

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 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  BigBirdConfig
							_UpperCAmelCase   :  jnp.dtype										=				jnp.floataa
							_UpperCAmelCase   :  bool										=				True
							def    lowerCAmelCase					(		self		:      Tuple):
								super().setup()
								__lowerCamelCase    :    Dict 	=			nn.Dense(5       ,dtype=self.dtype)


							def __call__(		self		:      Tuple       ,*SCREAMING_SNAKE_CASE__		:      Dict       ,**SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    str 	=			super().__call__(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			self.cls(outputs[2])
								return outputs[:2] + (cls_out,)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				FlaxBigBirdForNaturalQuestionsModule






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	def cross_entropy(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=None      ):
		__lowerCamelCase    :    int 	=			logits.shape[-1]
		__lowerCamelCase    :    List[Any] 	=			(labels[..., None] == jnp.arange(lowerCamelCase__      )[None]).astype('f4'      )
		__lowerCamelCase    :    List[str] 	=			jax.nn.log_softmax(lowerCamelCase__     ,			axis=-1      )
		__lowerCamelCase    :    Union[str, Any] 	=			-jnp.sum(labels * logits     ,			axis=-1      )
		if reduction is not None:
			__lowerCamelCase    :    Optional[int] 	=			reduction(lowerCamelCase__      )
		return loss

	__lowerCamelCase    :    Dict 	=			partial(lowerCamelCase__     ,			reduction=jnp.mean      )
	__lowerCamelCase    :    Tuple 	=			cross_entropy(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Any 	=			cross_entropy(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			cross_entropy(lowerCamelCase__     ,			lowerCamelCase__      )
	return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class 	A_		:
							_UpperCAmelCase   :  str										=				"google/bigbird-roberta-base"
							_UpperCAmelCase   :  int										=				3_000
							_UpperCAmelCase   :  int										=				10_500

							_UpperCAmelCase   :  int										=				128
							_UpperCAmelCase   :  int										=				3

							_UpperCAmelCase   :  int										=				1
							_UpperCAmelCase   :  int										=				5

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

							_UpperCAmelCase   :  str										=				"bigbird-roberta-natural-questions"
							_UpperCAmelCase   :  str										=				"training-expt"
							_UpperCAmelCase   :  str										=				"data/nq-training.jsonl"
							_UpperCAmelCase   :  str										=				"data/nq-validation.jsonl"
							def    lowerCAmelCase					(		self		:      List[str]):
								os.makedirs(self.base_dir       ,exist_ok=SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			os.path.join(self.base_dir       ,self.save_dir)
								__lowerCamelCase    :    Optional[Any] 	=			self.batch_size_per_device * jax.device_count()






@dataclass
class 	A_		:
							_UpperCAmelCase   :  int
							_UpperCAmelCase   :  int										=				4_096  # no dynamic padding on TPUs
							def __call__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								__lowerCamelCase    :    List[str] 	=			self.collate_fn(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			jax.tree_util.tree_map(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								return batch
							def    lowerCAmelCase					(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      Dict):
								__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			self.fetch_inputs(features['input_ids'])
								__lowerCamelCase    :    Tuple 	=			{
								    'input_ids': jnp.array(SCREAMING_SNAKE_CASE__       ,dtype=jnp.intaa),
								    'attention_mask': jnp.array(SCREAMING_SNAKE_CASE__       ,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    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      list):
								__lowerCamelCase    :    List[str] 	=			[self._fetch_inputs(SCREAMING_SNAKE_CASE__) for ids in input_ids]
								return zip(*SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      list):
								__lowerCamelCase    :    List[str] 	=			[1 for _ in range(len(SCREAMING_SNAKE_CASE__))]
								while len(SCREAMING_SNAKE_CASE__) < self.max_length:
									input_ids.append(self.pad_id)
									attention_mask.append(0)
								return input_ids, attention_mask






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__=None      )  ->					Dict:
	if seed is not None:
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.shuffle(seed=lowerCamelCase__      )
	for i in range(len(lowerCamelCase__      ) // batch_size      ):
		__lowerCamelCase    :    Union[str, Any] 	=			dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(lowerCamelCase__      )






@partial(jax.pmap     ,			axis_name='batch'      )
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			**lowerCamelCase__      )  ->					Any:
	def loss_fn(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			model_inputs.pop('start_labels'      )
		__lowerCamelCase    :    int 	=			model_inputs.pop('end_labels'      )
		__lowerCamelCase    :    Optional[Any] 	=			model_inputs.pop('pooled_labels'      )

