






"""simple docstring"""


import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


lowercase_    					=					logging.get_logger(__name__)

if is_vision_available():
       import PIL.Image
       from PIL import Image
       from PIL.Image import Image as ImageType
else:
       lowercase_    					=					object

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf


class  snake_case		:


   '''simple docstring'''

   def __init__(     self			:					Tuple,	_lowerCamelCase			:					Union[str, Any]  ):

     '''simple docstring'''
     __A						      =			value


   def __str__(     self			:					List[Any]  ):

     '''simple docstring'''
     return self.to_string()


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any]  ):

     '''simple docstring'''
     logger.error(
         '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''  )
     return self._value






   def     _SCREAMING_SNAKE_CASE    (     self			:					int  ):

     '''simple docstring'''
     logger.error(
         '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''  )
     return str(self._value  )





class  snake_case		(							_lowerCAmelCase       ,     _lowerCAmelCase						):


   '''simple docstring'''

   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return self._value






   def     _SCREAMING_SNAKE_CASE    (     self			:					Any  ):

     '''simple docstring'''
     return self._value





class  snake_case		(							_lowerCAmelCase       ,     _lowerCAmelCase						):


   '''simple docstring'''

   def __init__(     self			:					Union[str, Any],	_lowerCamelCase			:					int  ):

     '''simple docstring'''
     super().__init__(_lowerCamelCase  )

     if not is_vision_available():
       raise ImportError('''PIL must be installed in order to handle images.'''  )

     __A						      =			None
     __A						      =			None
     __A						      =			None

     if isinstance(_lowerCamelCase,	_lowerCamelCase  ):
       __A						      =			value
     elif isinstance(_lowerCamelCase,	(str, pathlib.Path)  ):
       __A						      =			value
     elif isinstance(_lowerCamelCase,	torch.Tensor  ):
       __A						      =			value
     else:
       raise ValueError(f'Unsupported type for {self.__class__.__name__}: {type(_lowerCamelCase  )}'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Union[str, Any],	_lowerCamelCase			:					int=None,	_lowerCamelCase			:					Any=None  ):

     '''simple docstring'''
     from IPython.display import Image, display

     display(Image(self.to_string()  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					str  ):

     '''simple docstring'''
     if self._raw is not None:
       return self._raw

     if self._path is not None:
       __A						      =			Image.open(self._path  )
       return self._raw






   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[int]  ):

     '''simple docstring'''
     if self._path is not None:
       return self._path

     if self._raw is not None:
       __A						      =			tempfile.mkdtemp()
       __A						      =			os.path.join(_lowerCamelCase,	str(uuid.uuida()  ) + '''.png'''  )
       self._raw.save(self._path  )

       return self._path

     if self._tensor is not None:
       __A						      =			self._tensor.cpu().detach().numpy()

       # There is likely simpler than load into image into save
       __A						      =			Image.fromarray((array * 2_55).astype(np.uinta  )  )

       __A						      =			tempfile.mkdtemp()
       __A						      =			os.path.join(_lowerCamelCase,	str(uuid.uuida()  ) + '''.png'''  )

       img.save(self._path  )

       return self._path





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(     self			:					Any,	_lowerCamelCase			:					List[Any],	_lowerCamelCase			:					List[str]=1_60_00  ):

     '''simple docstring'''
     super().__init__(_lowerCamelCase  )

     if not is_soundfile_availble():
       raise ImportError('''soundfile must be installed in order to handle audio.'''  )

     __A						      =			None
     __A						      =			None

     __A						      =			samplerate

     if isinstance(_lowerCamelCase,	(str, pathlib.Path)  ):
       __A						      =			value
     elif isinstance(_lowerCamelCase,	torch.Tensor  ):
       __A						      =			value
     else:
       raise ValueError(f'Unsupported audio type: {type(_lowerCamelCase  )}'  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple,	_lowerCamelCase			:					str=None,	_lowerCamelCase			:					Optional[int]=None  ):

     '''simple docstring'''
     from IPython.display import Audio, display

     display(Audio(self.to_string(),	rate=self.samplerate  )  )


   def     _SCREAMING_SNAKE_CASE    (     self			:					List[str]  ):

     '''simple docstring'''
     if self._tensor is not None:
       return self._tensor

     if self._path is not None:
       __A      ,   __A						      =			sf.read(self._path  )
       __A						      =			torch.tensor(_lowerCamelCase  )
       return self._tensor






