





'''simple docstring'''



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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

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


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





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


	def __call__(self       ,*_lowerCamelCase       ,**_lowerCamelCase	)							->							Optional[Any]:







				'''simple docstring'''





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






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			List[Any]     =    FlaxBigBirdForNaturalQuestionsModule





def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					Dict						,    lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Union[str, Any]			):
			def cross_entropy(lowerCamelCase_     :					List[str]						,    lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					Tuple=None			):
						__lowercase			  =							logits.shape[-1]
						__lowercase			  =							(labels[..., None] == jnp.arange(lowerCamelCase_			)[None]).astype('''f4'''			)
						__lowercase			  =							jax.nn.log_softmax(lowerCamelCase_						,    axis=-1			)
						__lowercase			  =							-jnp.sum(labels * logits						,    axis=-1			)
						if reduction is not None:
									__lowercase			  =							reduction(lowerCamelCase_			)
						return loss

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






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			str     =    "google/bigbird-roberta-base"
	a      :			int     =    3000
	a      :			int     =    1_0500

	a      :			int     =    128
	a      :			int     =    3

	a      :			int     =    1
	a      :			int     =    5

	# tx_args
	a      :			float     =    3e-5
	a      :			float     =    0.0
	a      :			int     =    2_0000
	a      :			float     =    0.00_95

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


	def   _UpperCAmelCase       (self	)							->							int:







				'''simple docstring'''





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






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			int
	a      :			int     =    4096  # no dynamic padding on TPUs


	def __call__(self       ,_lowerCamelCase	)							->							str:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Dict:







				'''simple docstring'''





				__lowercase  ,						__lowercase			  =							self.fetch_inputs(features['''input_ids''']	)
				__lowercase			  =							{
				    '''input_ids''': jnp.array(_lowerCamelCase       ,dtype=jnp.intaa	),
				    '''attention_mask''': jnp.array(_lowerCamelCase       ,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   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Any:







				'''simple docstring'''





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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					str						,    lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					int=None			):
			if seed is not None:
						__lowercase			  =							dataset.shuffle(seed=lowerCamelCase_			)
			for i in range(len(lowerCamelCase_			) // batch_size			):
						__lowercase			  =							dataset[i * batch_size : (i + 1) * batch_size]
						yield dict(lowerCamelCase_			)




@partial(jax.pmap						,    axis_name='''batch'''			)
def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					str						,    **lowerCamelCase_     :					Any			):
			def loss_fn(lowerCamelCase_     :					List[str]			):
						__lowercase			  =							model_inputs.pop('''start_labels'''			)
						__lowercase			  =							model_inputs.pop('''end_labels'''			)
						__lowercase			  =							model_inputs.pop('''pooled_labels'''			)

						__lowercase			  =							state.apply_fn(**lowerCamelCase_						,    params=lowerCamelCase_						,    dropout_rng=lowerCamelCase_						,    train=lowerCamelCase_			)
						__lowercase  ,						__lowercase  ,						__lowercase			  =							outputs

						return state.loss_fn(
						    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    )

			__lowercase  ,						__lowercase			  =							jax.random.split(lowerCamelCase_			)
			__lowercase			  =							jax.value_and_grad(lowerCamelCase_			)
			__lowercase  ,						__lowercase			  =							grad_fn(state.params			)
			__lowercase			  =							jax.lax.pmean({'''loss''': loss}						,    axis_name='''batch'''			)
			__lowercase			  =							jax.lax.pmean(lowerCamelCase_						,    '''batch'''			)

			__lowercase			  =							state.apply_gradients(grads=lowerCamelCase_			)
			return state, metrics, new_drp_rng




@partial(jax.pmap						,    axis_name='''batch'''			)
def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]						,    **lowerCamelCase_     :					List[Any]			):
			__lowercase			  =							model_inputs.pop('''start_labels'''			)
			__lowercase			  =							model_inputs.pop('''end_labels'''			)
			__lowercase			  =							model_inputs.pop('''pooled_labels'''			)

			__lowercase			  =							state.apply_fn(**lowerCamelCase_						,    params=state.params						,    train=lowerCamelCase_			)
			__lowercase  ,						__lowercase  ,						__lowercase			  =							outputs

			__lowercase			  =							state.loss_fn(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
			__lowercase			  =							jax.lax.pmean({'''loss''': loss}						,    axis_name='''batch'''			)
			return metrics






class 							__lowercase    ( train_state.TrainState  ):







	'''simple docstring'''

	a      :			Callable     =    struct.field(pytree_node=lowerCAmelCase__  )






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			Args
	a      :			Callable
	a      :			Callable
	a      :			Callable
	a      :			Callable
	a      :			wandb
	a      :			Callable     =    None


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							str:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[str]:







				'''simple docstring'''





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

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

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

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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							List[Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase	)							->							int:







				'''simple docstring'''





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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Any			):
			print(f"RESTORING CHECKPOINT FROM {save_dir}"						,    end=''' ... '''			)
			with open(os.path.join(lowerCamelCase_						,    '''flax_model.msgpack'''			)						,    '''rb'''			) as f:
						__lowercase			  =							from_bytes(state.params						,    f.read()			)

			with open(os.path.join(lowerCamelCase_						,    '''opt_state.msgpack'''			)						,    '''rb'''			) as f:
						__lowercase			  =							from_bytes(state.opt_state						,    f.read()			)

			__lowercase			  =							joblib.load(os.path.join(lowerCamelCase_						,    '''args.joblib'''			)			)
			__lowercase			  =							joblib.load(os.path.join(lowerCamelCase_						,    '''data_collator.joblib'''			)			)

			with open(os.path.join(lowerCamelCase_						,    '''training_state.json'''			)						,    '''r'''			) as f:
						__lowercase			  =							json.load(lowerCamelCase_			)
			__lowercase			  =							training_state['''step''']

