





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     BigBirdConfig
					lowerCamelCase     :     jnp.dtype			    =       jnp.floataa
					lowerCamelCase     :     bool			    =       True





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						super().setup()
						UpperCAmelCase_								=     nn.Dense(5 ,					dtype=self.dtype		)





					def __call__(						self    :  int ,					*__lowercase    :  Optional[Any] ,					**__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     super().__call__(*__lowercase ,					**__lowercase		)
						UpperCAmelCase_								=     self.cls(outputs[2]		)
						return outputs[:2] + (cls_out,)







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Any			    =       FlaxBigBirdForNaturalQuestionsModule




def    A_(			A				,      A				,      A				,      A				,      A				,      A   ):
	def cross_entropy(A				,      A				,      A=None   ):
		UpperCAmelCase_								=     logits.shape[-1]
		UpperCAmelCase_								=     (labels[..., None] == jnp.arange(A   )[None]).astype("""f4"""   )
		UpperCAmelCase_								=     jax.nn.log_softmax(A				,      axis=-1   )
		UpperCAmelCase_								=     -jnp.sum(labels * logits				,      axis=-1   )
		if reduction is not None:
			UpperCAmelCase_								=     reduction(A   )
		return loss

	UpperCAmelCase_								=     partial(A				,      reduction=jnp.mean   )
	UpperCAmelCase_								=     cross_entropy(A				,      A   )
	UpperCAmelCase_								=     cross_entropy(A				,      A   )
	UpperCAmelCase_								=     cross_entropy(A				,      A   )
	return (start_loss + end_loss + pooled_loss) / 3







@dataclass
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     str			    =       "google/bigbird-roberta-base"
					lowerCamelCase     :     int			    =       3_000
					lowerCamelCase     :     int			    =       10_500

					lowerCamelCase     :     int			    =       128
					lowerCamelCase     :     int			    =       3

					lowerCamelCase     :     int			    =       1
					lowerCamelCase     :     int			    =       5

					# tx_args
					lowerCamelCase     :     float			    =       3E-5
					lowerCamelCase     :     float			    =       0.0
					lowerCamelCase     :     int			    =       20_000
					lowerCamelCase     :     float			    =       0.0095

					lowerCamelCase     :     str			    =       "bigbird-roberta-natural-questions"
					lowerCamelCase     :     str			    =       "training-expt"
					lowerCamelCase     :     str			    =       "data/nq-training.jsonl"
					lowerCamelCase     :     str			    =       "data/nq-validation.jsonl"





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						os.makedirs(self.base_dir ,					exist_ok=__lowercase		)
						UpperCAmelCase_								=     os.path.join(self.base_dir ,					self.save_dir		)
						UpperCAmelCase_								=     self.batch_size_per_device * jax.device_count()







@dataclass
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     int
					lowerCamelCase     :     int			    =       4_096  # no dynamic padding on TPUs





					def __call__(						self    :  List[str] ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.collate_fn(__lowercase		)
						UpperCAmelCase_								=     jax.tree_util.tree_map(__lowercase ,					__lowercase		)
						return batch





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self.fetch_inputs(features["""input_ids"""]		)
						UpperCAmelCase_								=     {
						    """input_ids""": jnp.array(__lowercase ,					dtype=jnp.intaa		),
						    """attention_mask""": jnp.array(__lowercase ,					dtype=jnp.intaa		),
						    """start_labels""": jnp.array(features["""start_token"""] ,					dtype=jnp.intaa		),
						    """end_labels""": jnp.array(features["""end_token"""] ,					dtype=jnp.intaa		),
						    """pooled_labels""": jnp.array(features["""category"""] ,					dtype=jnp.intaa		),
						}
						return batch





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  list		):






						'''simple docstring'''







						UpperCAmelCase_								=     [self._fetch_inputs(__lowercase		) for ids in input_ids]
						return zip(*__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  list		):






						'''simple docstring'''







						UpperCAmelCase_								=     [1 for _ in range(len(__lowercase		)		)]
						while len(__lowercase		) < self.max_length:
							input_ids.append(self.pad_id		)
							attention_mask.append(0		)
						return input_ids, attention_mask




def    A_(			A				,      A				,      A=None   ):
	if seed is not None:
		UpperCAmelCase_								=     dataset.shuffle(seed=A   )
	for i in range(len(A   ) // batch_size   ):
		UpperCAmelCase_								=     dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(A   )




@partial(jax.pmap				,      axis_name="""batch"""   )
def    A_(			A				,      A				,      **A   ):
	def loss_fn(A   ):
		UpperCAmelCase_								=     model_inputs.pop("""start_labels"""   )
		UpperCAmelCase_								=     model_inputs.pop("""end_labels"""   )
		UpperCAmelCase_								=     model_inputs.pop("""pooled_labels"""   )

		UpperCAmelCase_								=     state.apply_fn(**A				,      params=A				,      dropout_rng=A				,      train=A   )
		UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     outputs

		return state.loss_fn(
		    A				,      A				,      A				,      A				,      A				,      A				,      )

	UpperCAmelCase_						,	UpperCAmelCase_								=     jax.random.split(A   )
	UpperCAmelCase_								=     jax.value_and_grad(A   )
	UpperCAmelCase_						,	UpperCAmelCase_								=     grad_fn(state.params   )
	UpperCAmelCase_								=     jax.lax.pmean({"""loss""": loss}				,      axis_name="""batch"""   )
	UpperCAmelCase_								=     jax.lax.pmean(A				,      """batch"""   )

	UpperCAmelCase_								=     state.apply_gradients(grads=A   )
	return state, metrics, new_drp_rng




@partial(jax.pmap				,      axis_name="""batch"""   )
def    A_(			A				,      **A   ):
	UpperCAmelCase_								=     model_inputs.pop("""start_labels"""   )
	UpperCAmelCase_								=     model_inputs.pop("""end_labels"""   )
	UpperCAmelCase_								=     model_inputs.pop("""pooled_labels"""   )

	UpperCAmelCase_								=     state.apply_fn(**A				,      params=state.params				,      train=A   )
	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     outputs

	UpperCAmelCase_								=     state.loss_fn(A				,      A				,      A				,      A				,      A				,      A   )
	UpperCAmelCase_								=     jax.lax.pmean({"""loss""": loss}				,      axis_name="""batch"""   )
	return metrics







class   _UpperCamelCase  (       train_state.TrainState  ):







					'''simple docstring'''

					lowerCamelCase     :     Callable			    =       struct.field(pytree_node=A_  )







@dataclass
class   _UpperCamelCase  :







					'''simple docstring'''

					lowerCamelCase     :     Args
					lowerCamelCase     :     Callable
					lowerCamelCase     :     Callable
					lowerCamelCase     :     Callable
					lowerCamelCase     :     Callable
					lowerCamelCase     :     wandb
					lowerCamelCase     :     Callable			    =       None





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict ,					__lowercase    :  str ,					__lowercase    :  Dict ,					__lowercase    :  Any ,					__lowercase    :  Optional[Any]=None		):






						'''simple docstring'''







						UpperCAmelCase_								=     model.params
						UpperCAmelCase_								=     TrainState.create(
						    apply_fn=model.__call__ ,					params=__lowercase ,					tx=__lowercase ,					loss_fn=__lowercase ,					)
						if ckpt_dir is not None:
							UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     restore_checkpoint(__lowercase ,					__lowercase		)
							UpperCAmelCase_								=     {
							    """lr""": args.lr,
							    """init_lr""": args.init_lr,
							    """warmup_steps""": args.warmup_steps,
							    """num_train_steps""": num_train_steps,
							    """weight_decay""": args.weight_decay,
							}
							UpperCAmelCase_						,	UpperCAmelCase_								=     build_tx(**__lowercase		)
							UpperCAmelCase_								=     train_state.TrainState(
							    step=__lowercase ,					apply_fn=model.__call__ ,					params=__lowercase ,					tx=__lowercase ,					opt_state=__lowercase ,					)
							UpperCAmelCase_								=     args
							UpperCAmelCase_								=     data_collator
							UpperCAmelCase_								=     lr
							UpperCAmelCase_								=     params
						UpperCAmelCase_								=     jax_utils.replicate(__lowercase		)
						return state





