




'''simple docstring'''

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


a  :				Optional[Any]									=  logging.get_logger(__name__)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					None:

					'''simple docstring'''

					snake_case_									=				nn.ModuleList([src_layers[i] for i in layers_to_copy]	)
					assert len(__UpperCAmelCase	) == len(__UpperCAmelCase	), F"{len(__UpperCAmelCase	)} != {len(__UpperCAmelCase	)}"
					dest_layers.load_state_dict(layers_to_copy.state_dict()	)


a  :				Union[str, Any]									=  {
    # 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))},
}
a  :				List[str]									=  {
    # 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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					try:
										snake_case_									=				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(__UpperCAmelCase	)	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					List[int]:

					'''simple docstring'''

					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(__UpperCAmelCase	)	)
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase = "student",      __UpperCAmelCase = None,      __UpperCAmelCase = None,      __UpperCAmelCase=False,      __UpperCAmelCase=None,      __UpperCAmelCase=None,      **__UpperCAmelCase,      )		->					Tuple[PreTrainedModel, List[int], List[int]]:

					'''simple docstring'''

					snake_case_									=				'''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(__UpperCAmelCase,      __UpperCAmelCase	):
										AutoTokenizer.from_pretrained(__UpperCAmelCase	).save_pretrained(__UpperCAmelCase	)  # purely for convenience
										snake_case_									=				AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase	).eval()
					else:
										assert isinstance(__UpperCAmelCase,      __UpperCAmelCase	), F"teacher must be a model or string got type {type(__UpperCAmelCase	)}"
					snake_case_									=				teacher.config.to_diff_dict()

					try:
										snake_case_			,snake_case_									=				teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															snake_case_									=				teacher_e
										if d is None:
															snake_case_									=				teacher_d
										init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}	)
					except AttributeError:  # T5
										if hasattr(teacher.config,      '''num_encoder_layers'''	):
															snake_case_			,snake_case_									=				teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															snake_case_			,snake_case_									=				teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															snake_case_									=				teacher_e
										if d is None:
															snake_case_									=				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(__UpperCAmelCase	)

					# Copy weights
					snake_case_									=				teacher.config_class(**__UpperCAmelCase	)
					snake_case_									=				AutoModelForSeqaSeqLM.from_config(__UpperCAmelCase	)
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					snake_case_									=				student.load_state_dict(teacher.state_dict(),      strict=__UpperCAmelCase	)
					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
										snake_case_			,snake_case_									=				list(range(__UpperCAmelCase	)	), list(range(__UpperCAmelCase	)	)
										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(__UpperCAmelCase	)
										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:
										snake_case_									=				pick_layers_to_copy(__UpperCAmelCase,      __UpperCAmelCase	)
					if d_layers_to_copy is None:
										snake_case_									=				pick_layers_to_copy(__UpperCAmelCase,      __UpperCAmelCase	)

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







'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, 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, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 						a				(   _lowerCamelCase   ,     _lowerCamelCase   ,     unittest.TestCase     ):
				snake_case_      =		CycleDiffusionPipeline
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    "negative_prompt",
				    "height",
				    "width",
				    "negative_prompt_embeds",
				}
				snake_case_      =		PipelineTesterMixin.required_optional_params - {"latents"}
				snake_case_      =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}     )
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS
				snake_case_      =		IMAGE_TO_IMAGE_IMAGE_PARAMS


				def   A_ (		self    :					Tuple					):
									torch.manual_seed(0					)
									snake_case_									=				UNetaDConditionModel(
									    block_out_channels=(32, 64)       ,				layers_per_block=2       ,				sample_size=32       ,				in_channels=4       ,				out_channels=4       ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,				cross_attention_dim=32       ,				)
									snake_case_									=				DDIMScheduler(
									    beta_start=0.0_0085       ,				beta_end=0.012       ,				beta_schedule='''scaled_linear'''       ,				num_train_timesteps=1000       ,				clip_sample=lowercase_       ,				set_alpha_to_one=lowercase_       ,				)
									torch.manual_seed(0					)
									snake_case_									=				AutoencoderKL(
									    block_out_channels=[32, 64]       ,				in_channels=3       ,				out_channels=3       ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,				latent_channels=4       ,				)
									torch.manual_seed(0					)
									snake_case_									=				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       ,				)
									snake_case_									=				CLIPTextModel(lowercase_					)
									snake_case_									=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

