


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_  (     _UpperCamelCase  ):


			'''simple docstring'''







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


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   np.max(_outputs       ,			axis=-1       ,			keepdims=_UpperCamelCase  )
			__lowercase 					=   np.exp(_outputs - maxes  )
			return shifted_exp / shifted_exp.sum(axis=-1       ,			keepdims=_UpperCamelCase  )
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"sigmoid"
					__UpperCAmelCase            =				"softmax"
					__UpperCAmelCase            =				"none"




@add_end_docstrings(
    lowerCAmelCase__     ,     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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				False
					__UpperCAmelCase            =				ClassificationFunction.NONE






					def __init__(				self ,       **snake_case_						)   ->			Union[str, 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 A      (				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_="" ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   tokenizer_kwargs

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

								if isinstance(snake_case_ ,       snake_case_						) or top_k is None:
											__lowercase 					=   top_k
											__lowercase 					=   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 					=   None
											else:
														__lowercase 					=   1

								if isinstance(snake_case_ ,       snake_case_						):
											__lowercase 					=   ClassificationFunction[function_to_apply.upper()]

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






					def __call__(				self ,       *snake_case_ ,       **snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   super().__call__(*snake_case_ ,       **snake_case_						)
								# TODO try and retrieve it in a nicer way from _sanitize_parameters.
								__lowercase 					=   '''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 A      (				self ,       snake_case_ ,       **snake_case_						)   ->			Dict[str, GenericTensor]:







								'''simple docstring'''

								__lowercase 					=   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 A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								return self.model(**snake_case_						)






					def A      (				self ,       snake_case_ ,       snake_case_=None ,       snake_case_=1 ,       snake_case_=True						)   ->			Any:







								'''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 					=   ClassificationFunction.SIGMOID
											elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
														__lowercase 					=   ClassificationFunction.SOFTMAX
											elif hasattr(self.model.config ,       '''function_to_apply'''						) and function_to_apply is None:
														__lowercase 					=   self.model.config.function_to_apply
											else:
														__lowercase 					=   ClassificationFunction.NONE

								__lowercase 					=   model_outputs['''logits'''][0]
								__lowercase 					=   outputs.numpy()

								if function_to_apply == ClassificationFunction.SIGMOID:
											__lowercase 					=   sigmoid(snake_case_						)
								elif function_to_apply == ClassificationFunction.SOFTMAX:
											__lowercase 					=   softmax(snake_case_						)
								elif function_to_apply == ClassificationFunction.NONE:
											__lowercase 					=   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 					=   [
								    {'''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 					=   dict_scores[:top_k]
								return dict_scores






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		lowercase_  (     *_UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
						__lowercase 					=   list(_UpperCamelCase  )
			for i in range(len(_UpperCamelCase  )  ):
						__lowercase 					=   None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ) and len(exception.args  ) == 1:
						return any(err in exception.args[0] for err in _statements  )
			return False





def 		lowercase_  (     _UpperCamelCase = None       ,			_UpperCamelCase = 1_28  ):


			'''simple docstring'''







			if function is None:
						return functools.partial(_UpperCamelCase       ,			starting_batch_size=_UpperCamelCase  )

			__lowercase 					=   starting_batch_size

			def decorator(*_UpperCamelCase       ,			**_UpperCamelCase  ):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						__lowercase 					=   list(inspect.signature(_UpperCamelCase  ).parameters.keys()  )
						# Guard against user error
						if len(_UpperCamelCase  ) < (len(_UpperCamelCase  ) + 1):
									__lowercase 					=   ''', '''.join([F'{arg}={value}' for arg, value in zip(params[1:]       ,			args[1:]  )]  )
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'  )
						while True:
									if batch_size == 0:
												raise RuntimeError('''No executable batch size found, reached zero.'''  )
									try:
												return function(_UpperCamelCase       ,			*_UpperCamelCase       ,			**_UpperCamelCase  )
									except Exception as e:
												if should_reduce_batch_size(_UpperCamelCase  ):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator


