
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							:str							 =				logging.get_logger(__name__)


def 						UpperCAmelCase			(	a_		,   a_		,   a_						)		->	None:







				"""simple docstring"""



				__A   			= nn.ModuleList([src_layers[i] for i in layers_to_copy]						)
				assert len(a_						) == len(a_						), F'''{len(a_						)} != {len(a_						)}'''
				dest_layers.load_state_dict(layers_to_copy.state_dict()						)


SCREAMING_SNAKE_CASE							:Tuple							 =				{
    # 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							:List[Any]							 =				{
    # 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 						UpperCAmelCase			(	a_		,   a_						)		->	str:







				"""simple docstring"""



				try:
								__A   			= 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(a_						)						)


def 						UpperCAmelCase			(	a_		,   a_						)		->	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(a_						)						)
				elif n_student == 1:
								return [n_teacher - 1]
				else:
								return LAYERS_TO_SUPERVISE[n_teacher][n_student]


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







				"""simple docstring"""



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

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

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

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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


a   :			List[Any]					  =    logging.get_logger(__name__)  # pylint: disable=invalid-name

class 			UpperCamelCase_ (							__magic_name__			):





	def __init__(  self		,		A		,		A		,		A		,		A		,		A		,		A		,		A		,		)			->			Optional[Any]:
								super().__init__()
								self.register_modules(
								    vae=A		,		text_encoder=A		,		tokenizer=A		,		unet=A		,		scheduler=A		,		safety_checker=A		,		feature_extractor=A		,		)





	def  _lowercase(  self		,		A = "auto"    )			->			List[Any]:
								if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															UpperCAmelCase     :		Optional[Any]							     =		self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(A    )





	def  _lowercase(  self    )			->			Dict:
								self.enable_attention_slicing(A    )






	@torch.no_grad()
	def __call__(  self		,		A		,		A = 512		,		A = 512		,		A = 50		,		A = 7.5		,		A = None		,		A = 1		,		A = 0.0		,		A = None		,		A = None		,		A = "pil"		,		A = True		,		A = None		,		A = 1		,		A = None		,		**A		,		)			->			List[Any]:

								if isinstance(A		,		A    ):
															UpperCAmelCase     :		List[str]							     =		1
								elif isinstance(A		,		A    ):
															UpperCAmelCase     :		Dict							     =		len(A    )
								else:
															raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(A    )}'''    )

								if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''    )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(A		,		A    ) or callback_steps <= 0)
								):
															raise ValueError(
															    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
															    f''' {type(A    )}.'''    )

								# get prompt text embeddings
								UpperCAmelCase     :		List[str]							     =		self.tokenizer(
								    A		,		padding="""max_length"""		,		max_length=self.tokenizer.model_max_length		,		return_tensors="""pt"""		,		)
								UpperCAmelCase     :		List[Any]							     =		text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															UpperCAmelCase     :		int							     =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
															logger.warning(
															    """The following part of your input was truncated because CLIP can only handle sequences up to"""
															    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''    )
															UpperCAmelCase     :		Tuple							     =		text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
															UpperCAmelCase     :		Union[str, Any]							     =		self.text_encoder(text_input_ids.to(self.device    )    )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								UpperCAmelCase     ,     UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		text_embeddings.shape
								UpperCAmelCase     :		List[str]							     =		text_embeddings.repeat(1		,		A		,		1    )
								UpperCAmelCase     :		List[Any]							     =		text_embeddings.view(bs_embed * num_images_per_prompt		,		A		,		-1    )

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								UpperCAmelCase     :		Optional[int]							     =		guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
															UpperCAmelCase     :		List[str]
															if negative_prompt is None:
																						UpperCAmelCase     :		Any							     =		[""""""]
															elif type(A    ) is not type(A    ):
																						raise TypeError(
																						    f'''`negative_prompt` should be the same type to `prompt`, but got {type(A    )} !='''
																						    f''' {type(A    )}.'''    )
															elif isinstance(A		,		A    ):
																						UpperCAmelCase     :		Optional[int]							     =		[negative_prompt]
															elif batch_size != len(A    ):
																						raise ValueError(
																						    f'''`negative_prompt`: {negative_prompt} has batch size {len(A    )}, but `prompt`:'''
																						    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																						    """ the batch size of `prompt`."""    )
															else:
																						UpperCAmelCase     :		Any							     =		negative_prompt