		__lowerCamelCase    :    Optional[Any] 	=			state.apply_fn(**lowerCamelCase__     ,			params=lowerCamelCase__     ,			dropout_rng=lowerCamelCase__     ,			train=lowerCamelCase__      )
		__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			outputs

		return state.loss_fn(
		    lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)

	__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			jax.random.split(lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			jax.value_and_grad(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			grad_fn(state.params      )
	__lowerCamelCase    :    Optional[int] 	=			jax.lax.pmean({'loss': loss}     ,			axis_name='batch'      )
	__lowerCamelCase    :    Any 	=			jax.lax.pmean(lowerCamelCase__     ,			'batch'      )

	__lowerCamelCase    :    str 	=			state.apply_gradients(grads=lowerCamelCase__      )
	return state, metrics, new_drp_rng






@partial(jax.pmap     ,			axis_name='batch'      )
def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			**lowerCamelCase__      )  ->					Any:
	__lowerCamelCase    :    Dict 	=			model_inputs.pop('start_labels'      )
	__lowerCamelCase    :    Optional[int] 	=			model_inputs.pop('end_labels'      )
	__lowerCamelCase    :    List[Any] 	=			model_inputs.pop('pooled_labels'      )

	__lowerCamelCase    :    Union[str, Any] 	=			state.apply_fn(**lowerCamelCase__     ,			params=state.params     ,			train=lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			outputs

	__lowerCamelCase    :    int 	=			state.loss_fn(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			jax.lax.pmean({'loss': loss}     ,			axis_name='batch'      )
	return metrics






class 	A_		(	train_state.TrainState    ):
							_UpperCAmelCase   :  Callable										=				struct.field(pytree_node=SCREAMING_SNAKE_CASE    )






@dataclass
class 	A_		:
							_UpperCAmelCase   :  Args
							_UpperCAmelCase   :  Callable
							_UpperCAmelCase   :  Callable
							_UpperCAmelCase   :  Callable
							_UpperCAmelCase   :  Callable
							_UpperCAmelCase   :  wandb
							_UpperCAmelCase   :  Callable										=				None
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Any=None):
								__lowerCamelCase    :    Any 	=			model.params
								__lowerCamelCase    :    List[Any] 	=			TrainState.create(
								    apply_fn=model.__call__       ,params=SCREAMING_SNAKE_CASE__       ,tx=SCREAMING_SNAKE_CASE__       ,loss_fn=SCREAMING_SNAKE_CASE__       ,)
								if ckpt_dir is not None:
									__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			restore_checkpoint(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Union[str, Any] 	=			{
									    'lr': args.lr,
									    'init_lr': args.init_lr,
									    'warmup_steps': args.warmup_steps,
									    'num_train_steps': num_train_steps,
									    'weight_decay': args.weight_decay,
									}
									__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			build_tx(**SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    List[str] 	=			train_state.TrainState(
									    step=SCREAMING_SNAKE_CASE__       ,apply_fn=model.__call__       ,params=SCREAMING_SNAKE_CASE__       ,tx=SCREAMING_SNAKE_CASE__       ,opt_state=SCREAMING_SNAKE_CASE__       ,)
									__lowerCamelCase    :    Union[str, Any] 	=			args
									__lowerCamelCase    :    int 	=			data_collator
									__lowerCamelCase    :    Union[str, Any] 	=			lr
									__lowerCamelCase    :    Optional[Any] 	=			params
								__lowerCamelCase    :    str 	=			jax_utils.replicate(SCREAMING_SNAKE_CASE__)
								return state
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			self.args
								__lowerCamelCase    :    Union[str, Any] 	=			len(SCREAMING_SNAKE_CASE__) // args.batch_size

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

											__lowerCamelCase    :    Optional[int] 	=			self.evaluate(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
											__lowerCamelCase    :    Optional[int] 	=			{
											    'step': state_step.item(),
											    'eval_loss': eval_loss.item(),
											    'tr_loss': tr_loss,
											    'lr': lr.item(),
											}
											tqdm.write(str(SCREAMING_SNAKE_CASE__))
											self.logger.log(SCREAMING_SNAKE_CASE__       ,commit=SCREAMING_SNAKE_CASE__)

										if i % args.save_steps == 0:
											self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"       ,state=SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    Union[str, Any] 	=			get_batched_dataset(SCREAMING_SNAKE_CASE__       ,self.args.batch_size)
								__lowerCamelCase    :    List[str] 	=			len(SCREAMING_SNAKE_CASE__) // self.args.batch_size
								__lowerCamelCase    :    List[str] 	=			jnp.array(0       ,dtype=jnp.floataa)
								__lowerCamelCase    :    int 	=			0
								for batch in tqdm(SCREAMING_SNAKE_CASE__       ,total=SCREAMING_SNAKE_CASE__       ,desc='Evaluating ... '):
									__lowerCamelCase    :    List[Any] 	=			self.data_collator(SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Any 	=			self.val_step_fn(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
									running_loss += jax_utils.unreplicate(metrics['loss'])
									i += 1
								return running_loss / i