									snake_case_									=				{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''vae''': vae,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''safety_checker''': None,
									    '''feature_extractor''': None,
									}
									return components


				def   A_ (		self    :					Any       ,				lowercase_    :					int       ,				lowercase_    :					Optional[Any]=0					):
									snake_case_									=				floats_tensor((1, 3, 32, 32)       ,				rng=random.Random(lowercase_					)					).to(lowercase_					)
									snake_case_									=				image / 2 + 0.5
									if str(lowercase_					).startswith('''mps'''					):
														snake_case_									=				torch.manual_seed(lowercase_					)
									else:
														snake_case_									=				torch.Generator(device=lowercase_					).manual_seed(lowercase_					)
									snake_case_									=				{
									    '''prompt''': '''An astronaut riding an elephant''',
									    '''source_prompt''': '''An astronaut riding a horse''',
									    '''image''': image,
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''eta''': 0.1,
									    '''strength''': 0.8,
									    '''guidance_scale''': 3,
									    '''source_guidance_scale''': 1,
									    '''output_type''': '''numpy''',
									}
									return inputs


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator

									snake_case_									=				self.get_dummy_components()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

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


				@unittest.skipIf(torch_device != '''cuda'''       ,				'''This test requires a GPU'''					)
				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				self.get_dummy_components()
									for name, module in components.items():
														if hasattr(lowercase_       ,				'''half'''					):
																			snake_case_									=				module.half()
									snake_case_									=				CycleDiffusionPipeline(**lowercase_					)
									snake_case_									=				pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)

									snake_case_									=				self.get_dummy_inputs(lowercase_					)
									snake_case_									=				pipe(**lowercase_					)
									snake_case_									=				output.images

									snake_case_									=				images[0, -3:, -3:, -1]

									assert images.shape == (1, 32, 32, 3)
									snake_case_									=				np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

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


				@skip_mps
				def   A_ (		self    :					Optional[int]					):
									return super().test_save_load_local()


				@unittest.skip('''non-deterministic pipeline'''					)
				def   A_ (		self    :					List[Any]					):
									return super().test_inference_batch_single_identical()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_dict_tuple_outputs_equivalent()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_save_load_optional_components()


				@skip_mps
				def   A_ (		self    :					Union[str, Any]					):
									return super().test_attention_slicing_forward_pass()







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


				def   A_ (		self    :					List[Any]					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(
									    lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_       ,				torch_dtype=torch.floataa       ,				revision='''fp16'''					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

									# the values aren't exactly equal, but the images look the same visually
									assert np.abs(image - expected_image					).max() < 5e-1


				def   A_ (		self    :					List[str]					):
									snake_case_									=				load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/cycle-diffusion/black_colored_car.png'''					)
									snake_case_									=				load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
									snake_case_									=				init_image.resize((512, 512)					)

									snake_case_									=				'''CompVis/stable-diffusion-v1-4'''
									snake_case_									=				DDIMScheduler.from_pretrained(lowercase_       ,				subfolder='''scheduler'''					)
									snake_case_									=				CycleDiffusionPipeline.from_pretrained(lowercase_       ,				scheduler=lowercase_       ,				safety_checker=lowercase_					)

									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									snake_case_									=				'''A black colored car'''
									snake_case_									=				'''A blue colored car'''

									snake_case_									=				torch.manual_seed(0					)
									snake_case_									=				pipe(
									    prompt=lowercase_       ,				source_prompt=lowercase_       ,				image=lowercase_       ,				num_inference_steps=100       ,				eta=0.1       ,				strength=0.85       ,				guidance_scale=3       ,				source_guidance_scale=1       ,				generator=lowercase_       ,				output_type='''np'''       ,				)
									snake_case_									=				output.images

									assert np.abs(image - expected_image					).max() < 2e-2

