





import re
from pathlib import Path
from unittest import TestCase

import pytest




@pytest.mark.integration
class     SCREAMING_SNAKE_CASE__ (						UpperCAmelCase_      ):



 def 				UpperCamelCase			(							self,__lowerCamelCase ):

        with open(__lowercase,encoding='''utf-8''' ) as input_file:
               A__											=						re.compile(r'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)''' )
               A__											=						input_file.read()
               A__											=						regexp.search(__lowercase )

        return match



 def 				UpperCamelCase			(							self,__lowerCamelCase ):

        with open(__lowercase,encoding='''utf-8''' ) as input_file:
               A__											=						re.compile(r'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',re.DOTALL )
               A__											=						input_file.read()
               # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
               A__											=						regexp.finditer(__lowercase )

        A__											=						[match for match in matches if match is not None and match.group(1 ) is not None]
        return matches[0] if matches else None



 def 				UpperCamelCase			(							self ):
        A__											=						Path('''./datasets''' )
        A__											=						list(dataset_paths.absolute().glob('''**/*.py''' ) )

        for dataset in dataset_files:
               if self._no_encoding_on_file_open(str(__lowercase ) ):
                      raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}" )




 def 				UpperCamelCase			(							self ):
        A__											=						Path('''./datasets''' )
        A__											=						list(dataset_paths.absolute().glob('''**/*.py''' ) )

        for dataset in dataset_files:
               if self._no_print_statements(str(__lowercase ) ):
                      raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead." )






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