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  Any ,					__lowercase    :  Dict ,					__lowercase    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.args
						UpperCAmelCase_								=     len(__lowercase		) // args.batch_size

						UpperCAmelCase_								=     jax.random.PRNGKey(0		)
						UpperCAmelCase_								=     jax.random.split(__lowercase ,					jax.device_count()		)
						for epoch in range(args.max_epochs		):
							UpperCAmelCase_								=     jnp.array(0 ,					dtype=jnp.floataa		)
							UpperCAmelCase_								=     get_batched_dataset(__lowercase ,					args.batch_size ,					seed=__lowercase		)
							UpperCAmelCase_								=     0
							for batch in tqdm(__lowercase ,					total=__lowercase ,					desc=F"""Running EPOCH-{epoch}"""		):
								UpperCAmelCase_								=     self.data_collator(__lowercase		)
								UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     self.train_step_fn(__lowercase ,					__lowercase ,					**__lowercase		)
								running_loss += jax_utils.unreplicate(metrics["""loss"""]		)
								i += 1
								if i % args.logging_steps == 0:
									UpperCAmelCase_								=     jax_utils.unreplicate(state.step		)
									UpperCAmelCase_								=     running_loss.item() / i
									UpperCAmelCase_								=     self.scheduler_fn(state_step - 1		)

									UpperCAmelCase_								=     self.evaluate(__lowercase ,					__lowercase		)
									UpperCAmelCase_								=     {
									    """step""": state_step.item(),
									    """eval_loss""": eval_loss.item(),
									    """tr_loss""": tr_loss,
									    """lr""": lr.item(),
									}
									tqdm.write(str(__lowercase		)		)
									self.logger.log(__lowercase ,					commit=__lowercase		)

								if i % args.save_steps == 0:
									self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}""" ,					state=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  List[str] ,					__lowercase    :  Tuple		):






						'''simple docstring'''







						UpperCAmelCase_								=     get_batched_dataset(__lowercase ,					self.args.batch_size		)
						UpperCAmelCase_								=     len(__lowercase		) // self.args.batch_size
						UpperCAmelCase_								=     jnp.array(0 ,					dtype=jnp.floataa		)
						UpperCAmelCase_								=     0
						for batch in tqdm(__lowercase ,					total=__lowercase ,					desc="""Evaluating ... """		):
							UpperCAmelCase_								=     self.data_collator(__lowercase		)
							UpperCAmelCase_								=     self.val_step_fn(__lowercase ,					**__lowercase		)
							running_loss += jax_utils.unreplicate(metrics["""loss"""]		)
							i += 1
						return running_loss / i





					def        SCREAMING_SNAKE_CASE							(						self    :  Union[str, Any] ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     jax_utils.unreplicate(__lowercase		)
						print(F"""SAVING CHECKPOINT IN {save_dir}""" ,					end=""" ... """		)
						self.model_save_fn(__lowercase ,					params=state.params		)
						with open(os.path.join(__lowercase ,					"""opt_state.msgpack"""		) ,					"""wb"""		) as f:
							f.write(to_bytes(state.opt_state		)		)
						joblib.dump(self.args ,					os.path.join(__lowercase ,					"""args.joblib"""		)		)
						joblib.dump(self.data_collator ,					os.path.join(__lowercase ,					"""data_collator.joblib"""		)		)
						with open(os.path.join(__lowercase ,					"""training_state.json"""		) ,					"""w"""		) as f:
							json.dump({"""step""": state.step.item()} ,					__lowercase		)
						print("""DONE"""		)




def    A_(			A				,      A   ):
	print(f"""RESTORING CHECKPOINT FROM {save_dir}"""				,      end=""" ... """   )
	with open(os.path.join(A				,      """flax_model.msgpack"""   )				,      """rb"""   ) as f:
		UpperCAmelCase_								=     from_bytes(state.params				,      f.read()   )

	with open(os.path.join(A				,      """opt_state.msgpack"""   )				,      """rb"""   ) as f:
		UpperCAmelCase_								=     from_bytes(state.opt_state				,      f.read()   )

