import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


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

__lowerCamelCase     :					Any				  =						logging.get_logger(__name__)



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			bytes					,	__UpperCamelCase       :			int      )				->   np.array:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= f"""{sampling_rate}"""
  SCREAMING_SNAKE_CASE__     				= """1"""
  SCREAMING_SNAKE_CASE__     				= """f32le"""
  SCREAMING_SNAKE_CASE__     				= [
      """ffmpeg""",
      """-i""",
      """pipe:0""",
      """-ac""",
      ac,
      """-ar""",
      ar,
      """-f""",
      format_for_conversion,
      """-hide_banner""",
      """-loglevel""",
      """quiet""",
      """pipe:1""",
  ]

  try:
    SCREAMING_SNAKE_CASE__     				= subprocess.Popen(__UpperCamelCase					,	stdin=subprocess.PIPE					,	stdout=subprocess.PIPE      )
  except FileNotFoundError:
    raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""      )
  SCREAMING_SNAKE_CASE__     				= ffmpeg_process.communicate(__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= output_stream[0]

  SCREAMING_SNAKE_CASE__     				= np.frombuffer(__UpperCamelCase					,	np.floataa      )
  if audio.shape[0] == 0:
    raise ValueError("""Malformed soundfile"""      )
  return audio







@add_end_docstrings(lowerCamelCase_						)
class __snake_case							(		lowerCamelCase_						):




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 5
     super().__init__(*_lowercase			,      **_lowercase )

     if self.framework != "pt":
       raise ValueError(f"""The {self.__class__} is only available in PyTorch.""" )

     self.check_model_type(_lowercase )




   def __call__(       self     :							Optional[int]			,      _lowercase     :							Union[np.ndarray, bytes, str]			,      **_lowercase     :							str			,      ):



     """simple docstring"""






     return super().__call__(_lowercase			,      **_lowercase )




   def       __a		(       self     :							Tuple			,      _lowercase     :							Tuple=None			,      **_lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= {}
     if top_k is not None:
       if top_k > self.model.config.num_labels:
         SCREAMING_SNAKE_CASE__     				= self.model.config.num_labels
       SCREAMING_SNAKE_CASE__     				= top_k
     return {}, {}, postprocess_params




   def       __a		(       self     :							str			,      _lowercase     :							List[str] ):



     """simple docstring"""






     if isinstance(_lowercase			,      _lowercase ):
       if inputs.startswith("""http://""" ) or inputs.startswith("""https://""" ):
         # We need to actually check for a real protocol, otherwise it's impossible to use a local file
         # like http_huggingface_co.png
         SCREAMING_SNAKE_CASE__     				= requests.get(_lowercase ).content
       else:
         with open(_lowercase			,      """rb""" ) as f:
           SCREAMING_SNAKE_CASE__     				= f.read()

     if isinstance(_lowercase			,      _lowercase ):
       SCREAMING_SNAKE_CASE__     				= ffmpeg_read(_lowercase			,      self.feature_extractor.sampling_rate )

     if isinstance(_lowercase			,      _lowercase ):
       # Accepting `"array"` which is the key defined in `datasets` for
       # better integration
       if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
         raise ValueError(
             """When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a """
             """\"raw\" key containing the numpy array representing the audio and a \"sampling_rate\" key, """
             """containing the sampling_rate associated with that array""" )

       SCREAMING_SNAKE_CASE__     				= inputs.pop("""raw"""			,      _lowercase )
       if _inputs is None:
         # Remove path which will not be used from `datasets`.
         inputs.pop("""path"""			,      _lowercase )
         SCREAMING_SNAKE_CASE__     				= inputs.pop("""array"""			,      _lowercase )
       SCREAMING_SNAKE_CASE__     				= inputs.pop("""sampling_rate""" )
       SCREAMING_SNAKE_CASE__     				= _inputs
       if in_sampling_rate != self.feature_extractor.sampling_rate:
         import torch

         if is_torchaudio_available():
           from torchaudio import functional as F
         else:
           raise ImportError(
               """torchaudio is required to resample audio samples in AudioClassificationPipeline. """
               """The torchaudio package can be installed through: `pip install torchaudio`.""" )