							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Tuple):
								__lowerCamelCase    :    int 	=			jax_utils.unreplicate(SCREAMING_SNAKE_CASE__)
								print(F"SAVING CHECKPOINT IN {save_dir}"       ,end=' ... ')
								self.model_save_fn(SCREAMING_SNAKE_CASE__       ,params=state.params)
								with open(os.path.join(SCREAMING_SNAKE_CASE__       ,'opt_state.msgpack')       ,'wb') as f:
									f.write(to_bytes(state.opt_state))
								joblib.dump(self.args       ,os.path.join(SCREAMING_SNAKE_CASE__       ,'args.joblib'))
								joblib.dump(self.data_collator       ,os.path.join(SCREAMING_SNAKE_CASE__       ,'data_collator.joblib'))
								with open(os.path.join(SCREAMING_SNAKE_CASE__       ,'training_state.json')       ,'w') as f:
									json.dump({'step': state.step.item()}       ,SCREAMING_SNAKE_CASE__)
								print('DONE')






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Dict:
	print(F"RESTORING CHECKPOINT FROM {save_dir}"     ,			end=' ... '      )
	with open(os.path.join(lowerCamelCase__     ,			'flax_model.msgpack'      )     ,			'rb'      ) as f:
		__lowerCamelCase    :    int 	=			from_bytes(state.params     ,			f.read()      )

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

	__lowerCamelCase    :    int 	=			joblib.load(os.path.join(lowerCamelCase__     ,			'args.joblib'      )      )
	__lowerCamelCase    :    List[str] 	=			joblib.load(os.path.join(lowerCamelCase__     ,			'data_collator.joblib'      )      )

	with open(os.path.join(lowerCamelCase__     ,			'training_state.json'      )     ,			'r'      ) as f:
		__lowerCamelCase    :    Union[str, Any] 	=			json.load(lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			training_state['step']

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[Any]:
	__lowerCamelCase    :    List[Any] 	=			num_train_steps - warmup_steps
	__lowerCamelCase    :    Optional[int] 	=			optax.linear_schedule(init_value=lowerCamelCase__     ,			end_value=lowerCamelCase__     ,			transition_steps=lowerCamelCase__      )
	__lowerCamelCase    :    str 	=			optax.linear_schedule(init_value=lowerCamelCase__     ,			end_value=1e-7     ,			transition_steps=lowerCamelCase__      )
	__lowerCamelCase    :    int 	=			optax.join_schedules(schedules=[warmup_fn, decay_fn]     ,			boundaries=[warmup_steps]      )
	return lr






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	def weight_decay_mask(lowerCamelCase__      ):
		__lowerCamelCase    :    Dict 	=			traverse_util.flatten_dict(lowerCamelCase__      )
		__lowerCamelCase    :    int 	=			{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
		return traverse_util.unflatten_dict(lowerCamelCase__      )

	__lowerCamelCase    :    List[Any] 	=			scheduler_fn(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	__lowerCamelCase    :    Tuple 	=			optax.adamw(learning_rate=lowerCamelCase__     ,			weight_decay=lowerCamelCase__     ,			mask=lowerCamelCase__      )
	return tx, lr







# Copyright 2021 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.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a		=[
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	__lowerCamelCase    :    int 	=			True
	while ask_again:
		__lowerCamelCase    :    Dict 	=			input(lowerCamelCase__      )
		try:
			if default is not None and len(lowerCamelCase__      ) == 0:
				return default
			return convert_value(lowerCamelCase__      ) if convert_value is not None else result
		except Exception:
			if error_message is not None:
				print(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=[]     ,			lowerCamelCase__=None     ,			lowerCamelCase__=0      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			BulletMenu(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			menu.run(default_choice=lowerCamelCase__      )
	return convert_value(lowerCamelCase__      ) if convert_value is not None else result






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			int(lowerCamelCase__      )
	return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return DynamoBackend(DYNAMO_BACKENDS[value]      ).value






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	return {"yes": True, "no": False}[value.lower()]






class 	A_		(	argparse.RawDescriptionHelpFormatter    ):
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			super()._format_usage(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			usage.replace('<command> [<args>] '       ,'')
								return usage




