


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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


lowerCAmelCase							=						logging.get_logger(__name__)




class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		Optional[int]        =	['''pixel_values''']


	def __init__(						self							,    lowercase__ = True							,    lowercase__ = None							,    lowercase__ = 0.9							,    lowercase__ = PILImageResampling.BICUBIC							,    lowercase__ = True							,    lowercase__ = None							,    lowercase__ = 1 / 2_5_5							,    lowercase__ = True							,    lowercase__ = True							,    lowercase__ = None							,    lowercase__ = None							,    **lowercase__							,    ):
							super().__init__(**lowercase__)
							__UpperCAmelCase :       Any  						=  size if size is not None else {'''shortest_edge''': 2_2_4}
							__UpperCAmelCase :       List[Any]  						=  get_size_dict(lowercase__							,    default_to_square=lowercase__)
							__UpperCAmelCase :       int  						=  crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							__UpperCAmelCase :       Optional[Any]  						=  get_size_dict(lowercase__							,    param_name='''crop_size''')

							__UpperCAmelCase :       List[Any]  						=  do_resize
							__UpperCAmelCase :       Any  						=  size
							__UpperCAmelCase :       str  						=  crop_pct
							__UpperCAmelCase :       Tuple  						=  resample
							__UpperCAmelCase :       Optional[int]  						=  do_center_crop
							__UpperCAmelCase :       Optional[int]  						=  crop_size
							__UpperCAmelCase :       List[Any]  						=  do_rescale
							__UpperCAmelCase :       Dict  						=  rescale_factor
							__UpperCAmelCase :       Tuple  						=  do_normalize
							__UpperCAmelCase :       Dict  						=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							__UpperCAmelCase :       str  						=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = PILImageResampling.BICUBIC							,    lowercase__ = None							,    **lowercase__							,    ):
							__UpperCAmelCase :       Union[str, Any]  						=  get_size_dict(lowercase__							,    default_to_square=lowercase__)
							if "shortest_edge" not in size and ("height" not in size or "width" not in size):
													raise ValueError(F"size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
							if crop_pct is not None:
													if "shortest_edge" in size:
																			__UpperCAmelCase :       Optional[Any]  						=  int(size['''shortest_edge'''] / crop_pct)
													elif "height" in size and "width" in size:
																			if size["height"] == size["width"]:
																									__UpperCAmelCase :       Union[str, Any]  						=  int(size['''height'''] / crop_pct)
																			else:
																									__UpperCAmelCase :       Dict  						=  (int(size['''height'''] / crop_pct), int(size['''width'''] / crop_pct))
													else:
																			raise ValueError('''Invalid size for resize: {}'''.format(lowercase__))

													__UpperCAmelCase :       Tuple  						=  get_resize_output_image_size(lowercase__							,    size=lowercase__							,    default_to_square=lowercase__)
							else:
													if "shortest_edge" in size:
																			__UpperCAmelCase :       int  						=  get_resize_output_image_size(lowercase__							,    size=size['''shortest_edge''']							,    default_to_square=lowercase__)
													elif "height" in size and "width" in size:
																			__UpperCAmelCase :       Any  						=  (size['''height'''], size['''width'''])
													else:
																			raise ValueError('''Invalid size for resize: {}'''.format(lowercase__))

							return resize(lowercase__							,    size=lowercase__							,    resample=lowercase__							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    **lowercase__							,    ):
							__UpperCAmelCase :       int  						=  get_size_dict(lowercase__)
							if "height" not in size or "width" not in size:
													raise ValueError(F"size must contain 'height' and 'width' as keys. Got {size.keys()}")
							return center_crop(lowercase__							,    size=(size['''height'''], size['''width'''])							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    **lowercase__							,    ):
							return rescale(lowercase__							,    scale=lowercase__							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    **lowercase__							,    ):
							return normalize(lowercase__							,    mean=lowercase__							,    std=lowercase__							,    data_format=lowercase__							,    **lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = ChannelDimension.FIRST							,    **lowercase__							,    ):
							__UpperCAmelCase :       str  						=  do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase :       str  						=  crop_pct if crop_pct is not None else self.crop_pct
							__UpperCAmelCase :       Dict  						=  resample if resample is not None else self.resample
							__UpperCAmelCase :       List[Any]  						=  do_center_crop if do_center_crop is not None else self.do_center_crop
							__UpperCAmelCase :       Union[str, Any]  						=  do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase :       Union[str, Any]  						=  rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase :       int  						=  do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase :       int  						=  image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase :       Any  						=  image_std if image_std is not None else self.image_std

							__UpperCAmelCase :       Tuple  						=  size if size is not None else self.size
							__UpperCAmelCase :       Tuple  						=  get_size_dict(lowercase__							,    default_to_square=lowercase__)
							__UpperCAmelCase :       Union[str, Any]  						=  crop_size if crop_size is not None else self.crop_size
							__UpperCAmelCase :       Optional[int]  						=  get_size_dict(lowercase__							,    param_name='''crop_size''')

							__UpperCAmelCase :       str  						=  make_list_of_images(lowercase__)

							if not valid_images(lowercase__):
													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_pct is None:
													raise ValueError('''Crop_pct 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 :       int  						=  [to_numpy_array(lowercase__) for image in images]

							if do_resize:
													__UpperCAmelCase :       str  						=  [self.resize(image=lowercase__							,    size=lowercase__							,    crop_pct=lowercase__							,    resample=lowercase__) for image in images]

							if do_center_crop:
													__UpperCAmelCase :       List[Any]  						=  [self.center_crop(image=lowercase__							,    size=lowercase__) for image in images]

							if do_rescale:
													__UpperCAmelCase :       Tuple  						=  [self.rescale(image=lowercase__							,    scale=lowercase__) for image in images]

							if do_normalize:
													__UpperCAmelCase :       List[str]  						=  [self.normalize(image=lowercase__							,    mean=lowercase__							,    std=lowercase__) for image in images]

							__UpperCAmelCase :       Union[str, Any]  						=  [to_channel_dimension_format(lowercase__							,    lowercase__) for image in images]

							__UpperCAmelCase :       Optional[int]  						=  {'''pixel_values''': images}
							return BatchFeature(data=lowercase__							,    tensor_type=lowercase__)





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


lowerCAmelCase							=						"""sshleifer/bart-tiny-random"""
lowerCAmelCase							=						"""patrickvonplaten/t5-tiny-random"""




@require_torch
class        lowerCamelCase      (     unittest.TestCase   ):


	@cached_property
	def    A(						self):
							return AutoConfig.from_pretrained(lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Dict  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=1)
							self.assertEqual(student.config.num_hidden_layers							,    1)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Union[str, Any]  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=lowercase__)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Tuple  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=lowercase__)
							self.assertEqual(student.config.encoder_layers							,    1)
							self.assertEqual(student.config.decoder_layers							,    self.teacher_config.encoder_layers)


	def    A(						self):
							__UpperCAmelCase						,			*__UpperCAmelCase :       Dict  						=  create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=1							,    d=1)
							self.assertEqual(student.config.encoder_layers							,    1)
							self.assertEqual(student.config.decoder_layers							,    1)


	def    A(						self):
							with self.assertRaises(lowercase__):
													create_student_by_copying_alternating_layers(lowercase__							,    tempfile.mkdtemp()							,    e=lowercase__							,    d=lowercase__)

