

'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


_a			:	str								=  logging.get_logger(__name__)


if is_vision_available():
  import PIL



class     lowercase_		(						a	):

  '''simple docstring'''





  __lowerCAmelCase   :	Dict      					=				["pixel_values"]
  def __init__(       self				,	a_ = True				,	a_ = None				,	a_ = PILImageResampling.BILINEAR				,	a_ = True				,	a_ = False				,	a_ = True				,	a_ = True				,	a_ = 1 / 2_5_5				,	a_ = True				,	a_ = None				,	a_ = None				,	**a_				,	)							->			None:


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

    UpperCAmelCase  							=		size if size is not None else {'height': 2_5_6_0, 'width': 1_9_2_0}
    if isinstance(a_				,	(tuple, list)       ):
      # The previous feature extractor size parameter was in (width, height) format
      UpperCAmelCase  							=		size[::-1]
    UpperCAmelCase  							=		get_size_dict(a_       )

    UpperCAmelCase  							=		do_resize
    UpperCAmelCase  							=		size
    UpperCAmelCase  							=		resample
    UpperCAmelCase  							=		do_thumbnail
    UpperCAmelCase  							=		do_align_long_axis
    UpperCAmelCase  							=		do_pad
    UpperCAmelCase  							=		do_rescale
    UpperCAmelCase  							=		rescale_factor
    UpperCAmelCase  							=		do_normalize
    UpperCAmelCase  							=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    UpperCAmelCase  							=		image_std if image_std is not None else IMAGENET_STANDARD_STD
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = None       )							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		get_image_size(a_       )
    UpperCAmelCase							, UpperCAmelCase  							=		size['height'], size['width']

    if (output_width < output_height and input_width > input_height) or (
        output_width > output_height and input_width < input_height
    ):
      UpperCAmelCase  							=		np.rotaa(a_				,	3       )

    if data_format is not None:
      UpperCAmelCase  							=		to_channel_dimension_format(a_				,	a_       )

    return image
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = False				,	a_ = None				,	)							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		size['height'], size['width']
    UpperCAmelCase							, UpperCAmelCase  							=		get_image_size(a_       )

    UpperCAmelCase  							=		output_width - input_width
    UpperCAmelCase  							=		output_height - input_height

    if random_padding:
      UpperCAmelCase  							=		np.random.randint(low=0				,	high=delta_height + 1       )
      UpperCAmelCase  							=		np.random.randint(low=0				,	high=delta_width + 1       )
    else:
      UpperCAmelCase  							=		delta_height // 2
      UpperCAmelCase  							=		delta_width // 2

    UpperCAmelCase  							=		delta_height - pad_top
    UpperCAmelCase  							=		delta_width - pad_left

    UpperCAmelCase  							=		((pad_top, pad_bottom), (pad_left, pad_right))
    return pad(a_				,	a_				,	data_format=a_       )
  def   snake_case_     (       self				,	*a_				,	**a_       )							->			Union[str, Any]:


    """simple docstring"""
    logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'       )
    return self.pad_image(*a_				,	**a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = PILImageResampling.BICUBIC				,	a_ = None				,	**a_				,	)							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase							, UpperCAmelCase  							=		get_image_size(a_       )
    UpperCAmelCase							, UpperCAmelCase  							=		size['height'], size['width']

    # We always resize to the smallest of either the input or output size.
    UpperCAmelCase  							=		min(a_				,	a_       )
    UpperCAmelCase  							=		min(a_				,	a_       )

    if height == input_height and width == input_width:
      return image

    if input_height > input_width:
      UpperCAmelCase  							=		int(input_width * height / input_height       )
    elif input_width > input_height:
      UpperCAmelCase  							=		int(input_height * width / input_width       )

    return resize(
        a_				,	size=(height, width)				,	resample=a_				,	reducing_gap=2.0				,	data_format=a_				,	**a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = PILImageResampling.BICUBIC				,	a_ = None				,	**a_				,	)							->			np.ndarray:


