




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 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					return 1.0 / (1.0 + np.exp(-_outputs				))







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							np.max(_outputs							,axis=-1							,keepdims=__A				)
					__magic_name__							:	int    							=							np.exp(_outputs - maxes				)
					return shifted_exp / shifted_exp.sum(axis=-1							,keepdims=__A				)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								='''sigmoid'''
			UpperCamelCase__								='''softmax'''
			UpperCamelCase__								='''none'''






@add_end_docstrings(
    _lowerCamelCase				,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    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=False
			UpperCamelCase__								=ClassificationFunction.NONE

			def __init__( self			:   Tuple       ,						**lowerCamelCase_			:   Tuple  )						->   List[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 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   Tuple=None       ,						lowerCamelCase_			:   Dict=None       ,						lowerCamelCase_			:   Optional[int]=""       ,						**lowerCamelCase_			:   int  )						->   str:
								# Using "" as default argument because we're going to use `top_k=None` in user code to declare
								# "No top_k"
								__magic_name__							:	Dict    							=							tokenizer_kwargs

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

								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) or top_k is None:
													__magic_name__							:	Dict    							=							top_k
													__magic_name__							:	List[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:
																		__magic_name__							:	str    							=							None
													else:
																		__magic_name__							:	int    							=							1

								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ):
													__magic_name__							:	str    							=							ClassificationFunction[function_to_apply.upper()]

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

			def __call__( self			:   Optional[Any]       ,						*lowerCamelCase_			:   Union[str, Any]       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Optional[int]    							=							super().__call__(*lowerCamelCase_       ,						**lowerCamelCase_  )
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								__magic_name__							:	int    							=							'''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 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Any       ,						**lowerCamelCase_			:   int  )						->   Dict[str, GenericTensor]:
								__magic_name__							:	Union[str, 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 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Dict  )						->   Union[str, Any]:
								return self.model(**lowerCamelCase_  )


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

								__magic_name__							:	Any    							=							model_outputs['''logits'''][0]
								__magic_name__							:	Optional[int]    							=							outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
													__magic_name__							:	List[str]    							=							sigmoid(lowerCamelCase_  )
								elif function_to_apply == ClassificationFunction.SOFTMAX:
													__magic_name__							:	Optional[int]    							=							softmax(lowerCamelCase_  )
								elif function_to_apply == ClassificationFunction.NONE:
													__magic_name__							:	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()}

								__magic_name__							:	Union[str, Any]    							=							[
								    {'''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:
																		__magic_name__							:	Dict    							=							dict_scores[:top_k]
								return dict_scores









import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


__lowerCamelCase   :						int				=			NewType('''DataClass''', Any)
__lowerCamelCase   :						Optional[Any]				=			NewType('''DataClassType''', Any)







def 		lowercase__   (		__A:	List[Any]				):





					'''simple docstring'''






					if isinstance(__A							,__A				):
										return v
					if v.lower() in ("yes", "true", "t", "y", "1"):
										return True
					elif v.lower() in ("no", "false", "f", "n", "0"):
										return False
					else:
										raise ArgumentTypeError(
										    F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''				)







def 		lowercase__   (		__A:	list				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							{str(__A				): choice for choice in choices}
					return lambda __A				: str_to_choice.get(__A							,__A				)







def 		lowercase__   (		*,
    __A:	Union[str, List[str]] = None							,__A:	str = None							,__A:	Any = dataclasses.MISSING							,__A:	Callable[[], Any] = dataclasses.MISSING							,__A:	dict = None							,**__A:	Optional[int]							,):





					'''simple docstring'''






					if metadata is None:
										# Important, don't use as default param in function signature because dict is mutable and shared across function calls
										__magic_name__							:	Optional[Any]    							=							{}
					if aliases is not None:
										__magic_name__							:	str    							=							aliases
					if help is not None:
										__magic_name__							:	Optional[int]    							=							help

					return dataclasses.field(metadata=__A							,default=__A							,default_factory=__A							,**__A				)


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=42

			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   Union[DataClassType, Iterable[DataClassType]]       ,						**lowerCamelCase_			:   str  )						->   Optional[Any]:
								# To make the default appear when using --help
								if "formatter_class" not in kwargs:
													__magic_name__							:	List[str]    							=							ArgumentDefaultsHelpFormatter
								super().__init__(**lowerCamelCase_  )
								if dataclasses.is_dataclass(lowerCamelCase_  ):
													__magic_name__							:	Union[str, Any]    							=							[dataclass_types]
								__magic_name__							:	Tuple    							=							list(lowerCamelCase_  )
								for dtype in self.dataclass_types:
													self._add_dataclass_arguments(lowerCamelCase_  )

			@staticmethod
			def 							UpperCAmelCase__       ( lowerCamelCase_			:   ArgumentParser       ,						lowerCamelCase_			:   dataclasses.Field  )						->   str:
								__magic_name__							:	int    							=							F'''--{field.name}'''
								__magic_name__							:	str    							=							field.metadata.copy()
								# field.metadata is not used at all by Data Classes,
								# it is provided as a third-party extension mechanism.
								if isinstance(field.type       ,						lowerCamelCase_  ):
													raise RuntimeError(
													    '''Unresolved type detected, which should have been done with the help of '''
													    '''`typing.get_type_hints` method by default'''  )

