

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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__							=			logging.get_logger(__name__)


if is_vision_available():
      import PIL






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""pixel_values"""]


 def __init__(    self		:			str      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[int, float] = 1 / 2_5_5      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    **lowerCAmelCase__		:			str      ,    )     ->   None:
   super().__init__(**lowerCAmelCase__     )
   UpperCAmelCase          =	size if size is not None else {"shortest_edge": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )

   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	resample
   UpperCAmelCase          =	do_center_crop
   UpperCAmelCase          =	crop_size
   UpperCAmelCase          =	do_rescale
   UpperCAmelCase          =	rescale_factor
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   UpperCAmelCase          =	image_std if image_std is not None else OPENAI_CLIP_STD
   UpperCAmelCase          =	do_convert_rgb


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			Optional[int]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	get_resize_output_image_size(
       lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    default_to_square=lowerCAmelCase__     )
   return resize(lowerCAmelCase__      ,    size=lowerCAmelCase__      ,    resample=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   return center_crop(lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[int, float]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   Optional[int]:
   return rescale(lowerCAmelCase__      ,    scale=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[str]      ,    )     ->   np.ndarray:
   return normalize(lowerCAmelCase__      ,    mean=lowerCAmelCase__      ,    std=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			ImageInput      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			int = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			float = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    lowerCAmelCase__		:			Optional[ChannelDimension] = ChannelDimension.FIRST      ,    **lowerCAmelCase__		:			int      ,    )     ->   PIL.Image.Image:
   UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase          =	size if size is not None else self.size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	resample if resample is not None else self.resample
   UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   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          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   UpperCAmelCase          =	make_list_of_images(lowerCAmelCase__     )

   if not valid_images(lowerCAmelCase__     ):
     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_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True."     )

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

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     UpperCAmelCase          =	[convert_to_rgb(lowerCAmelCase__     ) for image in images]

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

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

   if do_center_crop:
     UpperCAmelCase          =	[self.center_crop(image=lowerCAmelCase__      ,    size=lowerCAmelCase__     ) for image in images]

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

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

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

   UpperCAmelCase          =	{"pixel_values": images}
   return BatchFeature(data=lowerCAmelCase__      ,    tensor_type=lowerCAmelCase__     )






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     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     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(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    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(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

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


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch



