



"""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, 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


__snake_case	:				Union[str, Any]  					=			logging.get_logger(__name__)

class       UpperCamelCase  (					A__				):





					"""simple docstring"""






					_lowerCamelCase					: str				   =['''pixel_values''']





					def __init__(					self :							Optional[int]	,      _lowerCamelCase :							bool = True	,      _lowerCamelCase :							Dict[str, int] = None	,      _lowerCamelCase :							PILImageResampling = PIL.Image.NEAREST	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							Dict[str, int] = None	,      _lowerCamelCase :							Union[int, float] = 1 / 2_5_5	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = True	,      _lowerCamelCase :							bool = True	,      _lowerCamelCase :							Optional[Union[float, List[float]]] = None	,      _lowerCamelCase :							Optional[Union[float, List[float]]] = None	,      _lowerCamelCase :							bool = True	,      **_lowerCamelCase :							List[Any]	,      ):
										super().__init__(**_lowerCamelCase )
										A__				=   size if size is not None else {"height": 3_4_6, "width": 3_4_6}
										A__				=   get_size_dict(_lowerCamelCase )
										A__				=   crop_size if crop_size is not None else {"height": 2_8_9, "width": 2_8_9}
										A__				=   get_size_dict(_lowerCamelCase	,      param_name='''crop_size''' )

										A__				=   do_resize
										A__				=   size
										A__				=   resample
										A__				=   do_center_crop
										A__				=   crop_size
										A__				=   do_rescale
										A__				=   rescale_factor
										A__				=   rescale_offset
										A__				=   do_normalize
										A__				=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										A__				=   image_std if image_std is not None else IMAGENET_STANDARD_STD
										A__				=   include_top





					def 						A__  (					self :							str	,      _lowerCamelCase :							np.ndarray	,      _lowerCamelCase :							Dict[str, int]	,      _lowerCamelCase :							PILImageResampling = PIL.Image.NEAREST	,      _lowerCamelCase :							Optional[Union[str, ChannelDimension]] = None	,      **_lowerCamelCase :							Optional[int]	,      ):
										A__				=   get_size_dict(_lowerCamelCase )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
										return resize(
										    _lowerCamelCase	,      size=(size['''height'''], size['''width'''])	,      resample=_lowerCamelCase	,      data_format=_lowerCamelCase	,      **_lowerCamelCase )





					def 						A__  (					self :							int	,      _lowerCamelCase :							np.ndarray	,      _lowerCamelCase :							Dict[str, int]	,      _lowerCamelCase :							Optional[Union[str, ChannelDimension]] = None	,      **_lowerCamelCase :							Dict	,      ):
										A__				=   get_size_dict(_lowerCamelCase )
										if "height" not in size or "width" not in size:
															raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}''' )
										return center_crop(_lowerCamelCase	,      size=(size['''height'''], size['''width'''])	,      data_format=_lowerCamelCase	,      **_lowerCamelCase )





					def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							np.ndarray	,      _lowerCamelCase :							Union[int, float]	,      _lowerCamelCase :							bool = True	,      _lowerCamelCase :							Optional[Union[str, ChannelDimension]] = None	,      **_lowerCamelCase :							List[Any]	,      ):






										if offset:
															A__				=   (image - 1_2_7.5) * scale
															if data_format is not None:
																				A__				=   to_channel_dimension_format(_lowerCamelCase	,      _lowerCamelCase )
															A__				=   rescaled_image.astype(np.floataa )
										else:
															A__				=   rescale(_lowerCamelCase	,      scale=_lowerCamelCase	,      data_format=_lowerCamelCase	,      **_lowerCamelCase )
										return rescaled_image





					def 						A__  (					self :							Dict	,      _lowerCamelCase :							np.ndarray	,      _lowerCamelCase :							Union[float, List[float]]	,      _lowerCamelCase :							Union[float, List[float]]	,      _lowerCamelCase :							Optional[Union[str, ChannelDimension]] = None	,      **_lowerCamelCase :							str	,      ):
										return normalize(_lowerCamelCase	,      mean=_lowerCamelCase	,      std=_lowerCamelCase	,      data_format=_lowerCamelCase	,      **_lowerCamelCase )





					def 						A__  (					self :							int	,      _lowerCamelCase :							ImageInput	,      _lowerCamelCase :							bool = None	,      _lowerCamelCase :							Dict[str, int] = None	,      _lowerCamelCase :							Any=None	,      _lowerCamelCase :							bool = None	,      _lowerCamelCase :							Dict[str, int] = None	,      _lowerCamelCase :							bool = None	,      _lowerCamelCase :							float = None	,      _lowerCamelCase :							bool = 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 :							ChannelDimension = ChannelDimension.FIRST	,      **_lowerCamelCase :							Tuple	,      ):
										A__				=   do_resize if do_resize is not None else self.do_resize
										A__				=   resample if resample is not None else self.resample
										A__				=   do_center_crop if do_center_crop is not None else self.do_center_crop
										A__				=   do_rescale if do_rescale is not None else self.do_rescale
										A__				=   rescale_factor if rescale_factor is not None else self.rescale_factor
										A__				=   rescale_offset if rescale_offset is not None else self.rescale_offset
										A__				=   do_normalize if do_normalize is not None else self.do_normalize
										A__				=   image_mean if image_mean is not None else self.image_mean
										A__				=   image_std if image_std is not None else self.image_std
										A__				=   include_top if include_top is not None else self.include_top

										A__				=   size if size is not None else self.size
										A__				=   get_size_dict(_lowerCamelCase )
										A__				=   crop_size if crop_size is not None else self.crop_size
										A__				=   get_size_dict(_lowerCamelCase	,      param_name='''crop_size''' )

										A__				=   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 or resample is None:
															raise ValueError('''Size and resample 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__				=   [to_numpy_array(_lowerCamelCase ) for image in images]

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

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

										if do_rescale:
															A__				=   [self.rescale(image=_lowerCamelCase	,      scale=_lowerCamelCase	,      offset=_lowerCamelCase ) for image in images]

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

										if include_top:
															A__				=   [self.normalize(image=_lowerCamelCase	,      mean=[0, 0, 0]	,      std=_lowerCamelCase ) for image in images]

										A__				=   [to_channel_dimension_format(_lowerCamelCase	,      _lowerCamelCase ) for image in images]

										A__				=   {"pixel_values": images}
										return BatchFeature(data=_lowerCamelCase	,      tensor_type=_lowerCamelCase )








"""simple docstring"""







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case	:				List[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          with patch.object(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