   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     if self._path is not None:
       return self._path

     if self._tensor is not None:
       __A						      =			tempfile.mkdtemp()
       __A						      =			os.path.join(_lowerCamelCase,	str(uuid.uuida()  ) + '''.wav'''  )
       sf.write(self._path,	self._tensor,	samplerate=self.samplerate  )
       return self._path





lowercase_    					=					{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
lowercase_    					=					{str: AgentText}

if is_vision_available():
       lowercase_    					=					AgentImage



def  lowerCAmelCase    (   *__UpperCamelCase		,	**__UpperCamelCase			):




  """simple docstring"""







  __A						      =			[(arg.to_raw() if isinstance(__UpperCamelCase		,	__UpperCamelCase			) else arg) for arg in args]
  __A						      =			{k: (v.to_raw() if isinstance(__UpperCamelCase		,	__UpperCamelCase			) else v) for k, v in kwargs.items()}
  return args, kwargs





def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase=None			):




  """simple docstring"""







  if isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    __A						      =			{}
    for i, (k, v) in enumerate(outputs.items()			):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          __A						      =			AGENT_TYPE_MAPPING[output_types[i]](__UpperCamelCase			)
        else:
          __A						      =			AgentType(__UpperCamelCase			)

      else:
        # If the class does not have defined output, then we map according to the type
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__UpperCamelCase		,	_k			):
            __A						      =			_v(__UpperCamelCase			)
        if k not in decoded_outputs:
          __A						      =			AgentType[v]

  elif isinstance(__UpperCamelCase		,	(list, tuple)			):
    __A						      =			type(__UpperCamelCase			)()
    for i, v in enumerate(__UpperCamelCase			):
      if output_types is not None:
        # If the class has defined outputs, we can map directly according to the class definition
        if output_types[i] in AGENT_TYPE_MAPPING:
          decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](__UpperCamelCase			)			)
        else:
          decoded_outputs.append(AgentType(__UpperCamelCase			)			)
      else:
        # If the class does not have defined output, then we map according to the type
        __A						      =			False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(__UpperCamelCase		,	_k			):
            decoded_outputs.append(_v(__UpperCamelCase			)			)
            __A						      =			True

        if not found:
          decoded_outputs.append(AgentType(__UpperCamelCase			)			)

  else:
    if output_types[0] in AGENT_TYPE_MAPPING:
      # If the class has defined outputs, we can map directly according to the class definition
      __A						      =			AGENT_TYPE_MAPPING[output_types[0]](__UpperCamelCase			)

    else:
      # If the class does not have defined output, then we map according to the type
      for _k, _v in INSTANCE_TYPE_MAPPING.items():
        if isinstance(__UpperCamelCase		,	_k			):
          return _v(__UpperCamelCase			)
      return AgentType(__UpperCamelCase			)

  return decoded_outputs












"""simple docstring"""


# 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


lowercase_    					=					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						):


          '''simple docstring'''

          def __init__(     self			:					List[Any],	_lowerCamelCase			:					int = 1_01  ):

            '''simple docstring'''
            __A						      =			length


          def __len__(     self			:					Optional[int]  ):

            '''simple docstring'''
            return self.length






          def __getitem__(     self			:					List[str],	_lowerCamelCase			:					Union[str, Any]  ):

            '''simple docstring'''
            return i





       class  snake_case		:


          '''simple docstring'''

          def __call__(     self			:					Tuple,	_lowerCamelCase			:					Dict  ):

            '''simple docstring'''
            return {"input_ids": torch.tensor(_lowerCamelCase  ), "labels": torch.tensor(_lowerCamelCase  )}





       class  snake_case		(							nn.Module						):


          '''simple docstring'''

          def __init__(     self			:					List[str]  ):

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






          def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					Tuple,	_lowerCamelCase			:					Optional[Any]=None  ):

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



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __A						      =			HfArgumentParser((TrainingArguments,)			)
  sys.argv += ["--output_dir", "./examples"]
  __A						      =			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 [1_0_0_1, 2_5_6, 1_5]:
    __A						      =			DummyDataset(__UpperCamelCase			)

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

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

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

    __A						      =			2

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

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

    __A						      =			None

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



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  main()


if __name__ == "__main__":
       main()




