


"""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 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







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







		def 							__A						(	self				)     ->							Tuple:
								super().setup()
								SCREAMING_SNAKE_CASE													=					nn.Dense(5						,       dtype=self.dtype				)







		def __call__(	self						,       *lowerCAmelCase__						,       **lowerCAmelCase__				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					super().__call__(*lowerCAmelCase__						,       **lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.cls(outputs[2]				)
								return outputs[:2] + (cls_out,)





class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     List[str]								=     FlaxBigBirdForNaturalQuestionsModule





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

						SCREAMING_SNAKE_CASE													=					partial(SCREAMING_SNAKE_CASE_ ,			reduction=jnp.mean  )
						SCREAMING_SNAKE_CASE													=					cross_entropy(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					cross_entropy(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					cross_entropy(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     str								=     "google/bigbird-roberta-base"
		SCREAMING_SNAKE_CASE_						:     int								=     3_0_0_0
		SCREAMING_SNAKE_CASE_						:     int								=     1_0_5_0_0

		SCREAMING_SNAKE_CASE_						:     int								=     1_2_8
		SCREAMING_SNAKE_CASE_						:     int								=     3

		SCREAMING_SNAKE_CASE_						:     int								=     1
		SCREAMING_SNAKE_CASE_						:     int								=     5

		# tx_args
		SCREAMING_SNAKE_CASE_						:     float								=     3e-5
		SCREAMING_SNAKE_CASE_						:     float								=     0.0
		SCREAMING_SNAKE_CASE_						:     int								=     2_0_0_0_0
		SCREAMING_SNAKE_CASE_						:     float								=     0.0_0_9_5

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







		def 							__A						(	self				)     ->							int:
								os.makedirs(self.base_dir						,       exist_ok=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					os.path.join(self.base_dir						,       self.save_dir				)
								SCREAMING_SNAKE_CASE													=					self.batch_size_per_device * jax.device_count()





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     int
		SCREAMING_SNAKE_CASE_						:     int								=     4_0_9_6  # no dynamic padding on TPUs







		def __call__(	self						,       lowerCAmelCase__				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					self.collate_fn(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					jax.tree_util.tree_map(lowerCAmelCase__						,       lowerCAmelCase__				)
								return batch







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					self.fetch_inputs(features['input_ids']				)
								SCREAMING_SNAKE_CASE													=					{
								    '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						,       lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					[self._fetch_inputs(lowerCAmelCase__				) for ids in input_ids]
								return zip(*lowerCAmelCase__				)







		def 							__A						(	self						,       lowerCAmelCase__				)     ->							Any:
								SCREAMING_SNAKE_CASE													=					[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     lowercase			(SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						str:
						if seed is not None:
												SCREAMING_SNAKE_CASE													=					dataset.shuffle(seed=SCREAMING_SNAKE_CASE_  )
						for i in range(len(SCREAMING_SNAKE_CASE_  ) // batch_size  ):
												SCREAMING_SNAKE_CASE													=					dataset[i * batch_size : (i + 1) * batch_size]
												yield dict(SCREAMING_SNAKE_CASE_  )


@partial(jax.pmap ,			axis_name='batch'  )
def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				Any ,			**SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[str]:
						def loss_fn(SCREAMING_SNAKE_CASE_		:				List[str]  ):
												SCREAMING_SNAKE_CASE													=					model_inputs.pop('start_labels'  )
												SCREAMING_SNAKE_CASE													=					model_inputs.pop('end_labels'  )
												SCREAMING_SNAKE_CASE													=					model_inputs.pop('pooled_labels'  )

												SCREAMING_SNAKE_CASE													=					state.apply_fn(**SCREAMING_SNAKE_CASE_ ,			params=SCREAMING_SNAKE_CASE_ ,			dropout_rng=SCREAMING_SNAKE_CASE_ ,			train=SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					outputs

												return state.loss_fn(
												    SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			)

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

						SCREAMING_SNAKE_CASE													=					state.apply_gradients(grads=SCREAMING_SNAKE_CASE_  )
						return state, metrics, new_drp_rng


@partial(jax.pmap ,			axis_name='batch'  )
def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			**SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE													=					model_inputs.pop('start_labels'  )
						SCREAMING_SNAKE_CASE													=					model_inputs.pop('end_labels'  )
						SCREAMING_SNAKE_CASE													=					model_inputs.pop('pooled_labels'  )

						SCREAMING_SNAKE_CASE													=					state.apply_fn(**SCREAMING_SNAKE_CASE_ ,			params=state.params ,			train=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					outputs

						SCREAMING_SNAKE_CASE													=					state.loss_fn(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					jax.lax.pmean({'loss': loss} ,			axis_name='batch'  )
						return metrics
class 					lowerCAmelCase	(					train_state.TrainState    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Callable								=     struct.field(pytree_node=lowerCamelCase_    )





