



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor


class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					def __init__(     self					:       Optional[Any]				,					_snake_case					:       Dict				,					_snake_case					:       List[str]=13				,					_snake_case					:       Any=3				,					_snake_case					:       List[Any]=2_24				,					_snake_case					:       List[str]=30				,					_snake_case					:       List[str]=4_00				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Union[str, Any]=True				,					_snake_case					:       List[str]=[0.5, 0.5, 0.5]				,					_snake_case					:       Optional[Any]=[0.5, 0.5, 0.5]				,					):






								"""simple docstring"""


								A__       =    size if size is not None else {'height': 18, 'width': 18}
								A__       =    parent
								A__       =    batch_size
								A__       =    num_channels
								A__       =    image_size
								A__       =    min_resolution
								A__       =    max_resolution
								A__       =    do_resize
								A__       =    size
								A__       =    do_normalize
								A__       =    image_mean
								A__       =    image_std



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}







@require_torch
@require_vision
class      __lowerCAmelCase     (		UpperCAmelCase_							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		List[str]			       =    ViTImageProcessor if is_vision_available() else None



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    EfficientFormerImageProcessorTester(self					)



					@property
					def     _a							(     self					:       int					):






								"""simple docstring"""


								return self.image_proc_tester.prepare_image_processor_dict()



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(__A				,					'image_mean'					)					)
								self.assertTrue(hasattr(__A				,					'image_std'					)					)
								self.assertTrue(hasattr(__A				,					'do_normalize'					)					)
								self.assertTrue(hasattr(__A				,					'do_resize'					)					)
								self.assertTrue(hasattr(__A				,					'size'					)					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								A__       =    prepare_image_inputs(self.image_proc_tester				,					equal_resolution=__A					)
								for image in image_inputs:
											self.assertIsInstance(__A				,					Image.Image					)

								# Test not batched input
								A__       =    image_processor(image_inputs[0]				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['height'],
								        self.image_proc_tester.size['width'],
								    )				,					)

								# Test batched
								A__       =    image_processor(__A				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['height'],
								        self.image_proc_tester.size['width'],
								    )				,					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								A__       =    prepare_image_inputs(self.image_proc_tester				,					equal_resolution=__A				,					numpify=__A					)
								for image in image_inputs:
											self.assertIsInstance(__A				,					np.ndarray					)

								# Test not batched input
								A__       =    image_processor(image_inputs[0]				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['height'],
								        self.image_proc_tester.size['width'],
								    )				,					)

								# Test batched
								A__       =    image_processor(__A				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['height'],
								        self.image_proc_tester.size['width'],
								    )				,					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								A__       =    prepare_image_inputs(self.image_proc_tester				,					equal_resolution=__A				,					torchify=__A					)
								for image in image_inputs:
											self.assertIsInstance(__A				,					torch.Tensor					)

								# Test not batched input
								A__       =    image_processor(image_inputs[0]				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['height'],
								        self.image_proc_tester.size['width'],
								    )				,					)

								# Test batched
								A__       =    image_processor(__A				,					return_tensors='pt'					).pixel_values
								self.assertEqual(
								    encoded_images.shape				,					(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['height'],
								        self.image_proc_tester.size['width'],
								    )				,					)





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


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


@require_torch
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								return AutoConfig.from_pretrained(_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=1					)
								self.assertEqual(student.config.num_hidden_layers				,					1					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)
								self.assertEqual(student.config.encoder_layers				,					1					)
								self.assertEqual(student.config.decoder_layers				,					self.teacher_config.encoder_layers					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_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					:       str					):






								"""simple docstring"""


								with self.assertRaises(_snake_case					):
											create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=_snake_case				,					d=_snake_case					)




