

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    1
										_UpperCAmelCase       :		Optional[int] 		=    3
										_UpperCAmelCase       :		Union[str, Any] 		=    (32, 32)

										_UpperCAmelCase       :		str 		=    floats_tensor((batch_size, num_channels) + sizes						,					rng=random.Random(0	)	).to(A_	)
										return image


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Tuple 		=    UNetaDConditionModel(
										    block_out_channels=(32, 32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=7						,					out_channels=4						,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D")						,					up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D")						,					cross_attention_dim=32						,					attention_head_dim=8						,					use_linear_projection=A_						,					only_cross_attention=(True, True, False)						,					num_class_embeds=100						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Any 		=    AutoencoderKL(
										    block_out_channels=[32, 32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=4						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=32						,					intermediate_size=37						,					layer_norm_eps=1e-05						,					num_attention_heads=4						,					num_hidden_layers=5						,					pad_token_id=1						,					vocab_size=1000						,					hidden_act="gelu"						,					projection_dim=512						,					)
										return CLIPTextModel(A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    "cpu"  # ensure determinism for the device-dependent torch.Generator
										_UpperCAmelCase       :		Optional[int] 		=    self.dummy_cond_unet_upscale
										_UpperCAmelCase       :		Dict 		=    DDPMScheduler()
										_UpperCAmelCase       :		List[str] 		=    DDIMScheduler(prediction_type="v_prediction"	)
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_vae
										_UpperCAmelCase       :		Tuple 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Any 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										_UpperCAmelCase       :		Union[str, Any] 		=    self.dummy_image.cpu().permute(0						,					2						,					3						,					1	)[0]
										_UpperCAmelCase       :		str 		=    Image.fromarray(np.uinta(A_	)	).convert("RGB"	).resize((64, 64)	)

										# make sure here that pndm scheduler skips prk
										_UpperCAmelCase       :		List[str] 		=    StableDiffusionUpscalePipeline(
										    unet=A_						,					low_res_scheduler=A_						,					scheduler=A_						,					vae=A_						,					text_encoder=A_						,					tokenizer=A_						,					max_noise_level=350						,					)
										_UpperCAmelCase       :		Tuple 		=    sd_pipe.to(A_	)
										sd_pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		List[Any] 		=    "A painting of a squirrel eating a burger"
										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    sd_pipe(
										    [prompt]						,					image=A_						,					generator=A_						,					guidance_scale=6.0						,					noise_level=20						,					num_inference_steps=2						,					output_type="np"						,					)

										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    sd_pipe(
										    [prompt]						,					image=A_						,					generator=A_						,					guidance_scale=6.0						,					noise_level=20						,					num_inference_steps=2						,					output_type="np"						,					return_dict=A_						,					)[0]

										_UpperCAmelCase       :		Dict 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Any 		=    image_from_tuple[0, -3:, -3:, -1]

										_UpperCAmelCase       :		int 		=    low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)
										_UpperCAmelCase       :		Optional[int] 		=    np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    "cpu"  # ensure determinism for the device-dependent torch.Generator
										_UpperCAmelCase       :		Union[str, Any] 		=    self.dummy_cond_unet_upscale
										_UpperCAmelCase       :		Optional[int] 		=    DDPMScheduler()
										_UpperCAmelCase       :		Optional[Any] 		=    DDIMScheduler(prediction_type="v_prediction"	)
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_vae
										_UpperCAmelCase       :		int 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Any 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										_UpperCAmelCase       :		int 		=    self.dummy_image.cpu().permute(0						,					2						,					3						,					1	)[0]
										_UpperCAmelCase       :		List[str] 		=    Image.fromarray(np.uinta(A_	)	).convert("RGB"	).resize((64, 64)	)

										# make sure here that pndm scheduler skips prk
										_UpperCAmelCase       :		List[str] 		=    StableDiffusionUpscalePipeline(
										    unet=A_						,					low_res_scheduler=A_						,					scheduler=A_						,					vae=A_						,					text_encoder=A_						,					tokenizer=A_						,					max_noise_level=350						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    sd_pipe.to(A_	)
										sd_pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    "A painting of a squirrel eating a burger"
										_UpperCAmelCase       :		Dict 		=    sd_pipe(
										    2 * [prompt]						,					image=2 * [low_res_image]						,					guidance_scale=6.0						,					noise_level=20						,					num_inference_steps=2						,					output_type="np"						,					)
										_UpperCAmelCase       :		List[Any] 		=    output.images
										assert image.shape[0] == 2

										_UpperCAmelCase       :		List[str] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    sd_pipe(
										    [prompt]						,					image=A_						,					generator=A_						,					num_images_per_prompt=2						,					guidance_scale=6.0						,					noise_level=20						,					num_inference_steps=2						,					output_type="np"						,					)
										_UpperCAmelCase       :		Tuple 		=    output.images
										assert image.shape[0] == 2