								__magic_name__							:	Union[str, Any]    							=							kwargs.pop('''aliases'''       ,						[]  )
								if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ):
													__magic_name__							:	Tuple    							=							[aliases]

								__magic_name__							:	Optional[int]    							=							getattr(field.type       ,						'''__origin__'''       ,						field.type  )
								if origin_type is Union or (hasattr(lowerCamelCase_       ,						'''UnionType'''  ) and isinstance(lowerCamelCase_       ,						types.UnionType  )):
													if str not in field.type.__args__ and (
													    len(field.type.__args__  ) != 2 or type(lowerCamelCase_  ) not in field.type.__args__
													):
																		raise ValueError(
																		    '''Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'''
																		    ''' the argument parser only supports one type per argument.'''
																		    F''' Problem encountered in field \'{field.name}\'.'''  )
													if type(lowerCamelCase_  ) not in field.type.__args__:
																		# filter `str` in Union
																		__magic_name__							:	int    							=							field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																		__magic_name__							:	str    							=							getattr(field.type       ,						'''__origin__'''       ,						field.type  )
													elif bool not in field.type.__args__:
																		# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																		__magic_name__							:	List[str]    							=							(
																		    field.type.__args__[0] if isinstance(lowerCamelCase_       ,						field.type.__args__[1]  ) else field.type.__args__[1]
																		)
																		__magic_name__							:	Union[str, Any]    							=							getattr(field.type       ,						'''__origin__'''       ,						field.type  )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
								__magic_name__							:	Any    							=							{}
								if origin_type is Literal or (isinstance(field.type       ,						lowerCamelCase_  ) and issubclass(field.type       ,						lowerCamelCase_  )):
													if origin_type is Literal:
																		__magic_name__							:	Optional[int]    							=							field.type.__args__
													else:
																		__magic_name__							:	Dict    							=							[x.value for x in field.type]

													__magic_name__							:	Union[str, Any]    							=							make_choice_type_function(kwargs['''choices''']  )

													if field.default is not dataclasses.MISSING:
																		__magic_name__							:	List[Any]    							=							field.default
													else:
																		__magic_name__							:	List[str]    							=							True
								elif field.type is bool or field.type == Optional[bool]:
													# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
													# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
													__magic_name__							:	Union[str, Any]    							=							copy(lowerCamelCase_  )

													# Hack because type=bool in argparse does not behave as we want.
													__magic_name__							:	str    							=							string_to_bool
													if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
																		# Default value is False if we have no default when of type bool.
																		__magic_name__							:	Union[str, Any]    							=							False if field.default is dataclasses.MISSING else field.default
																		# This is the value that will get picked if we don't include --field_name in any way
																		__magic_name__							:	int    							=							default
																		# This tells argparse we accept 0 or 1 value after --field_name
																		__magic_name__							:	Tuple    							=							'''?'''
																		# This is the value that will get picked if we do --field_name (without value)
																		__magic_name__							:	Any    							=							True
								elif isclass(lowerCamelCase_  ) and issubclass(lowerCamelCase_       ,						lowerCamelCase_  ):
													__magic_name__							:	Tuple    							=							field.type.__args__[0]
													__magic_name__							:	List[str]    							=							'''+'''
													if field.default_factory is not dataclasses.MISSING:
																		__magic_name__							:	int    							=							field.default_factory()
													elif field.default is dataclasses.MISSING:
																		__magic_name__							:	str    							=							True
								else:
													__magic_name__							:	Tuple    							=							field.type
													if field.default is not dataclasses.MISSING:
																		__magic_name__							:	str    							=							field.default
													elif field.default_factory is not dataclasses.MISSING:
																		__magic_name__							:	Any    							=							field.default_factory()
													else:
																		__magic_name__							:	Any    							=							True
								parser.add_argument(lowerCamelCase_       ,						*lowerCamelCase_       ,						**lowerCamelCase_  )

								# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
								# Order is important for arguments with the same destination!
								# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
								# here and we do not need those changes/additional keys.
								if field.default is True and (field.type is bool or field.type == Optional[bool]):
													__magic_name__							:	Dict    							=							False
													parser.add_argument(F'''--no_{field.name}'''       ,						action='''store_false'''       ,						dest=field.name       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   DataClassType  )						->   Optional[int]:
								if hasattr(lowerCamelCase_       ,						'''_argument_group_name'''  ):
													__magic_name__							:	Tuple    							=							self.add_argument_group(dtype._argument_group_name  )
								else:
													__magic_name__							:	Any    							=							self