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Optional[Any]			):
			__lowercase			  =							num_train_steps - warmup_steps
			__lowercase			  =							optax.linear_schedule(init_value=lowerCamelCase_						,    end_value=lowerCamelCase_						,    transition_steps=lowerCamelCase_			)
			__lowercase			  =							optax.linear_schedule(init_value=lowerCamelCase_						,    end_value=1E-7						,    transition_steps=lowerCamelCase_			)
			__lowercase			  =							optax.join_schedules(schedules=[warmup_fn, decay_fn]						,    boundaries=[warmup_steps]			)
			return lr




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					Any						,    lowerCamelCase_     :					List[Any]						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					int			):
			def weight_decay_mask(lowerCamelCase_     :					Optional[int]			):
						__lowercase			  =							traverse_util.flatten_dict(lowerCamelCase_			)
						__lowercase			  =							{k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
						return traverse_util.unflatten_dict(lowerCamelCase_			)

			__lowercase			  =							scheduler_fn(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)

			__lowercase			  =							optax.adamw(learning_rate=lowerCamelCase_						,    weight_decay=lowerCamelCase_						,    mask=lowerCamelCase_			)
			return tx, lr












'''simple docstring'''



import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_SCREAMING_SNAKE_CASE   =      ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
_SCREAMING_SNAKE_CASE   =      {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
	raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      ''' Hello world! cécé herlolip'''

_SCREAMING_SNAKE_CASE   =      [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]




def 				_lowerCAmelCase  (			lowerCamelCase_     :					int			):
			__lowercase			  =							[
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			]
			for k in ignore_keys:
						state_dict.pop(lowerCamelCase_						,    lowerCamelCase_			)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Tuple						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					Union[str, Any]			):
			__lowercase			  =							dct.pop(lowerCamelCase_			)
			__lowercase			  =							val




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any			):
			__lowercase			  =							torch.load(lowerCamelCase_						,    map_location='''cpu'''			)
			__lowercase			  =							torch.hub.load('''pytorch/fairseq'''						,    '''bart.large.cnn'''			).eval()
			hub_interface.model.load_state_dict(sd['''model''']			)
			return hub_interface




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[str]			):
			__lowercase  ,						__lowercase			  =							emb.weight.shape
			__lowercase			  =							nn.Linear(lowerCamelCase_						,    lowerCamelCase_						,    bias=lowerCamelCase_			)
			__lowercase			  =							emb.weight.data
			return lin_layer




@torch.no_grad()
def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[str, Any]						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[Any]=None			):
			if not os.path.exists(lowerCamelCase_			):
						__lowercase			  =							torch.hub.load('''pytorch/fairseq'''						,    lowerCamelCase_			).eval()
			else:
						__lowercase			  =							load_xsum_checkpoint(lowerCamelCase_			)

			bart.model.upgrade_state_dict(bart.model.state_dict()			)
			if hf_checkpoint_name is None:
						__lowercase			  =							checkpoint_path.replace('''.'''						,    '''-'''			)
			__lowercase			  =							BartConfig.from_pretrained(lowerCamelCase_			)
			__lowercase			  =							bart.encode(lowerCamelCase_			).unsqueeze(0			)
			__lowercase			  =							BartTokenizer.from_pretrained(lowerCamelCase_			).encode(lowerCamelCase_						,    return_tensors='''pt'''			).unsqueeze(0			)
			if not torch.eq(lowerCamelCase_						,    lowerCamelCase_			).all():
						raise ValueError(
						    f"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"			)

			if checkpoint_path == "bart.large.mnli":
						__lowercase			  =							bart.state_dict()
						remove_ignore_keys_(lowerCamelCase_			)
						__lowercase			  =							state_dict['''model.decoder.embed_tokens.weight''']
						for src, dest in mnli_rename_keys:
									rename_key(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_			)
						__lowercase			  =							BartForSequenceClassification(lowerCamelCase_			).eval()
						model.load_state_dict(lowerCamelCase_			)
						__lowercase			  =							bart.predict('''mnli'''						,    lowerCamelCase_						,    return_logits=lowerCamelCase_			)
						__lowercase			  =							model(lowerCamelCase_			)[0]  # logits
			else:  # no classification heads to worry about
						__lowercase			  =							bart.model.state_dict()
						remove_ignore_keys_(lowerCamelCase_			)
						__lowercase			  =							state_dict['''decoder.embed_tokens.weight''']
						__lowercase			  =							bart.extract_features(lowerCamelCase_			)
						if hf_checkpoint_name == "facebook/bart-large":
									__lowercase			  =							BartModel(lowerCamelCase_			).eval()
									model.load_state_dict(lowerCamelCase_			)
									__lowercase			  =							model(lowerCamelCase_			).model[0]
						else:
									__lowercase			  =							BartForConditionalGeneration(lowerCamelCase_			).eval()  # an existing summarization ckpt
									model.model.load_state_dict(lowerCamelCase_			)
									if hasattr(lowerCamelCase_						,    '''lm_head'''			):
												__lowercase			  =							make_linear_from_emb(model.model.shared			)
									__lowercase			  =							model.model(lowerCamelCase_			)[0]

    # Check results
			if fairseq_output.shape != new_model_outputs.shape:
						raise ValueError(
						    f"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"			)
			if (fairseq_output != new_model_outputs).any().item():
						raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''			)
			Path(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
			model.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE   =      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
	)
	parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument(
	    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
	)
	_SCREAMING_SNAKE_CASE   =      parser.parse_args()
	convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