						@unittest.skipIf(torch_device != "cuda"						,					"This test requires a GPU"	)
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.dummy_cond_unet_upscale
										_UpperCAmelCase       :		Tuple 		=    DDPMScheduler()
										_UpperCAmelCase       :		Tuple 		=    DDIMScheduler(prediction_type="v_prediction"	)
										_UpperCAmelCase       :		Any 		=    self.dummy_vae
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Tuple 		=    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

										_UpperCAmelCase       :		List[str] 		=    self.dummy_image.cpu().permute(0						,					2						,					3						,					1	)[0]
										_UpperCAmelCase       :		Union[str, Any] 		=    Image.fromarray(np.uinta(A_	)	).convert("RGB"	).resize((64, 64)	)

										# put models in fp16, except vae as it overflows in fp16
										_UpperCAmelCase       :		Any 		=    unet.half()
										_UpperCAmelCase       :		int 		=    text_encoder.half()

										# make sure here that pndm scheduler skips prk
										_UpperCAmelCase       :		List[Any] 		=    StableDiffusionUpscalePipeline(
										    unet=A_						,					low_res_scheduler=A_						,					scheduler=A_						,					vae=A_						,					text_encoder=A_						,					tokenizer=A_						,					max_noise_level=350						,					)
										_UpperCAmelCase       :		str 		=    sd_pipe.to(A_	)
										sd_pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Any 		=    "A painting of a squirrel eating a burger"
										_UpperCAmelCase       :		List[Any] 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Dict 		=    sd_pipe(
										    [prompt]						,					image=A_						,					generator=A_						,					num_inference_steps=2						,					output_type="np"						,					).images

										_UpperCAmelCase       :		List[Any] 		=    low_res_image.size[0] * 4
										assert image.shape == (1, expected_height_width, expected_height_width, 3)







@slow
@require_torch_gpu
class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)
										_UpperCAmelCase       :		Optional[Any] 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat.npy"	)

										_UpperCAmelCase       :		str 		=    "stabilityai/stable-diffusion-x4-upscaler"
										_UpperCAmelCase       :		int 		=    StableDiffusionUpscalePipeline.from_pretrained(A_	)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)
										pipe.enable_attention_slicing()

										_UpperCAmelCase       :		str 		=    "a cat sitting on a park bench"

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Any 		=    pipe(
										    prompt=A_						,					image=A_						,					generator=A_						,					output_type="np"						,					)
										_UpperCAmelCase       :		List[Any] 		=    output.images[0]

										assert image.shape == (512, 512, 3)
										assert np.abs(expected_image - image	).max() < 1e-3


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)
										_UpperCAmelCase       :		int 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"
										    "/upsampled_cat_fp16.npy"	)

										_UpperCAmelCase       :		Optional[Any] 		=    "stabilityai/stable-diffusion-x4-upscaler"
										_UpperCAmelCase       :		Tuple 		=    StableDiffusionUpscalePipeline.from_pretrained(
										    A_						,					torch_dtype=torch.floataa						,					)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)
										pipe.enable_attention_slicing()

										_UpperCAmelCase       :		Optional[int] 		=    "a cat sitting on a park bench"

										_UpperCAmelCase       :		List[str] 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    pipe(
										    prompt=A_						,					image=A_						,					generator=A_						,					output_type="np"						,					)
										_UpperCAmelCase       :		Optional[Any] 		=    output.images[0]

										assert image.shape == (512, 512, 3)
										assert np.abs(expected_image - image	).max() < 5e-1





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										_UpperCAmelCase       :		Optional[int] 		=    load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/sd2-upscale/low_res_cat.png"	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "stabilityai/stable-diffusion-x4-upscaler"
										_UpperCAmelCase       :		Dict 		=    StableDiffusionUpscalePipeline.from_pretrained(
										    A_						,					torch_dtype=torch.floataa						,					)
										pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										_UpperCAmelCase       :		List[str] 		=    "a cat sitting on a park bench"

										_UpperCAmelCase       :		int 		=    torch.manual_seed(0	)
										_UpperCAmelCase       :		Any 		=    pipe(
										    prompt=A_						,					image=A_						,					generator=A_						,					num_inference_steps=5						,					output_type="np"						,					)

										_UpperCAmelCase       :		Dict 		=    torch.cuda.max_memory_allocated()
										# make sure that less than 2.9 GB is allocated
										assert mem_bytes < 2.9 * 10**9





import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  nn.ModuleList  ,      lowerCAmelCase:  List[int] )   ->       None:
				_UpperCAmelCase       :		str 		=    nn.ModuleList([src_layers[i] for i in layers_to_copy] )
				assert len(lowerCAmelCase ) == len(lowerCAmelCase ), F'{len(lowerCAmelCase )} != {len(lowerCAmelCase )}'
				dest_layers.load_state_dict(layers_to_copy.state_dict() )


