


"""simple docstring"""



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       lowerCamelCase					(a_						:Union[str, Any])     ->							List[Any]:
							return 1.0 / (1.0 + np.exp(-_outputs))






def       lowerCamelCase					(a_						:Tuple)     ->							Union[str, Any]:
							lowercase		:Optional[Any]       							=				np.max(_outputs				,				axis=-1				,				keepdims=a_)
							lowercase		:Union[str, Any]       							=				np.exp(_outputs - maxes)
							return shifted_exp / shifted_exp.sum(axis=-1				,				keepdims=a_)
class 					__magic_name__		(			__UpperCAmelCase     ):
							__A			:      Tuple        =   "sigmoid"
							__A			:      Optional[Any]        =   "softmax"
							__A			:      Any        =   "none"







@add_end_docstrings(
    __UpperCAmelCase			,						R"\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `\"default\"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `\"sigmoid\"`: Applies the sigmoid function on the output.\n            - `\"softmax\"`: Applies the softmax function on the output.\n            - `\"none\"`: Does not apply any function on the output.\n    "			,						)
class 					__magic_name__		(			__UpperCAmelCase     ):
							__A			:      str        =   False
							__A			:      Any        =   ClassificationFunction.NONE






							def __init__(   self  :    Optional[int]  ,     **snake_case__  :    Optional[Any]				):

														'''simple docstring'''






														super().__init__(**snake_case__				)

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






							def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    str=None  ,     snake_case__  :    str=None  ,     snake_case__  :    Dict=""  ,     **snake_case__  :    int				):

														'''simple docstring'''






														lowercase		:Optional[int]       							=				tokenizer_kwargs

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

														if isinstance(snake_case__  ,     snake_case__				) or top_k is None:
																					lowercase		:Tuple       							=				top_k
																					lowercase		: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`.'''  ,     snake_case__  ,     )
																					if return_all_scores:
																												lowercase		:Dict       							=				None
																					else:
																												lowercase		:Any       							=				1

														if isinstance(snake_case__  ,     snake_case__				):
																					lowercase		:List[Any]       							=				ClassificationFunction[function_to_apply.upper()]

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






							def __call__(   self  :    List[Any]  ,     *snake_case__  :    List[str]  ,     **snake_case__  :    int				):

														'''simple docstring'''






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






							def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    Optional[Any]  ,     **snake_case__  :    List[str]				):

														'''simple docstring'''






														lowercase		:str       							=				self.framework
														if isinstance(snake_case__  ,     snake_case__				):
																					return self.tokenizer(**snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__				)
														elif isinstance(snake_case__  ,     snake_case__				) and len(snake_case__				) == 1 and isinstance(inputs[0]  ,     snake_case__				) 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=snake_case__  ,     **snake_case__				)
														elif isinstance(snake_case__  ,     snake_case__				):
																					# 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(snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__				)






							def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    Optional[Any]				):

														'''simple docstring'''






														return self.model(**snake_case__				)







							def  __snake_case						(   self  :    str  ,     snake_case__  :    List[Any]  ,     snake_case__  :    List[str]=None  ,     snake_case__  :    List[Any]=1  ,     snake_case__  :    List[str]=True				):

														'''simple docstring'''






														if function_to_apply is None:
																					if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
																												lowercase		:Union[str, Any]       							=				ClassificationFunction.SIGMOID
																					elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																												lowercase		:Optional[int]       							=				ClassificationFunction.SOFTMAX
																					elif hasattr(self.model.config  ,     '''function_to_apply'''				) and function_to_apply is None:
																												lowercase		:List[Any]       							=				self.model.config.function_to_apply
																					else:
																												lowercase		:Tuple       							=				ClassificationFunction.NONE

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

														if function_to_apply == ClassificationFunction.SIGMOID:
																					lowercase		:int       							=				sigmoid(snake_case__				)
														elif function_to_apply == ClassificationFunction.SOFTMAX:
																					lowercase		:List[Any]       							=				softmax(snake_case__				)
														elif function_to_apply == ClassificationFunction.NONE:
																					lowercase		: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()}

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





"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCAmelCase      		=			'''
Human: <<task>>

Assistant: '''


UpperCAmelCase      		=			'''huggingface-tools/default-prompts'''
UpperCAmelCase      		=			{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s'''				,				a_) is not None:
              return prompt_or_repo_id

       lowercase		:List[str]       							=				cached_file(
           a_				,				PROMPT_FILES[mode]				,				repo_type='''dataset'''				,				user_agent={'''agent''': agent_name})
       with open(a_				,				'''r'''				,				encoding='''utf-8''') as f:
              return f.read()

