
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)


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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     42
	snake_case_					    =     42
	snake_case_					    =     None





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     2






	@register_to_config
	def __init__(       self					: str							,A					: float = 0.02							,A					: float = 1_00							,A					: float = 1.0_07							,A					: float = 80							,A					: float = 0.05							,A					: float = 50							,):
					# standard deviation of the initial noise distribution
					__A   			= sigma_max

					# setable values
					__A   			= None
					__A   			= None
					__A   			= None  # sigma(t_i)






	def 				UpperCamelCase_  (       self					: str							,A					: torch.FloatTensor							,A					: Optional[int] = None ):
					return sample






	def 				UpperCamelCase_  (       self					: Dict							,A					: int							,A					: Union[str, torch.device] = None ):
					__A   			= num_inference_steps
					__A   			= np.arange(0							,self.num_inference_steps )[::-1].copy()
					__A   			= torch.from_numpy(A ).to(A )
					__A   			= [
					    (
					        self.config.sigma_max**2
					        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
					    )
					    for i in self.timesteps
					]
					__A   			= torch.tensor(A							,dtype=torch.floataa							,device=A )






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: torch.FloatTensor							,A					: float							,A					: Optional[torch.Generator] = None ):
					if self.config.s_min <= sigma <= self.config.s_max:
									__A   			= min(self.config.s_churn / self.num_inference_steps							,2**0.5 - 1 )
					else:
									__A   			= 0

					# sample eps ~ N(0, S_noise^2 * I)
					__A   			= self.config.s_noise * randn_tensor(sample.shape							,generator=A ).to(sample.device )
					__A   			= sigma + gamma * sigma
					__A   			= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

					return sample_hat, sigma_hat






	def 				UpperCamelCase_  (       self					: Dict							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_hat + sigma_hat * model_output
					__A   			= (sample_hat - pred_original_sample) / sigma_hat
					__A   			= sample_hat + (sigma_prev - sigma_hat) * derivative

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: torch.FloatTensor							,A					: float							,A					: float							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: torch.FloatTensor							,A					: bool = True							,):
					__A   			= sample_prev + sigma_prev * model_output
					__A   			= (sample_prev - pred_original_sample) / sigma_prev
					__A   			= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

					if not return_dict:
									return (sample_prev, derivative)

					return KarrasVeOutput(
					    prev_sample=A							,derivative=A							,pred_original_sample=A )







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: str ):
					raise NotImplementedError()
