
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


_UpperCAmelCase	:     Optional[Any]		       = """sshleifer/bart-tiny-random"""
_UpperCAmelCase	:     Optional[int]		       = """patrickvonplaten/t5-tiny-random"""







@require_torch
class 				lowercase       ( unittest.TestCase    ):




				@cached_property
				def 					a							(      self ):
											return AutoConfig.from_pretrained(snake_case )




				def 					a							(      self ):
											snake_case_       ,					*snake_case_           =					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 ):
											snake_case_       ,					*snake_case_           =					create_student_by_copying_alternating_layers(snake_case   ,     tempfile.mkdtemp()   ,     e=1   ,     d=snake_case )




				def 					a							(      self ):
											snake_case_       ,					*snake_case_           =					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 ):
											snake_case_       ,					*snake_case_           =					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 ):
											with self.assertRaises(snake_case ):
																		create_student_by_copying_alternating_layers(snake_case   ,     tempfile.mkdtemp()   ,     e=snake_case   ,     d=snake_case )







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)







@dataclass
class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				Union[List[PIL.Image.Image], np.ndarray]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]
				__SCREAMING_SNAKE_CASE      :				Optional[List[bool]]


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_if import IFPipeline
					from .pipeline_if_imgaimg import IFImgaImgPipeline
					from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
					from .pipeline_if_inpainting import IFInpaintingPipeline
					from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
					from .pipeline_if_superresolution import IFSuperResolutionPipeline
					from .safety_checker import IFSafetyChecker
					from .watermark import IFWatermarker




