



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       =      {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Tuple			       =    'data2vec-vision'



					def __init__(     self					:       Optional[Any]				,					_snake_case					:       Optional[int]=7_68				,					_snake_case					:       List[Any]=12				,					_snake_case					:       Optional[int]=12				,					_snake_case					:       int=30_72				,					_snake_case					:       Union[str, Any]="gelu"				,					_snake_case					:       Tuple=0.0				,					_snake_case					:       Union[str, Any]=0.0				,					_snake_case					:       List[str]=0.02				,					_snake_case					:       List[str]=1E-12				,					_snake_case					:       int=2_24				,					_snake_case					:       Dict=16				,					_snake_case					:       Tuple=3				,					_snake_case					:       Dict=False				,					_snake_case					:       Tuple=False				,					_snake_case					:       List[str]=False				,					_snake_case					:       Any=False				,					_snake_case					:       Optional[Any]=0.1				,					_snake_case					:       Optional[int]=0.1				,					_snake_case					:       Dict=True				,					_snake_case					:       str=[3, 5, 7, 11]				,					_snake_case					:       Optional[int]=[1, 2, 3, 6]				,					_snake_case					:       str=True				,					_snake_case					:       Tuple=0.4				,					_snake_case					:       List[str]=2_56				,					_snake_case					:       Optional[Any]=1				,					_snake_case					:       Any=False				,					_snake_case					:       Union[str, Any]=2_55				,					**_snake_case					:       Union[str, Any]				,					):






								"""simple docstring"""


								super().__init__(**UpperCAmelCase__					)

								A__       =    hidden_size
								A__       =    num_hidden_layers
								A__       =    num_attention_heads
								A__       =    intermediate_size
								A__       =    hidden_act
								A__       =    hidden_dropout_prob
								A__       =    attention_probs_dropout_prob
								A__       =    initializer_range
								A__       =    layer_norm_eps

								A__       =    image_size
								A__       =    patch_size
								A__       =    num_channels
								A__       =    use_mask_token
								A__       =    use_absolute_position_embeddings
								A__       =    use_relative_position_bias
								A__       =    use_shared_relative_position_bias
								A__       =    layer_scale_init_value
								A__       =    drop_path_rate
								A__       =    use_mean_pooling
								# decode head attributes (semantic segmentation)
								A__       =    out_indices
								A__       =    pool_scales
								# auxiliary head attributes (semantic segmentation)
								A__       =    use_auxiliary_head
								A__       =    auxiliary_loss_weight
								A__       =    auxiliary_channels
								A__       =    auxiliary_num_convs
								A__       =    auxiliary_concat_input
								A__       =    semantic_loss_ignore_index







class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Optional[int]			       =    version.parse("1.11"      )



					@property
					def     _a							(     self					:       int					):






								"""simple docstring"""


								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ]					)



					@property
					def     _a							(     self					:       str					):






								"""simple docstring"""


								return 1E-4





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					)