								try:
													__magic_name__							:	Dict[str, type]    							=							get_type_hints(lowerCamelCase_  )
								except NameError:
													raise RuntimeError(
													    F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
													    '''removing line of `from __future__ import annotations` which opts in Postponed '''
													    '''Evaluation of Annotations (PEP 563)'''  )
								except TypeError as ex:
													# Remove this block when we drop Python 3.9 support
													if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(lowerCamelCase_  ):
																		__magic_name__							:	Any    							=							'''.'''.join(map(lowerCamelCase_       ,						sys.version_info[:3]  )  )
																		raise RuntimeError(
																		    F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
																		    '''line of `from __future__ import annotations` which opts in union types as '''
																		    '''`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '''
																		    '''support Python versions that lower than 3.10, you need to use '''
																		    '''`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '''
																		    '''`X | None`.'''  ) from ex
													raise

								for field in dataclasses.fields(lowerCamelCase_  ):
													if not field.init:
																		continue
													__magic_name__							:	Tuple    							=							type_hints[field.name]
													self._parse_dataclass_field(lowerCamelCase_       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Union[str, Any]=None       ,						lowerCamelCase_			:   List[str]=False       ,						lowerCamelCase_			:   List[Any]=True       ,						lowerCamelCase_			:   Union[str, Any]=None       ,						lowerCamelCase_			:   List[Any]=None       ,						)						->   Tuple[DataClass, ...]:

								if args_file_flag or args_filename or (look_for_args_file and len(sys.argv  )):
													__magic_name__							:	int    							=							[]

													if args_filename:
																		args_files.append(Path(lowerCamelCase_  )  )
													elif look_for_args_file and len(sys.argv  ):
																		args_files.append(Path(sys.argv[0]  ).with_suffix('''.args'''  )  )

													# args files specified via command line flag should overwrite default args files so we add them last
													if args_file_flag:
																		# Create special parser just to extract the args_file_flag values
																		__magic_name__							:	str    							=							ArgumentParser()
																		args_file_parser.add_argument(lowerCamelCase_       ,						type=lowerCamelCase_       ,						action='''append'''  )

																		# Use only remaining args for further parsing (remove the args_file_flag)
																		__magic_name__							,    __magic_name__							:	List[str]    							=							args_file_parser.parse_known_args(args=lowerCamelCase_  )
																		__magic_name__							:	List[Any]    							=							vars(lowerCamelCase_  ).get(args_file_flag.lstrip('''-'''  )       ,						lowerCamelCase_  )

																		if cmd_args_file_paths:
																							args_files.extend([Path(lowerCamelCase_  ) for p in cmd_args_file_paths]  )

													__magic_name__							:	List[str]    							=							[]
													for args_file in args_files:
																		if args_file.exists():
																							file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
													__magic_name__							:	Optional[int]    							=							file_args + args if args is not None else file_args + sys.argv[1:]
								__magic_name__							,    __magic_name__							:	Tuple    							=							self.parse_known_args(args=lowerCamelCase_  )
								__magic_name__							:	Any    							=							[]
								for dtype in self.dataclass_types:
													__magic_name__							:	str    							=							{f.name for f in dataclasses.fields(lowerCamelCase_  ) if f.init}
													__magic_name__							:	Tuple    							=							{k: v for k, v in vars(lowerCamelCase_  ).items() if k in keys}
													for k in keys:
																		delattr(lowerCamelCase_       ,						lowerCamelCase_  )
													__magic_name__							:	Optional[Any]    							=							dtype(**lowerCamelCase_  )
													outputs.append(lowerCamelCase_  )
								if len(namespace.__dict__  ) > 0:
													# additional namespace.
													outputs.append(lowerCamelCase_  )
								if return_remaining_strings:
													return (*outputs, remaining_args)
								else:
													if remaining_args:
																		raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''  )

													return (*outputs,)

			def 							UpperCAmelCase__       ( self			:   int       ,						lowerCamelCase_			:   Dict[str, Any]       ,						lowerCamelCase_			:   bool = False  )						->   Tuple[DataClass, ...]:
								__magic_name__							:	int    							=							set(args.keys()  )
								__magic_name__							:	Any    							=							[]
								for dtype in self.dataclass_types:
													__magic_name__							:	int    							=							{f.name for f in dataclasses.fields(lowerCamelCase_  ) if f.init}
													__magic_name__							:	List[str]    							=							{k: v for k, v in args.items() if k in keys}
													unused_keys.difference_update(inputs.keys()  )
													__magic_name__							:	Optional[Any]    							=							dtype(**lowerCamelCase_  )
													outputs.append(lowerCamelCase_  )
								if not allow_extra_keys and unused_keys:
													raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(lowerCamelCase_  )}'''  )
								return tuple(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   bool = False  )						->   Tuple[DataClass, ...]:
								with open(Path(lowerCamelCase_  )       ,						encoding='''utf-8'''  ) as open_json_file:
													__magic_name__							:	Any    							=							json.loads(open_json_file.read()  )
								__magic_name__							:	Tuple    							=							self.parse_dict(lowerCamelCase_       ,						allow_extra_keys=lowerCamelCase_  )
								return tuple(lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   bool = False  )						->   Tuple[DataClass, ...]:
								__magic_name__							:	Any    							=							self.parse_dict(yaml.safe_load(Path(lowerCamelCase_  ).read_text()  )       ,						allow_extra_keys=lowerCamelCase_  )
								return tuple(lowerCamelCase_  )