    """simple docstring"""
    UpperCAmelCase  							=		get_size_dict(a_       )
    UpperCAmelCase  							=		min(size['height']				,	size['width']       )
    UpperCAmelCase  							=		get_resize_output_image_size(a_				,	size=a_				,	default_to_square=a_       )
    UpperCAmelCase  							=		resize(a_				,	size=a_				,	resample=a_				,	data_format=a_				,	**a_       )
    return resized_image
  def   snake_case_     (       self				,	a_				,	a_				,	a_ = None				,	**a_				,	)							->			Optional[Any]:


    """simple docstring"""
    return rescale(a_				,	scale=a_				,	data_format=a_				,	**a_       )
  def   snake_case_     (       self				,	a_				,	a_				,	a_				,	a_ = None				,	**a_				,	)							->			np.ndarray:


    """simple docstring"""
    return normalize(a_				,	mean=a_				,	std=a_				,	data_format=a_				,	**a_       )





  def   snake_case_     (       self				,	a_				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = False				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = ChannelDimension.FIRST				,	**a_				,	)							->			PIL.Image.Image:


    """simple docstring"""
    UpperCAmelCase  							=		do_resize if do_resize is not None else self.do_resize
    UpperCAmelCase  							=		size if size is not None else self.size
    if isinstance(a_				,	(tuple, list)       ):
      # Previous feature extractor had size in (width, height) format
      UpperCAmelCase  							=		size[::-1]
    UpperCAmelCase  							=		get_size_dict(a_       )
    UpperCAmelCase  							=		resample if resample is not None else self.resample
    UpperCAmelCase  							=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
    UpperCAmelCase  							=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
    UpperCAmelCase  							=		do_pad if do_pad is not None else self.do_pad
    UpperCAmelCase  							=		do_rescale if do_rescale is not None else self.do_rescale
    UpperCAmelCase  							=		rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCAmelCase  							=		do_normalize if do_normalize is not None else self.do_normalize
    UpperCAmelCase  							=		image_mean if image_mean is not None else self.image_mean
    UpperCAmelCase  							=		image_std if image_std is not None else self.image_std

    UpperCAmelCase  							=		make_list_of_images(a_       )

    if not valid_images(a_       ):
      raise ValueError(
          'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
          'torch.Tensor, tf.Tensor or jax.ndarray.'       )

    if do_resize and size is None:
      raise ValueError('Size must be specified if do_resize is True.'       )

    if do_rescale and rescale_factor is None:
      raise ValueError('Rescale factor must be specified if do_rescale is True.'       )

    if do_pad and size is None:
      raise ValueError('Size must be specified if do_pad is True.'       )

    if do_normalize and (image_mean is None or image_std is None):
      raise ValueError('Image mean and std must be specified if do_normalize is True.'       )

    # All transformations expect numpy arrays.
    UpperCAmelCase  							=		[to_numpy_array(a_       ) for image in images]

    if do_align_long_axis:
      UpperCAmelCase  							=		[self.align_long_axis(a_				,	size=a_       ) for image in images]

    if do_resize:
      UpperCAmelCase  							=		[self.resize(image=a_				,	size=a_				,	resample=a_       ) for image in images]

    if do_thumbnail:
      UpperCAmelCase  							=		[self.thumbnail(image=a_				,	size=a_       ) for image in images]

    if do_pad:
      UpperCAmelCase  							=		[self.pad_image(image=a_				,	size=a_				,	random_padding=a_       ) for image in images]

    if do_rescale:
      UpperCAmelCase  							=		[self.rescale(image=a_				,	scale=a_       ) for image in images]

    if do_normalize:
      UpperCAmelCase  							=		[self.normalize(image=a_				,	mean=a_				,	std=a_       ) for image in images]

    UpperCAmelCase  							=		[to_channel_dimension_format(a_				,	a_       ) for image in images]

    UpperCAmelCase  							=		{'pixel_values': images}
    return BatchFeature(data=a_				,	tensor_type=a_       )





'''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


