'''simple docstring'''





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_:					Union[str, Any]  ='sshleifer/bart-tiny-random'
SCREAMING_SNAKE_CASE_:					List[str]  ='patrickvonplaten/t5-tiny-random'




@require_torch
class 	__A				(			unittest.TestCase     ):



    @cached_property
    def  _lowercase						(self   :   Dict			):
         return AutoConfig.from_pretrained(__a			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_   ,			*UpperCAmelCase_	   =       create_student_by_copying_alternating_layers(__a       ,      tempfile.mkdtemp()       ,      e=1       ,      d=1			)
         self.assertEqual(student.config.num_hidden_layers       ,      1			)



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_   ,			*UpperCAmelCase_	   =       create_student_by_copying_alternating_layers(__a       ,      tempfile.mkdtemp()       ,      e=1       ,      d=__a			)



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_   ,			*UpperCAmelCase_	   =       create_student_by_copying_alternating_layers(__a       ,      tempfile.mkdtemp()       ,      e=1       ,      d=__a			)
         self.assertEqual(student.config.encoder_layers       ,      1			)
         self.assertEqual(student.config.decoder_layers       ,      self.teacher_config.encoder_layers			)



    def  _lowercase						(self   :   List[str]			):
         UpperCAmelCase_   ,			*UpperCAmelCase_	   =       create_student_by_copying_alternating_layers(__a       ,      tempfile.mkdtemp()       ,      e=1       ,      d=1			)
         self.assertEqual(student.config.encoder_layers       ,      1			)
         self.assertEqual(student.config.decoder_layers       ,      1			)






    def  _lowercase						(self   :   Tuple			):
         with self.assertRaises(__a			):
              create_student_by_copying_alternating_layers(__a       ,      tempfile.mkdtemp()       ,      e=__a       ,      d=__a			)





'''simple docstring'''





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




