import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def        lowerCAmelCase_			(					*__A							)					->    List[Any]:







      '''simple docstring'''


      if not isinstance(__A,  __A							):
            UpperCAmelCase__								=  list(__A							)
      for i in range(len(__A							)							):
            UpperCAmelCase__								=  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        lowerCAmelCase_			(					__A							)					->    bool:







      '''simple docstring'''


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







def        lowerCAmelCase_			(					__A = None,  __A = 128							)					->    Tuple:







      '''simple docstring'''


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

      UpperCAmelCase__								=  starting_batch_size

      def decorator(*__A,  **__A							):
            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()
            UpperCAmelCase__								=  list(inspect.signature(__A							).parameters.keys()							)
            # Guard against user error
            if len(__A							) < (len(__A							) + 1):
                  UpperCAmelCase__								=  ", ".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(__A,  *__A,  **__A							)
                  except Exception as e:
                        if should_reduce_batch_size(__A							):
                              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




# 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 torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'facebook/bart-large-mnli'
   __UpperCAmelCase			:			Optional[Any]              =      (
       'This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '
       'should be the text to classify, and `labels`, which should be the list of labels to use for classification. '
       'It returns the most likely label in the list of provided `labels` for the input text.'
   )
   __UpperCAmelCase			:			Optional[int]              =      'text_classifier'
   __UpperCAmelCase			:			int              =      AutoTokenizer
   __UpperCAmelCase			:			Dict              =      AutoModelForSequenceClassification

   __UpperCAmelCase			:			int              =      ['text', ['text']]
   __UpperCAmelCase			:			Optional[int]              =      ['text']


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         super().setup()
         UpperCAmelCase__								=  self.model.config
         UpperCAmelCase__								=  -1
         for idx, label in config.idalabel.items():
               if label.lower().startswith("entail"		):
                     UpperCAmelCase__								=  int(__UpperCAmelCase		)
         if self.entailment_id == -1:
               raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  labels
         return self.pre_processor(
             [text] * len(__UpperCAmelCase		)	,  [f"""This example is {label}""" for label in labels]	,  return_tensors="pt"	,  padding="max_length"	,  )





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  outputs.logits
         UpperCAmelCase__								=  torch.argmax(logits[:, 2]		).item()
         return self._labels[label_id]



