
import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=()					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__="no"					,	SCREAMING_SNAKE_CASE__="29500"				) ->    Union[str, Any]:
       lowercase							:     str       	=      False
       lowercase							:     List[Any]       	=      False
       if any(key.startswith("""KAGGLE"""				) for key in os.environ.keys()				):
              lowercase							:     Tuple       	=      True
       elif "IPython" in sys.modules:
              lowercase							:     Dict       	=      """google.colab""" in str(sys.modules["""IPython"""].get_ipython()				)

       try:
              lowercase							:     List[str]       	=      PrecisionType(mixed_precision.lower()				)
       except ValueError:
              raise ValueError(
                  f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."				)

       if (in_colab or in_kaggle) and (os.environ.get("""TPU_NAME"""					,	SCREAMING_SNAKE_CASE__				) is not None):
              # TPU launch
              import torch_xla.distributed.xla_multiprocessing as xmp

              if len(AcceleratorState._shared_state				) > 0:
                     raise ValueError(
                         """To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside """
                         """your training function. Restart your notebook and make sure no cells initializes an """
                         """`Accelerator`."""				)
              if num_processes is None:
                     lowercase							:     List[Any]       	=      8

              lowercase							:     List[str]       	=      PrepareForLaunch(SCREAMING_SNAKE_CASE__					,	distributed_type="""TPU"""				)
              print(f"Launching a training on {num_processes} TPU cores."				)
              xmp.spawn(SCREAMING_SNAKE_CASE__					,	args=SCREAMING_SNAKE_CASE__					,	nprocs=SCREAMING_SNAKE_CASE__					,	start_method="""fork"""				)
       elif in_colab:
              # No need for a distributed launch otherwise as it's either CPU or one GPU.
              if torch.cuda.is_available():
                     print("""Launching training on one GPU."""				)
              else:
                     print("""Launching training on one CPU."""				)
              function(*SCREAMING_SNAKE_CASE__				)
       else:
              if num_processes is None:
                     raise ValueError(
                         """You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."""				)

              if num_processes > 1:
                     # Multi-GPU launch
                     from torch.multiprocessing import start_processes
                     from torch.multiprocessing.spawn import ProcessRaisedException

                     if len(AcceleratorState._shared_state				) > 0:
                            raise ValueError(
                                """To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized """
                                """inside your training function. Restart your notebook and make sure no cells initializes an """
                                """`Accelerator`."""				)

                     if torch.cuda.is_initialized():
                            raise ValueError(
                                """To launch a multi-GPU training from your notebook, you need to avoid running any instruction """
                                """using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA """
                                """function."""				)

                     # torch.distributed will expect a few environment variable to be here. We set the ones common to each
                     # process here (the other ones will be set be the launcher).
                     with patch_environment(
                         world_size=SCREAMING_SNAKE_CASE__					,	master_addr="""127.0.01"""					,	master_port=SCREAMING_SNAKE_CASE__					,	mixed_precision=SCREAMING_SNAKE_CASE__				):
                            lowercase							:     Optional[Any]       	=      PrepareForLaunch(SCREAMING_SNAKE_CASE__					,	distributed_type="""MULTI_GPU"""				)
                            print(f"Launching training on {num_processes} GPUs."				)
                            try:
                                   start_processes(SCREAMING_SNAKE_CASE__					,	args=SCREAMING_SNAKE_CASE__					,	nprocs=SCREAMING_SNAKE_CASE__					,	start_method="""fork"""				)
                            except ProcessRaisedException as e:
                                   if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                                          raise RuntimeError(
                                              """CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. """
                                              """This likely stems from an outside import causing issues once the `notebook_launcher()` is called. """
                                              """Please review your imports and test them when running the `notebook_launcher()` to identify """
                                              """which one is problematic."""				) from e

              else:
                     # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
                     if is_mps_available():
                            lowercase							:     Union[str, Any]       	=      """1"""
                            print("""Launching training on MPS."""				)
                     elif torch.cuda.is_available():
                            print("""Launching training on one GPU."""				)
                     else:
                            print("""Launching training on CPU."""				)
                     function(*SCREAMING_SNAKE_CASE__				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=()					,	SCREAMING_SNAKE_CASE__=2				) ->    List[Any]:
       from torch.multiprocessing import start_processes

       with tempfile.NamedTemporaryFile() as tmp_file:
              # torch.distributed will expect a few environment variable to be here. We set the ones common to each
              # process here (the other ones will be set be the launcher).
              with patch_environment(
                  world_size=SCREAMING_SNAKE_CASE__					,	master_addr="""127.0.01"""					,	master_port="""29500"""					,	accelerate_mixed_precision="""no"""					,	accelerate_debug_rdv_file=tmp_file.name					,	accelerate_use_cpu="""yes"""					,	):
                     lowercase							:     Tuple       	=      PrepareForLaunch(SCREAMING_SNAKE_CASE__					,	debug=SCREAMING_SNAKE_CASE__				)
                     start_processes(SCREAMING_SNAKE_CASE__					,	args=SCREAMING_SNAKE_CASE__					,	nprocs=SCREAMING_SNAKE_CASE__					,	start_method="""fork"""				)


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           requires_backends(self		,"""vision"""      )
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

           lowercase							:     Tuple       	=      scores.tolist()
           lowercase							:     Dict       	=      ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,snake_case      )]
