


'''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  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   BigBirdConfig
				__magic_name__       :   jnp.dtype							= jnp.floataa
				__magic_name__       :   bool							= True




				def   a__(	self    :				Union[str, Any]   )->  str:

										"""simple docstring"""




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





				def __call__(	self    :				List[Any]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				int   )->  Union[str, Any]:

										"""simple docstring"""




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







class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Dict							= FlaxBigBirdForNaturalQuestionsModule







def    lowerCamelCase__   ( A     :    str   ,     A     :    Any   ,     A     :    int   ,     A     :    str   ,     A     :    List[str]   ,     A     :    List[Any]  ):




						'''simple docstring'''






						def cross_entropy(A     :    Optional[int]   ,     A     :    Tuple   ,     A     :    int=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__:
				__magic_name__       :   str							= "google/bigbird-roberta-base"
				__magic_name__       :   int							= 3000
				__magic_name__       :   int							= 1_0500

				__magic_name__       :   int							= 128
				__magic_name__       :   int							= 3

				__magic_name__       :   int							= 1
				__magic_name__       :   int							= 5

				# tx_args
				__magic_name__       :   float							= 3e-5
				__magic_name__       :   float							= 0.0
				__magic_name__       :   int							= 2_0000
				__magic_name__       :   float							= 0.0_0_9_5

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




				def   a__(	self    :				Optional[Any]   )->  List[Any]:

										"""simple docstring"""




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







@dataclass
class  UpperCamelCase__:
				__magic_name__       :   int
				__magic_name__       :   int							= 4096  # no dynamic padding on TPUs




				def __call__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.collate_fn(lowerCAmelCase   )
										UpperCAmelCase         =			jax.tree_util.tree_map(lowerCAmelCase   ,					lowerCAmelCase   )
										return batch




				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Any   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self.fetch_inputs(features['''input_ids''']   )
										UpperCAmelCase         =			{
										    '''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   a__(	self    :				Tuple   ,					lowerCAmelCase    :				list   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			[self._fetch_inputs(lowerCAmelCase   ) for ids in input_ids]
										return zip(*lowerCAmelCase   )





				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				list   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[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__   ( A     :    str   ,     A     :    Optional[Any]   ,     A     :    List[str]=None  ):




						'''simple docstring'''






						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    lowerCamelCase__   ( A     :    List[str]   ,     A     :    Optional[int]   ,     **A     :    int  ):




						'''simple docstring'''






						def loss_fn(A     :    Dict  ):
												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    lowerCamelCase__   ( A     :    List[str]   ,     **A     :    List[str]  ):




						'''simple docstring'''






						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 ):
				__magic_name__       :   Callable							= struct.field(pytree_node=lowerCAmelCase )







@dataclass
class  UpperCamelCase__:
				__magic_name__       :   Args
				__magic_name__       :   Callable
				__magic_name__       :   Callable
				__magic_name__       :   Callable
				__magic_name__       :   Callable
				__magic_name__       :   wandb
				__magic_name__       :   Callable							= None




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[Any]=None   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			model.params
										UpperCAmelCase         =			TrainState.create(
										    apply_fn=model.__call__   ,					params=lowerCAmelCase   ,					tx=lowerCAmelCase   ,					loss_fn=lowerCAmelCase   ,					)
										if ckpt_dir is not None:
																UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			restore_checkpoint(lowerCAmelCase   ,					lowerCAmelCase   )
																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(**lowerCAmelCase   )
																UpperCAmelCase         =			train_state.TrainState(
																    step=lowerCAmelCase   ,					apply_fn=model.__call__   ,					params=lowerCAmelCase   ,					tx=lowerCAmelCase   ,					opt_state=lowerCAmelCase   ,					)
																UpperCAmelCase         =			args
																UpperCAmelCase         =			data_collator
																UpperCAmelCase         =			lr
																UpperCAmelCase         =			params
										UpperCAmelCase         =			jax_utils.replicate(lowerCAmelCase   )
										return state




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			self.args
										UpperCAmelCase         =			len(lowerCAmelCase   ) // args.batch_size

										UpperCAmelCase         =			jax.random.PRNGKey(0   )
										UpperCAmelCase         =			jax.random.split(lowerCAmelCase   ,					jax.device_count()   )
										for epoch in range(args.max_epochs   ):
																UpperCAmelCase         =			jnp.array(0   ,					dtype=jnp.floataa   )
																UpperCAmelCase         =			get_batched_dataset(lowerCAmelCase   ,					args.batch_size   ,					seed=lowerCAmelCase   )
																UpperCAmelCase         =			0
																for batch in tqdm(lowerCAmelCase   ,					total=lowerCAmelCase   ,					desc=F"""Running EPOCH-{epoch}"""   ):
																						UpperCAmelCase         =			self.data_collator(lowerCAmelCase   )
																						UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			self.train_step_fn(lowerCAmelCase   ,					lowerCAmelCase   ,					**lowerCAmelCase   )
																						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(lowerCAmelCase   ,					lowerCAmelCase   )
																												UpperCAmelCase         =			{
																												    '''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   a__(	self    :				Tuple   ,					lowerCAmelCase    :				List[str]   ,					lowerCAmelCase    :				Tuple   )->  Any:

										"""simple docstring"""




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





				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				str   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			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__   ( A     :    Tuple   ,     A     :    Dict  ):




						'''simple docstring'''






						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    lowerCamelCase__   ( A     :    Any   ,     A     :    Any   ,     A     :    Union[str, Any]   ,     A     :    Tuple  ):




						'''simple docstring'''






						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    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    Optional[Any]   ,     A     :    str   ,     A     :    Optional[Any]   ,     A     :    Any  ):




						'''simple docstring'''






						def weight_decay_mask(A     :    List[str]  ):
												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







'''simple docstring'''



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    :    int			     =  {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    str			     =  [
	    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XmodForCausalLM""",
	    """XmodForMaskedLM""",
	    """XmodForMultipleChoice""",
	    """XmodForQuestionAnswering""",
	    """XmodForSequenceClassification""",
	    """XmodForTokenClassification""",
	    """XmodModel""",
	    """XmodPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xmod import (
		    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XmodForCausalLM,
		    XmodForMaskedLM,
		    XmodForMultipleChoice,
		    XmodForQuestionAnswering,
		    XmodForSequenceClassification,
		    XmodForTokenClassification,
		    XmodModel,
		    XmodPreTrainedModel,
		)

else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