@dataclass
class 					lowerCAmelCase	:




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Args
		SCREAMING_SNAKE_CASE_						:     Callable
		SCREAMING_SNAKE_CASE_						:     Callable
		SCREAMING_SNAKE_CASE_						:     Callable
		SCREAMING_SNAKE_CASE_						:     Callable
		SCREAMING_SNAKE_CASE_						:     wandb
		SCREAMING_SNAKE_CASE_						:     Callable								=     None







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__=None				)     ->							str:
								SCREAMING_SNAKE_CASE													=					model.params
								SCREAMING_SNAKE_CASE													=					TrainState.create(
								    apply_fn=model.__call__						,       params=lowerCAmelCase__						,       tx=lowerCAmelCase__						,       loss_fn=lowerCAmelCase__						,       )
								if ckpt_dir is not None:
														SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					restore_checkpoint(lowerCAmelCase__						,       lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					{
														    'lr': args.lr,
														    'init_lr': args.init_lr,
														    'warmup_steps': args.warmup_steps,
														    'num_train_steps': num_train_steps,
														    'weight_decay': args.weight_decay,
														}
														SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					build_tx(**lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					train_state.TrainState(
														    step=lowerCAmelCase__						,       apply_fn=model.__call__						,       params=lowerCAmelCase__						,       tx=lowerCAmelCase__						,       opt_state=lowerCAmelCase__						,       )
														SCREAMING_SNAKE_CASE													=					args
														SCREAMING_SNAKE_CASE													=					data_collator
														SCREAMING_SNAKE_CASE													=					lr
														SCREAMING_SNAKE_CASE													=					params
								SCREAMING_SNAKE_CASE													=					jax_utils.replicate(lowerCAmelCase__				)
								return state







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Union[str, Any]:
								SCREAMING_SNAKE_CASE													=					self.args
								SCREAMING_SNAKE_CASE													=					len(lowerCAmelCase__				) // args.batch_size

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

																										SCREAMING_SNAKE_CASE													=					self.evaluate(lowerCAmelCase__						,       lowerCAmelCase__				)
																										SCREAMING_SNAKE_CASE													=					{
																										    '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						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					get_batched_dataset(lowerCAmelCase__						,       self.args.batch_size				)
								SCREAMING_SNAKE_CASE													=					len(lowerCAmelCase__				) // self.args.batch_size
								SCREAMING_SNAKE_CASE													=					jnp.array(0						,       dtype=jnp.floataa				)
								SCREAMING_SNAKE_CASE													=					0
								for batch in tqdm(lowerCAmelCase__						,       total=lowerCAmelCase__						,       desc='Evaluating ... '				):
														SCREAMING_SNAKE_CASE													=					self.data_collator(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.val_step_fn(lowerCAmelCase__						,       **lowerCAmelCase__				)
														running_loss += jax_utils.unreplicate(metrics['loss']				)
														i += 1
								return running_loss / i







		def 							__A						(	self						,       lowerCAmelCase__						,       lowerCAmelCase__				)     ->							int:
								SCREAMING_SNAKE_CASE													=					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     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						print(F'RESTORING CHECKPOINT FROM {save_dir}' ,			end=' ... '  )
						with open(os.path.join(SCREAMING_SNAKE_CASE_ ,			'flax_model.msgpack'  ) ,			'rb'  ) as f:
												SCREAMING_SNAKE_CASE													=					from_bytes(state.params ,			f.read()  )

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

						SCREAMING_SNAKE_CASE													=					joblib.load(os.path.join(SCREAMING_SNAKE_CASE_ ,			'args.joblib'  )  )
						SCREAMING_SNAKE_CASE													=					joblib.load(os.path.join(SCREAMING_SNAKE_CASE_ ,			'data_collator.joblib'  )  )

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

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Any  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					num_train_steps - warmup_steps
						SCREAMING_SNAKE_CASE													=					optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE_ ,			end_value=SCREAMING_SNAKE_CASE_ ,			transition_steps=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE_ ,			end_value=1E-7 ,			transition_steps=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					optax.join_schedules(schedules=[warmup_fn, decay_fn] ,			boundaries=[warmup_steps]  )
						return lr


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Any ,			SCREAMING_SNAKE_CASE_		:				Tuple  )   ->						Any:
						def weight_decay_mask(SCREAMING_SNAKE_CASE_		:				Optional[Any]  ):
												SCREAMING_SNAKE_CASE													=					traverse_util.flatten_dict(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
												return traverse_util.unflatten_dict(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					scheduler_fn(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					optax.adamw(learning_rate=SCREAMING_SNAKE_CASE_ ,			weight_decay=SCREAMING_SNAKE_CASE_ ,			mask=SCREAMING_SNAKE_CASE_  )
						return tx, lr






"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


