

'''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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   _A       :     Union[str, Any]       =					['''pixel_values''']
   def __init__( self			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BILINEAR     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Union[int, float] = 1 / 255     ,SCREAMING_SNAKE_CASE__			:      bool = True     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,**SCREAMING_SNAKE_CASE__			:      List[str]     ,):
     super().__init__(**SCREAMING_SNAKE_CASE__							)
     SCREAMING_SNAKE_CASE:int						      =					size if size is not None else {"shortest_edge": 384}
     SCREAMING_SNAKE_CASE:List[str]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					do_resize
     SCREAMING_SNAKE_CASE:List[Any]						      =					size
     # Default value set here for backwards compatibility where the value in config is None
     SCREAMING_SNAKE_CASE:Dict						      =					crop_pct if crop_pct is not None else 224 / 256
     SCREAMING_SNAKE_CASE:Any						      =					resample
     SCREAMING_SNAKE_CASE:Dict						      =					do_rescale
     SCREAMING_SNAKE_CASE:Optional[int]						      =					rescale_factor
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					do_normalize
     SCREAMING_SNAKE_CASE:Optional[int]						      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE:Tuple						      =					image_std if image_std is not None else IMAGENET_STANDARD_STD
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int]     ,SCREAMING_SNAKE_CASE__			:      float     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = PILImageResampling.BICUBIC     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      int     ,):
     SCREAMING_SNAKE_CASE:List[Any]						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
     if "shortest_edge" not in size:
       raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''							)
     SCREAMING_SNAKE_CASE:Any						      =					size["shortest_edge"]

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       SCREAMING_SNAKE_CASE:Any						      =					int(shortest_edge / crop_pct							)
       SCREAMING_SNAKE_CASE:Any						      =					get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           SCREAMING_SNAKE_CASE__     ,size=(shortest_edge, shortest_edge)     ,resample=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[int, float]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return rescale(SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      np.ndarray     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Union[float, List[float]]     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, ChannelDimension]] = None     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,):
     return normalize(SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__     ,data_format=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      ImageInput     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Dict[str, int] = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      PILImageResampling = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      float = None     ,SCREAMING_SNAKE_CASE__			:      bool = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[float, List[float]]] = None     ,SCREAMING_SNAKE_CASE__			:      Optional[Union[str, TensorType]] = None     ,SCREAMING_SNAKE_CASE__			:      ChannelDimension = ChannelDimension.FIRST     ,**SCREAMING_SNAKE_CASE__			:      List[Any]     ,):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE:List[str]						      =					crop_pct if crop_pct is not None else self.crop_pct
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE:List[str]						      =					do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE:str						      =					rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE:str						      =					do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE:Any						      =					image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE:Optional[int]						      =					size if size is not None else self.size
     SCREAMING_SNAKE_CASE:Tuple						      =					get_size_dict(SCREAMING_SNAKE_CASE__     ,default_to_square=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:List[Any]						      =					make_list_of_images(SCREAMING_SNAKE_CASE__							)

     if not valid_images(SCREAMING_SNAKE_CASE__							):
       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 or resample is None:
       raise ValueError("Size and resample must be specified if do_resize is True."							)

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError("crop_pct must be specified if size < 384."							)

     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."							)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE:int						      =					[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

     if do_resize:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					[self.resize(image=SCREAMING_SNAKE_CASE__     ,size=SCREAMING_SNAKE_CASE__     ,crop_pct=SCREAMING_SNAKE_CASE__     ,resample=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[self.rescale(image=SCREAMING_SNAKE_CASE__     ,scale=SCREAMING_SNAKE_CASE__							) for image in images]

     if do_normalize:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					[self.normalize(image=SCREAMING_SNAKE_CASE__     ,mean=SCREAMING_SNAKE_CASE__     ,std=SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:Any						      =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for image in images]

     SCREAMING_SNAKE_CASE:int						      =					{"pixel_values": images}
     return BatchFeature(data=SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)









'''simple docstring'''

import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					[]
  for line in lines:
    SCREAMING_SNAKE_CASE:Any						      =					re.sub(r"#.*"      ,       ""      ,       snake_case )  # remove comments
    if line:
      filtered_lines.append(snake_case )
  SCREAMING_SNAKE_CASE:int						      =					"\n".join(snake_case )

  # Make a hash from all this code
  SCREAMING_SNAKE_CASE:str						      =					full_str.encode("utf-8" )
  return shaaaa(snake_case ).hexdigest()


# get importable module names and hash for caching
A_       						=							{
    "csv": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    "json": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    "pandas": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    "parquet": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    "arrow": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    "text": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    "imagefolder": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    "audiofolder": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
A_       						=							{
    ".csv": ("csv", {}),
    ".tsv": ("csv", {"sep": "\t"}),
    ".json": ("json", {}),
    ".jsonl": ("json", {}),
    ".parquet": ("parquet", {}),
    ".arrow": ("arrow", {}),
    ".txt": ("text", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("imagefolder", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("audiofolder", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A_       						=							{"imagefolder", "audiofolder"}

# Used to filter data files based on extensions given a module name
A_       						=							{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
    _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(".zip")


_MODULE_TO_EXTENSIONS["audiofolder"].append(".zip")






