





'''simple docstring'''




import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter



class     lowercase_		(						TensorFormatter[Mapping, np.ndarray, Mapping]	):

  '''simple docstring'''





  def __init__(       self				,	a_=None				,	**a_       )							->			Dict:


    """simple docstring"""
    super().__init__(features=a_       )
    UpperCAmelCase  							=		np_array_kwargs
  def   snake_case_     (       self				,	a_       )							->			Optional[int]:


    """simple docstring"""
    if isinstance(a_				,	a_       ):
      if column and all(
          isinstance(a_				,	np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
        return np.stack(a_       )
      else:
        # don't use np.array(column, dtype=object)
        # since it fails in certain cases
        # see https://stackoverflow.com/q/51005699
        UpperCAmelCase  							=		np.empty(len(a_       )				,	dtype=a_       )
        UpperCAmelCase  							=		column
        return out
    return column
  def   snake_case_     (       self				,	a_       )							->			Optional[Any]:


    """simple docstring"""
    if isinstance(a_				,	(str, bytes, type(a_       ))       ):
      return value
    elif isinstance(a_				,	(np.character, np.ndarray)       ) and np.issubdtype(value.dtype				,	np.character       ):
      return value
    elif isinstance(a_				,	np.number       ):
      return value

    UpperCAmelCase  							=		{}

    if isinstance(a_				,	np.ndarray       ) and np.issubdtype(value.dtype				,	np.integer       ):
      UpperCAmelCase  							=		{'dtype': np.intaa}
    elif isinstance(a_				,	np.ndarray       ) and np.issubdtype(value.dtype				,	np.floating       ):
      UpperCAmelCase  							=		{'dtype': np.floataa}
    elif config.PIL_AVAILABLE and "PIL" in sys.modules:
      import PIL.Image

      if isinstance(a_				,	PIL.Image.Image       ):
        return np.asarray(a_				,	**self.np_array_kwargs       )

    return np.asarray(a_				,	**{**default_dtype, **self.np_array_kwargs}       )
  def   snake_case_     (       self				,	a_       )							->			Optional[Any]:


    """simple docstring"""
    # support for torch, tf, jax etc.
    if config.TORCH_AVAILABLE and "torch" in sys.modules:
      import torch

      if isinstance(a_				,	torch.Tensor       ):
        return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
    if hasattr(a_				,	'__array__'       ) and not isinstance(a_				,	(np.ndarray, np.character, np.number)       ):
      UpperCAmelCase  							=		data_struct.__array__()
    # support for nested types like struct of list of struct
    if isinstance(a_				,	np.ndarray       ):
      if data_struct.dtype == object:
        return self._consolidate([self.recursive_tensorize(a_       ) for substruct in data_struct]       )
    if isinstance(a_				,	(list, tuple)       ):
      return self._consolidate([self.recursive_tensorize(a_       ) for substruct in data_struct]       )
    return self._tensorize(a_       )
  def   snake_case_     (       self				,	a_       )							->			Union[str, Any]:


    """simple docstring"""
    return map_nested(self._recursive_tensorize				,	a_				,	map_list=a_       )
  def   snake_case_     (       self				,	a_       )							->			Mapping:


    """simple docstring"""
    UpperCAmelCase  							=		self.numpy_arrow_extractor().extract_row(a_       )
    UpperCAmelCase  							=		self.python_features_decoder.decode_row(a_       )
    return self.recursive_tensorize(a_       )
  def   snake_case_     (       self				,	a_       )							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase  							=		self.numpy_arrow_extractor().extract_column(a_       )
    UpperCAmelCase  							=		self.python_features_decoder.decode_column(a_				,	pa_table.column_names[0]       )
    UpperCAmelCase  							=		self.recursive_tensorize(a_       )
    UpperCAmelCase  							=		self._consolidate(a_       )
    return column





  def   snake_case_     (       self				,	a_       )							->			Mapping:


    """simple docstring"""
    UpperCAmelCase  							=		self.numpy_arrow_extractor().extract_batch(a_       )
    UpperCAmelCase  							=		self.python_features_decoder.decode_batch(a_       )
    UpperCAmelCase  							=		self.recursive_tensorize(a_       )
    for column_name in batch:
      UpperCAmelCase  							=		self._consolidate(batch[column_name]       )
    return batch





'''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


_a			:	int								=  logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  _a			:	List[str]								=  object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf



class     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_       )							->			Dict:


