


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        SCREAMING_SNAKE_CASE_						(			__A :			List[str]				)	->		str:

       """simple docstring"""

       a_					:		Tuple							=  []
       for line in lines:
              a_					:		Any							=  re.sub(R'#.*'  ,    ''  ,    __A				)  # remove comments
              if line:
                     filtered_lines.append(__A				)
       a_					:		Tuple							=  '\n'.join(__A				)

       # Make a hash from all this code
       a_					:		Tuple							=  full_str.encode('utf-8'				)
       return shaaaa(__A				).hexdigest()


# get importable module names and hash for caching
UpperCAmelCase_      : List[Any]  					=       {
    '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
UpperCAmelCase_      : Dict  					=       {
    '.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})
UpperCAmelCase_      : Optional[int]  					=       {'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
UpperCAmelCase_      : Dict[str, List[str]]  					=       {}
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')






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, logging


UpperCAmelCase_      : Optional[int]  					=       logging.get_logger(__name__)
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[str, Any]        =						['''pixel_values''']
      def __init__(       self				:       List[str]     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Optional[Dict[str, int]] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BILINEAR     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       bool = True     ,							SCREAMING_SNAKE_CASE__				:       Union[int, float] = 1 / 2_5_5     ,							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__				:       Union[str, Any]     ,							)		->			None:
             super().__init__(**SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  size if size is not None else {'shortest_edge': 2_5_6}
             a_					:		Any							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  do_resize
             a_					:		Dict							=  size
             a_					:		Optional[Any]							=  resample
             a_					:		Optional[int]							=  do_center_crop
             a_					:		Dict							=  crop_size
             a_					:		int							=  do_rescale
             a_					:		int							=  rescale_factor
             a_					:		Tuple							=  do_normalize
             a_					:		int							=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             a_					:		Tuple							=  image_std if image_std is not None else IMAGENET_STANDARD_STD
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = PILImageResampling.BICUBIC     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		List[Any]							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             if "shortest_edge" not in size:
                    raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""     )
             a_					:		Tuple							=  get_resize_output_image_size(SCREAMING_SNAKE_CASE__     ,							size=size['shortest_edge']     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             return resize(SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ,							resample=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			np.ndarray:
             a_					:		str							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             return center_crop(SCREAMING_SNAKE_CASE__     ,							size=(size['height'], size['width'])     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       np.ndarray     ,							SCREAMING_SNAKE_CASE__				:       float     ,							SCREAMING_SNAKE_CASE__				:       Optional[Union[str, ChannelDimension]] = None     ,							**SCREAMING_SNAKE_CASE__				:       Optional[int]     )		->			np.ndarray:
             return rescale(SCREAMING_SNAKE_CASE__     ,							scale=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							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__				:       List[str]     ,							)		->			np.ndarray:
             return normalize(SCREAMING_SNAKE_CASE__     ,							mean=SCREAMING_SNAKE_CASE__     ,							std=SCREAMING_SNAKE_CASE__     ,							data_format=SCREAMING_SNAKE_CASE__     ,							**SCREAMING_SNAKE_CASE__     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       ImageInput     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       PILImageResampling = None     ,							SCREAMING_SNAKE_CASE__				:       bool = None     ,							SCREAMING_SNAKE_CASE__				:       Dict[str, int] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[bool] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[float] = None     ,							SCREAMING_SNAKE_CASE__				:       Optional[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__				:       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			Union[str, Any]:
             a_					:		List[str]							=  do_resize if do_resize is not None else self.do_resize
             a_					:		Dict							=  size if size is not None else self.size
             a_					:		Dict							=  get_size_dict(SCREAMING_SNAKE_CASE__     ,							default_to_square=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  resample if resample is not None else self.resample
             a_					:		List[Any]							=  do_center_crop if do_center_crop is not None else self.do_center_crop
             a_					:		int							=  crop_size if crop_size is not None else self.crop_size
             a_					:		Optional[int]							=  get_size_dict(SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  do_rescale if do_rescale is not None else self.do_rescale
             a_					:		Dict							=  rescale_factor if rescale_factor is not None else self.rescale_factor
             a_					:		Any							=  do_normalize if do_normalize is not None else self.do_normalize
             a_					:		str							=  image_mean if image_mean is not None else self.image_mean
             a_					:		Dict							=  image_std if image_std is not None else self.image_std

             a_					:		Optional[int]							=  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:
                    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.'     )

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

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

             if do_center_crop:
                    a_					:		int							=  [self.center_crop(image=SCREAMING_SNAKE_CASE__     ,							size=SCREAMING_SNAKE_CASE__     ) for image in images]

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

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

             a_					:		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ) for image in images]

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