	UpperCAmelCase_								=     joblib.load(os.path.join(A				,      """args.joblib"""   )   )
	UpperCAmelCase_								=     joblib.load(os.path.join(A				,      """data_collator.joblib"""   )   )

	with open(os.path.join(A				,      """training_state.json"""   )				,      """r"""   ) as f:
		UpperCAmelCase_								=     json.load(A   )
	UpperCAmelCase_								=     training_state["""step"""]

	print("""DONE"""   )
	return params, opt_state, step, args, data_collator




def    A_(			A				,      A				,      A				,      A   ):
	UpperCAmelCase_								=     num_train_steps - warmup_steps
	UpperCAmelCase_								=     optax.linear_schedule(init_value=A				,      end_value=A				,      transition_steps=A   )
	UpperCAmelCase_								=     optax.linear_schedule(init_value=A				,      end_value=1E-7				,      transition_steps=A   )
	UpperCAmelCase_								=     optax.join_schedules(schedules=[warmup_fn, decay_fn]				,      boundaries=[warmup_steps]   )
	return lr




def    A_(			A				,      A				,      A				,      A				,      A   ):
	def weight_decay_mask(A   ):
		UpperCAmelCase_								=     traverse_util.flatten_dict(A   )
		UpperCAmelCase_								=     {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
		return traverse_util.unflatten_dict(A   )

	UpperCAmelCase_								=     scheduler_fn(A				,      A				,      A				,      A   )

	UpperCAmelCase_								=     optax.adamw(learning_rate=A				,      weight_decay=A				,      mask=A   )
	return tx, lr









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


UpperCamelCase__   : Tuple					  = logging.get_logger(__name__)




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     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()   )


UpperCamelCase__   : str					  = {
    # 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))},
}
UpperCamelCase__   : 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    A_(			A				,      A   ):
	try:
		UpperCAmelCase_								=     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    A_(			A				,      A   ):
	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    A_(			A				,      A = "student"				,      A = None				,      A = None				,      A=False				,      A=None				,      A=None				,      **A				,      ):
	UpperCAmelCase_								=     """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
		UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_pretrained(A   ).eval()
	else:
		assert isinstance(A				,      A   ), f"""teacher must be a model or string got type {type(A   )}"""
	UpperCAmelCase_								=     teacher.config.to_diff_dict()

	try:
		UpperCAmelCase_						,	UpperCAmelCase_								=     teacher.config.encoder_layers, teacher.config.decoder_layers
		if e is None:
			UpperCAmelCase_								=     teacher_e
		if d is None:
			UpperCAmelCase_								=     teacher_d
		init_kwargs.update({"""encoder_layers""": e, """decoder_layers""": d}   )
	except AttributeError:  # T5
		if hasattr(teacher.config				,      """num_encoder_layers"""   ):
			UpperCAmelCase_						,	UpperCAmelCase_								=     teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
		else:
			UpperCAmelCase_						,	UpperCAmelCase_								=     teacher.config.num_layers, teacher.config.num_decoder_layers
		if e is None:
			UpperCAmelCase_								=     teacher_e
		if d is None:
			UpperCAmelCase_								=     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
	UpperCAmelCase_								=     teacher.config_class(**A   )
	UpperCAmelCase_								=     AutoModelForSeqaSeqLM.from_config(A   )
	# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
	UpperCAmelCase_								=     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
		UpperCAmelCase_						,	UpperCAmelCase_								=     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:
		UpperCAmelCase_								=     pick_layers_to_copy(A				,      A   )
	if d_layers_to_copy is None:
		UpperCAmelCase_								=     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}"""   )
	UpperCAmelCase_								=     {
	    """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)


