




import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


__a          =			'sshleifer/bart-tiny-random'
__a          =			'patrickvonplaten/t5-tiny-random'

@require_torch
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		int:
         return AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[int]       =      create_student_by_copying_alternating_layers(_SCREAMING_SNAKE_CASE       ,tempfile.mkdtemp()       ,e=1       ,d=1  )
         self.assertEqual(student.config.num_hidden_layers       ,1  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Tuple       =      create_student_by_copying_alternating_layers(_SCREAMING_SNAKE_CASE       ,tempfile.mkdtemp()       ,e=1       ,d=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[Any]       =      create_student_by_copying_alternating_layers(_SCREAMING_SNAKE_CASE       ,tempfile.mkdtemp()       ,e=1       ,d=_SCREAMING_SNAKE_CASE  )
         self.assertEqual(student.config.encoder_layers       ,1  )
         self.assertEqual(student.config.decoder_layers       ,self.teacher_config.encoder_layers  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Any       =      create_student_by_copying_alternating_layers(_SCREAMING_SNAKE_CASE       ,tempfile.mkdtemp()       ,e=1       ,d=1  )
         self.assertEqual(student.config.encoder_layers       ,1  )
         self.assertEqual(student.config.decoder_layers       ,1  )





    def 							a__				(				self  )			->		Optional[Any]:
         with self.assertRaises(_SCREAMING_SNAKE_CASE  ):
              create_student_by_copying_alternating_layers(_SCREAMING_SNAKE_CASE       ,tempfile.mkdtemp()       ,e=_SCREAMING_SNAKE_CASE       ,d=_SCREAMING_SNAKE_CASE  )





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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if isinstance(_lowercase			,						(list, tuple)				) and isinstance(videos[0]			,						(list, tuple)				) and is_valid_image(videos[0][0]				):
          return videos

     elif isinstance(_lowercase			,						(list, tuple)				) and is_valid_image(videos[0]				):
          return [videos]

     elif is_valid_image(_lowercase				):
          return [[videos]]

     raise ValueError(f'''Could not make batched video from {videos}'''				)



class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				['''pixel_values''']





    def __init__(				self       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = 1 / 255       ,_SCREAMING_SNAKE_CASE = True       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		None:
         super().__init__(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      size if size is not None else {'''shortest_edge''': 224}
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         UpperCAmelCase_      :							List[str]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         UpperCAmelCase_      :							str       =      do_resize
         UpperCAmelCase_      :							Union[str, Any]       =      size
         UpperCAmelCase_      :							int       =      do_center_crop
         UpperCAmelCase_      :							List[str]       =      crop_size
         UpperCAmelCase_      :							Optional[int]       =      resample
         UpperCAmelCase_      :							List[Any]       =      do_rescale
         UpperCAmelCase_      :							Tuple       =      rescale_factor
         UpperCAmelCase_      :							Optional[Any]       =      do_normalize
         UpperCAmelCase_      :							int       =      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else IMAGENET_STANDARD_STD





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         if "shortest_edge" in size:
              UpperCAmelCase_      :							Dict       =      get_resize_output_image_size(_SCREAMING_SNAKE_CASE       ,size['''shortest_edge''']       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         elif "height" in size and "width" in size:
              UpperCAmelCase_      :							Tuple       =      (size['''height'''], size['''width'''])
         else:
              raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
         return resize(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         UpperCAmelCase_      :							str       =      get_size_dict(_SCREAMING_SNAKE_CASE  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''  )
         return center_crop(_SCREAMING_SNAKE_CASE       ,size=(size['''height'''], size['''width'''])       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         return rescale(_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,**_SCREAMING_SNAKE_CASE       ,)			->		np.ndarray:
         return normalize(_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,)			->		np.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.
         UpperCAmelCase_      :							Any       =      to_numpy_array(_SCREAMING_SNAKE_CASE  )

         if do_resize:
              UpperCAmelCase_      :							Union[str, Any]       =      self.resize(image=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE  )

         if do_center_crop:
              UpperCAmelCase_      :							Optional[int]       =      self.center_crop(_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE  )

         if do_rescale:
              UpperCAmelCase_      :							str       =      self.rescale(image=_SCREAMING_SNAKE_CASE       ,scale=_SCREAMING_SNAKE_CASE  )

         if do_normalize:
              UpperCAmelCase_      :							List[Any]       =      self.normalize(image=_SCREAMING_SNAKE_CASE       ,mean=_SCREAMING_SNAKE_CASE       ,std=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      to_channel_dimension_format(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         return image





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST       ,**_SCREAMING_SNAKE_CASE       ,)			->		PIL.Image.Image:
         UpperCAmelCase_      :							Dict       =      do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_      :							int       =      resample if resample is not None else self.resample
         UpperCAmelCase_      :							List[str]       =      do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_      :							Optional[Any]       =      do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_      :							List[Any]       =      rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_      :							Tuple       =      do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_      :							Optional[int]       =      image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_      :							Optional[int]       =      image_std if image_std is not None else self.image_std

         UpperCAmelCase_      :							List[str]       =      size if size is not None else self.size
         UpperCAmelCase_      :							Optional[int]       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,default_to_square=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_      :							Any       =      get_size_dict(_SCREAMING_SNAKE_CASE       ,param_name='''crop_size'''  )

         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.'''  )

         UpperCAmelCase_      :							List[Any]       =      make_batched(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      [
             [
                 self._preprocess_image(
                     image=_SCREAMING_SNAKE_CASE       ,do_resize=_SCREAMING_SNAKE_CASE       ,size=_SCREAMING_SNAKE_CASE       ,resample=_SCREAMING_SNAKE_CASE       ,do_center_crop=_SCREAMING_SNAKE_CASE       ,crop_size=_SCREAMING_SNAKE_CASE       ,do_rescale=_SCREAMING_SNAKE_CASE       ,rescale_factor=_SCREAMING_SNAKE_CASE       ,do_normalize=_SCREAMING_SNAKE_CASE       ,image_mean=_SCREAMING_SNAKE_CASE       ,image_std=_SCREAMING_SNAKE_CASE       ,data_format=_SCREAMING_SNAKE_CASE       ,)
                 for img in video
             ]
             for video in videos
         ]

         UpperCAmelCase_      :							Any       =      {'''pixel_values''': videos}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE       ,tensor_type=_SCREAMING_SNAKE_CASE  )