SCREAMING_SNAKE_CASE_        =      {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
SCREAMING_SNAKE_CASE_        =      {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[str] )   ->       Dict:
				try:
								_UpperCAmelCase       :		int 		=    LAYERS_TO_COPY[n_teacher][n_student]
								return val
				except KeyError:
								if n_student != n_teacher:
												warnings.warn(
												    F'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
												    F' {n_student}' )
								return list(range(lowerCAmelCase ) )

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[Any]  ,      lowerCAmelCase:  Optional[Any] )   ->       List[int]:
				if n_student > n_teacher:
								raise ValueError(F'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}' )
				elif n_teacher == n_student:
								return list(range(lowerCAmelCase ) )
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Union[str, PreTrainedModel]  ,      lowerCAmelCase:  Union[str, Path] = "student"  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  Union[int, None] = None  ,      lowerCAmelCase:  str=False  ,      lowerCAmelCase:  Union[str, Any]=None  ,      lowerCAmelCase:  Any=None  ,      **lowerCAmelCase:  str  ,      )   ->       Tuple[PreTrainedModel, List[int], List[int]]:
				_UpperCAmelCase       :		Dict 		=    "encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
				assert (e is not None) or (d is not None), _msg
				if isinstance(lowerCAmelCase  ,      lowerCAmelCase ):
								AutoTokenizer.from_pretrained(lowerCAmelCase ).save_pretrained(lowerCAmelCase )  # purely for convenience
								_UpperCAmelCase       :		Dict 		=    AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase ).eval()
				else:
								assert isinstance(lowerCAmelCase  ,      lowerCAmelCase ), F'teacher must be a model or string got type {type(lowerCAmelCase )}'
				_UpperCAmelCase       :		List[str] 		=    teacher.config.to_diff_dict()

				try:
								_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    teacher.config.encoder_layers, teacher.config.decoder_layers
								if e is None:
												_UpperCAmelCase       :		int 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[Any] 		=    teacher_d
								init_kwargs.update({"encoder_layers": e, "decoder_layers": d} )
				except AttributeError:  # T5
								if hasattr(teacher.config  ,      "num_encoder_layers" ):
												_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
								else:
												_UpperCAmelCase		,							_UpperCAmelCase       :		Dict 		=    teacher.config.num_layers, teacher.config.num_decoder_layers
								if e is None:
												_UpperCAmelCase       :		List[Any] 		=    teacher_e
								if d is None:
												_UpperCAmelCase       :		Optional[int] 		=    teacher_d
								if hasattr(teacher.config  ,      "num_encoder_layers" ):
												init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d} )
								else:
												init_kwargs.update({"num_layers": e, "num_decoder_layers": d} )

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
				init_kwargs.update(lowerCAmelCase )

				# Copy weights
				_UpperCAmelCase       :		Any 		=    teacher.config_class(**lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    AutoModelForSeqaSeqLM.from_config(lowerCAmelCase )
				# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
				_UpperCAmelCase       :		List[Any] 		=    student.load_state_dict(teacher.state_dict()  ,      strict=lowerCAmelCase )
				assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

				if copy_first_teacher_layers:  # Our copying is done. We just log and save
								_UpperCAmelCase		,							_UpperCAmelCase       :		Any 		=    list(range(lowerCAmelCase ) ), list(range(lowerCAmelCase ) )
								logger.info(
								    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
								    F' {save_path}' )
								student.save_pretrained(lowerCAmelCase )
								return student, e_layers_to_copy, d_layers_to_copy

				# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
				if e_layers_to_copy is None:
								_UpperCAmelCase       :		List[int] 		=    pick_layers_to_copy(lowerCAmelCase  ,      lowerCAmelCase )
				if d_layers_to_copy is None:
								_UpperCAmelCase       :		List[int] 		=    pick_layers_to_copy(lowerCAmelCase  ,      lowerCAmelCase )

				try:
								if hasattr(
								    lowerCAmelCase  ,      "prophetnet" ):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
												copy_layers(teacher.prophetnet.encoder.layers  ,      student.prophetnet.encoder.layers  ,      lowerCAmelCase )
												copy_layers(teacher.prophetnet.decoder.layers  ,      student.prophetnet.decoder.layers  ,      lowerCAmelCase )
								else:
												copy_layers(teacher.model.encoder.layers  ,      student.model.encoder.layers  ,      lowerCAmelCase )
												copy_layers(teacher.model.decoder.layers  ,      student.model.decoder.layers  ,      lowerCAmelCase )
				except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
								copy_layers(teacher.encoder.block  ,      student.encoder.block  ,      lowerCAmelCase )
								copy_layers(teacher.decoder.block  ,      student.decoder.block  ,      lowerCAmelCase )
				logger.info(
				    F'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}' )
				_UpperCAmelCase       :		str 		=    {
				    "teacher_type": teacher.config.model_type,
				    "copied_encoder_layers": e_layers_to_copy,
				    "copied_decoder_layers": d_layers_to_copy,
				}
				student.save_pretrained(lowerCAmelCase )
				# Save information about copying for easier reproducibility

				return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
					fire.Fire(create_student_by_copying_alternating_layers)


