consolidate(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_batch(__lowerCamelCase							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							for column_name in batch:
										a    =			self._consolidate(batch[column_name]							)
							return batch