         SCREAMING_SNAKE_CASE__     				= F.resample(
             torch.from_numpy(_lowercase )			,      _lowercase			,      self.feature_extractor.sampling_rate ).numpy()

     if not isinstance(_lowercase			,      np.ndarray ):
       raise ValueError("""We expect a numpy ndarray as input""" )
     if len(inputs.shape ) != 1:
       raise ValueError("""We expect a single channel audio input for AudioClassificationPipeline""" )

     SCREAMING_SNAKE_CASE__     				= self.feature_extractor(
         _lowercase			,      sampling_rate=self.feature_extractor.sampling_rate			,      return_tensors="""pt""" )
     return processed




   def       __a		(       self     :							List[Any]			,      _lowercase     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model(**_lowercase )
     return model_outputs




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]=5 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= model_outputs.logits[0].softmax(-1 )
     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= probs.topk(_lowercase )

     SCREAMING_SNAKE_CASE__     				= scores.tolist()
     SCREAMING_SNAKE_CASE__     				= ids.tolist()

     SCREAMING_SNAKE_CASE__     				= [{"""score""": score, """label""": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase			,      _lowercase )]

     return labels

# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__lowerCamelCase     :					List[str]				  =						logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer







   class __snake_case							(		lowerCamelCase_						):




      def __init__(       self     :							Tuple			,      _lowercase     :							int = 1_01 ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__     				= length




      def __len__(       self     :							str ):



        """simple docstring"""






        return self.length




      def __getitem__(       self     :							Optional[Any]			,      _lowercase     :							Union[str, Any] ):



        """simple docstring"""






        return i







   class __snake_case							:




      def __call__(       self     :							Dict			,      _lowercase     :							Dict ):



        """simple docstring"""






        return {"input_ids": torch.tensor(_lowercase ), "labels": torch.tensor(_lowercase )}







   class __snake_case							(		nn.Module						):




      def __init__(       self     :							Tuple ):



        """simple docstring"""






        super().__init__()
        # Add some (unused) params otherwise DDP will complain.
        SCREAMING_SNAKE_CASE__     				= nn.Linear(1_20			,      80 )




      def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict=None ):



        """simple docstring"""






        if labels is not None:
          return torch.tensor(0.0			,      device=input_ids.device ), input_ids
        else:
          return input_ids



def       __SCREAMING_SNAKE_CASE    (   )				->   Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= HfArgumentParser((TrainingArguments,)      )
  sys.argv += ["--output_dir", "./examples"]
  SCREAMING_SNAKE_CASE__     				= parser.parse_args_into_dataclasses()[0]

  logger.warning(
      f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
      f"""tpu_num_cores: {training_args.tpu_num_cores}"""					,	)

  # Essentially, what we want to verify in the distributed case is
  # that we get all samples back, in the right order.
  # (this is crucial for prediction for instance)
  for dataset_length in [10_01, 2_56, 15]:
    SCREAMING_SNAKE_CASE__     				= DummyDataset(__UpperCamelCase      )

    def compute_metrics(__UpperCamelCase       :			EvalPrediction      ) -> Dict:
      SCREAMING_SNAKE_CASE__     				= list(range(len(__UpperCamelCase      )      )      )
      SCREAMING_SNAKE_CASE__     				= p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      return {"success": success}

    SCREAMING_SNAKE_CASE__     				= Trainer(
        model=DummyModel()					,	args=__UpperCamelCase					,	data_collator=DummyDataCollator()					,	eval_dataset=__UpperCamelCase					,	compute_metrics=__UpperCamelCase					,	)
    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= 2

    SCREAMING_SNAKE_CASE__     				= trainer.evaluate()
    logger.info(__UpperCamelCase      )
    if metrics["eval_success"] is not True:
      logger.error(__UpperCamelCase      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= trainer.predict(__UpperCamelCase      )
    logger.info(p.metrics      )
    if p.metrics["test_success"] is not True:
      logger.error(p.metrics      )
      exit(1      )

    SCREAMING_SNAKE_CASE__     				= None

  logger.info("""🔥 All distributed tests successful"""      )



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]      )				->   Dict:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()