															UpperCAmelCase     :		Dict							     =		text_input_ids.shape[-1]
															UpperCAmelCase     :		List[Any]							     =		self.tokenizer(
															    A		,		padding="""max_length"""		,		max_length=A		,		truncation=A		,		return_tensors="""pt"""		,		)
															UpperCAmelCase     :		Tuple							     =		self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															UpperCAmelCase     :		int							     =		uncond_embeddings.shape[1]
															UpperCAmelCase     :		List[Any]							     =		uncond_embeddings.repeat(A		,		A		,		1    )
															UpperCAmelCase     :		List[str]							     =		uncond_embeddings.view(batch_size * num_images_per_prompt		,		A		,		-1    )

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															UpperCAmelCase     :		List[str]							     =		torch.cat([uncond_embeddings, text_embeddings]    )

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								UpperCAmelCase     :		Tuple							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								UpperCAmelCase     :		Optional[int]							     =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
								UpperCAmelCase     :		str							     =		text_embeddings.dtype
								if latents is None:
															if self.device.type == "mps":
																						# randn does not exist on mps
																						UpperCAmelCase     :		Dict							     =		torch.randn(
																						    A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(self.device    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device="""cpu"""		,		dtype=A    ).to(
																						    self.device    )
															else:
																						UpperCAmelCase     :		int							     =		torch.randn(
																						    A		,		generator=A		,		device=self.device		,		dtype=A    )
																						UpperCAmelCase     :		int							     =		torch.randn(A		,		generator=A		,		device=self.device		,		dtype=A    )
								else:
															if latents_reference.shape != latents_shape:
																						raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''    )
															UpperCAmelCase     :		Optional[Any]							     =		latents_reference.to(self.device    )
															UpperCAmelCase     :		Tuple							     =		latents.to(self.device    )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								UpperCAmelCase     :		int							     =		(latents_shape[3] - latents_shape_reference[3]) // 2
								UpperCAmelCase     :		List[str]							     =		(latents_shape[2] - latents_shape_reference[2]) // 2
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								UpperCAmelCase     :		Union[str, Any]							     =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								UpperCAmelCase     :		Optional[int]							     =		0 if dx < 0 else dx
								UpperCAmelCase     :		List[str]							     =		0 if dy < 0 else dy
								UpperCAmelCase     :		Union[str, Any]							     =		max(-dx		,		0    )
								UpperCAmelCase     :		List[Any]							     =		max(-dy		,		0    )
								# import pdb
								# pdb.set_trace()
								UpperCAmelCase     :		str							     =		latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(A    )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								UpperCAmelCase     :		Union[str, Any]							     =		self.scheduler.timesteps.to(self.device    )

								# scale the initial noise by the standard deviation required by the scheduler
								UpperCAmelCase     :		Optional[int]							     =		latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								UpperCAmelCase     :		int							     =		"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
								UpperCAmelCase     :		Optional[Any]							     =		{}
								if accepts_eta:
															UpperCAmelCase     :		List[str]							     =		eta

								for i, t in enumerate(self.progress_bar(A    )    ):
															# expand the latents if we are doing classifier free guidance
															UpperCAmelCase     :		Optional[Any]							     =		torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
															UpperCAmelCase     :		str							     =		self.scheduler.scale_model_input(A		,		A    )

															# predict the noise residual
															UpperCAmelCase     :		Any							     =		self.unet(A		,		A		,		encoder_hidden_states=A    ).sample

															# perform guidance
															if do_classifier_free_guidance:
																						UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		noise_pred.chunk(2    )
																						UpperCAmelCase     :		List[Any]							     =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															UpperCAmelCase     :		Dict							     =		self.scheduler.step(A		,		A		,		A		,		**A    ).prev_sample

															# call the callback, if provided
															if callback is not None and i % callback_steps == 0:
																						callback(A		,		A		,		A    )

								UpperCAmelCase     :		Union[str, Any]							     =		1 / 0.1_8_2_1_5 * latents
								UpperCAmelCase     :		Tuple							     =		self.vae.decode(A    ).sample

								UpperCAmelCase     :		Union[str, Any]							     =		(image / 2 + 0.5).clamp(0		,		1    )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								UpperCAmelCase     :		Union[str, Any]							     =		image.cpu().permute(0		,		2		,		3		,		1    ).float().numpy()

								if self.safety_checker is not None:
															UpperCAmelCase     :		int							     =		self.feature_extractor(self.numpy_to_pil(A    )		,		return_tensors="""pt"""    ).to(
															    self.device    )
															UpperCAmelCase     ,     UpperCAmelCase     :		int							     =		self.safety_checker(
															    images=A		,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
								else:
															UpperCAmelCase     :		Any							     =		None

								if output_type == "pil":
															UpperCAmelCase     :		int							     =		self.numpy_to_pil(A    )

								if not return_dict:
															return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=A		,		nsfw_content_detected=A    )