    """simple docstring"""
    UpperCAmelCase  							=		value
  def __str__(       self       )							->			Any:


    """simple docstring"""
    return self.to_string()
  def   snake_case_     (       self       )							->			Union[str, 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   snake_case_     (       self       )							->			str:


    """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     lowercase_		(						a	,    a	):

  '''simple docstring'''





  def   snake_case_     (       self       )							->			Dict:


    """simple docstring"""
    return self._value





  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    return self._value




class     lowercase_		(						a	,    a	):

  '''simple docstring'''





  def __init__(       self				,	a_       )							->			int:


    """simple docstring"""
    super().__init__(a_       )

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

    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None

    if isinstance(a_				,	a_       ):
      UpperCAmelCase  							=		value
    elif isinstance(a_				,	(str, pathlib.Path)       ):
      UpperCAmelCase  							=		value
    elif isinstance(a_				,	torch.Tensor       ):
      UpperCAmelCase  							=		value
    else:
      raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(a_       )}'''       )
  def   snake_case_     (       self				,	a_=None				,	a_=None       )							->			Dict:


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

    display(Image(self.to_string()       )       )
  def   snake_case_     (       self       )							->			List[str]:


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

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





  def   snake_case_     (       self       )							->			List[Any]:


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

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

      return self._path

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

      # There is likely simpler than load into image into save
      UpperCAmelCase  							=		Image.fromarray((array * 2_5_5).astype(np.uinta       )       )

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

      img.save(self._path       )

      return self._path




class     lowercase_		(						a	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=1_6_0_0_0       )							->			str:


    """simple docstring"""
    super().__init__(a_       )

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

    UpperCAmelCase  							=		None
    UpperCAmelCase  							=		None

    UpperCAmelCase  							=		samplerate

    if isinstance(a_				,	(str, pathlib.Path)       ):
      UpperCAmelCase  							=		value
    elif isinstance(a_				,	torch.Tensor       ):
      UpperCAmelCase  							=		value
    else:
      raise ValueError(F'''Unsupported audio type: {type(a_       )}'''       )
  def   snake_case_     (       self				,	a_=None				,	a_=None       )							->			Dict:


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

    display(Audio(self.to_string()				,	rate=self.samplerate       )       )
  def   snake_case_     (       self       )							->			List[str]:


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

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





  def   snake_case_     (       self       )							->			int:


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

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




_a			:	Optional[int]								=  {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
_a			:	Tuple								=  {str: AgentText}

if is_vision_available():
  _a			:	List[str]								=  AgentImage


def  lowerCamelCase__						(  *SCREAMING_SNAKE_CASE :				List[str]    ,				**SCREAMING_SNAKE_CASE :				Tuple  ):
  UpperCAmelCase  							=		[(arg.to_raw() if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ) else arg) for arg in args]
  UpperCAmelCase  							=		{k: (v.to_raw() if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ) else v) for k, v in kwargs.items()}
  return args, kwargs



def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Tuple    ,				SCREAMING_SNAKE_CASE :				Optional[Any]=None  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    UpperCAmelCase  							=		{}
    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:
          UpperCAmelCase  							=		AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE  )
        else:
          UpperCAmelCase  							=		AgentType(SCREAMING_SNAKE_CASE  )

      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(SCREAMING_SNAKE_CASE    ,				_k  ):
            UpperCAmelCase  							=		_v(SCREAMING_SNAKE_CASE  )
        if k not in decoded_outputs:
          UpperCAmelCase  							=		AgentType[v]

  elif isinstance(SCREAMING_SNAKE_CASE    ,				(list, tuple)  ):
    UpperCAmelCase  							=		type(SCREAMING_SNAKE_CASE  )()
    for i, v in enumerate(SCREAMING_SNAKE_CASE  ):
      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]](SCREAMING_SNAKE_CASE  )  )
        else:
          decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE  )  )
      else:
        # If the class does not have defined output, then we map according to the type
        UpperCAmelCase  							=		False
        for _k, _v in INSTANCE_TYPE_MAPPING.items():
          if isinstance(SCREAMING_SNAKE_CASE    ,				_k  ):
            decoded_outputs.append(_v(SCREAMING_SNAKE_CASE  )  )
            UpperCAmelCase  							=		True

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

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

    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(SCREAMING_SNAKE_CASE    ,				_k  ):
          return _v(SCREAMING_SNAKE_CASE  )
      return AgentType(SCREAMING_SNAKE_CASE  )

  return decoded_outputs


