




'''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 __UpperCAmelCase	(   _UpperCAmelCase		:   Dict						)      ->      List[str]:
			return 1.0 / (1.0 + np.exp(-_outputs						))


def __UpperCAmelCase	(   _UpperCAmelCase		:   Any						)      ->      List[str]:
			__snake_case			=		np.max(_outputs		,				axis=-1		,				keepdims=__lowerCamelCase						)
			__snake_case			=		np.exp(_outputs - maxes						)
			return shifted_exp / shifted_exp.sum(axis=-1		,				keepdims=__lowerCamelCase						)



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			"""sigmoid"""
			__SCREAMING_SNAKE_CASE    =			"""softmax"""
			__SCREAMING_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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			False
			__SCREAMING_SNAKE_CASE    =			ClassificationFunction.NONE





			def __init__(      self						:    List[str]      ,		**a_						:    List[Any]							):



						"""simple docstring"""




						super().__init__(**a_							)

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





			def 							A (      self						:    Any      ,		a_						:    int=None      ,		a_						:    Any=None      ,		a_						:    Union[str, Any]=""      ,		**a_						:    Tuple							):



						"""simple docstring"""




						__snake_case			=		tokenizer_kwargs

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

						if isinstance(a_      ,		a_							) or top_k is None:
									__snake_case			=		top_k
									__snake_case			=		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:
												__snake_case			=		None
									else:
												__snake_case			=		1

						if isinstance(a_      ,		a_							):
									__snake_case			=		ClassificationFunction[function_to_apply.upper()]

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





			def __call__(      self						:    Dict      ,		*a_						:    Optional[int]      ,		**a_						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		super().__call__(*a_      ,		**a_							)
						# TODO try and retrieve it in a nicer way from _sanitize_parameters.
						__snake_case			=		"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 							A (      self						:    Union[str, Any]      ,		a_						:    Optional[Any]      ,		**a_						:    Optional[Any]							):



						"""simple docstring"""




						__snake_case			=		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 							A (      self						:    List[str]      ,		a_						:    Tuple							):



						"""simple docstring"""




						return self.model(**a_							)






			def 							A (      self						:    Dict      ,		a_						:    Union[str, Any]      ,		a_						:    int=None      ,		a_						:    Union[str, Any]=1      ,		a_						:    Tuple=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:
												__snake_case			=		ClassificationFunction.SIGMOID
									elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
												__snake_case			=		ClassificationFunction.SOFTMAX
									elif hasattr(self.model.config      ,		"function_to_apply"							) and function_to_apply is None:
												__snake_case			=		self.model.config.function_to_apply
									else:
												__snake_case			=		ClassificationFunction.NONE

						__snake_case			=		model_outputs["logits"][0]
						__snake_case			=		outputs.numpy()

						if function_to_apply == ClassificationFunction.SIGMOID:
									__snake_case			=		sigmoid(a_							)
						elif function_to_apply == ClassificationFunction.SOFTMAX:
									__snake_case			=		softmax(a_							)
						elif function_to_apply == ClassificationFunction.NONE:
									__snake_case			=		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()}

						__snake_case			=		[
						    {"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:
												__snake_case			=		dict_scores[:top_k]
						return dict_scores











import operator as op


__UpperCamelCase   :			Optional[Any]          = "scaler.pt"
__UpperCamelCase   :			Optional[Any]          = "pytorch_model"
__UpperCamelCase   :			str          = "random_states"
__UpperCamelCase   :			Optional[int]          = "optimizer"
__UpperCamelCase   :			Optional[int]          = "scheduler"
__UpperCamelCase   :			str          = "pytorch_model.bin"
__UpperCamelCase   :			List[str]          = "pytorch_model.bin.index.json"
__UpperCamelCase   :			List[str]          = "model.safetensors"
__UpperCamelCase   :			Optional[int]          = "model.safetensors.index.json"
__UpperCamelCase   :			List[str]          = "1.10.2"
__UpperCamelCase   :			Dict          = "py38"
__UpperCamelCase   :			List[str]          = "4.17.0"
__UpperCamelCase   :			Any          = ["ml.p3.16xlarge", "ml.p3dn.24xlarge", "ml.p4dn.24xlarge"]
__UpperCamelCase   :			Any          = ["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "HYBRID_SHARD_ZERO2"]
__UpperCamelCase   :			int          = ["TRANSFORMER_BASED_WRAP", "SIZE_BASED_WRAP", "NO_WRAP"]
__UpperCamelCase   :			Dict          = ["BACKWARD_PRE", "BACKWARD_POST", "NO_PREFETCH"]
__UpperCamelCase   :			str          = ["FULL_STATE_DICT", "LOCAL_STATE_DICT", "SHARDED_STATE_DICT"]
__UpperCamelCase   :			List[Any]          = "2.0.1"
__UpperCamelCase   :			int          = ["pdsh", "standard", "openmpi", "mvapich"]
__UpperCamelCase   :			List[str]          = ["default", "reduce-overhead", "max-autotune"]

__UpperCamelCase   :			List[Any]          = {">": op.gt, ">=": op.ge, "==": op.eq, "!=": op.ne, "<=": op.le, "<": op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase   :			List[Any]          = [
    "nnodes",
    "nproc_per_node",
    "rdzv_backend",
    "rdzv_endpoint",
    "rdzv_id",
    "rdzv_conf",
    "standalone",
    "max_restarts",
    "monitor_interval",
    "start_method",
    "role",
    "module",
    "m",
    "no_python",
    "run_path",
    "log_dir",
    "r",
    "redirects",
    "t",
    "tee",
    "node_rank",
    "master_addr",
    "master_port",
]

__UpperCamelCase   :			List[str]          = ["DEEPSPEED", "MULTI_GPU", "FSDP", "MEGATRON_LM"]
__UpperCamelCase   :			int          = ["DEEPSPEED", "MULTI_XPU", "FSDP"]

