






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 ( __lowercase    ):
      _a				:    BigBirdConfig
      _a				:    jnp.dtype				      =				jnp.floataa
      _a				:    bool				      =				True
      def     lowercase__       (  self ):
       super().setup()
       snake_case__	:     Tuple											=nn.Dense(5					,      dtype=self.dtype )



      def __call__(  self					,      *a					,      **a ):
       snake_case__	:     str											=super().__call__(*a					,      **a )
       snake_case__	:     Any											=self.cls(outputs[2] )
       return outputs[:2] + (cls_out,)







class 			_lowercase ( __lowercase    ):
      _a				:    Dict				      =				FlaxBigBirdForNaturalQuestionsModule







def       A__				(   _a	:       Any		,	_a	:       Optional[Any]		,	_a	:       List[str]		,	_a	:       Dict		,	_a	:       List[str]		,	_a	:       List[str]						):





 '''simple docstring'''







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

 snake_case__	:     Union[str, Any]											=partial(_a		,	reduction=jnp.mean						)
 snake_case__	:     Optional[int]											=cross_entropy(_a		,	_a						)
 snake_case__	:     Optional[int]											=cross_entropy(_a		,	_a						)
 snake_case__	:     Optional[Any]											=cross_entropy(_a		,	_a						)
 return (start_loss + end_loss + pooled_loss) / 3







@dataclass
class 			_lowercase :
      _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				      =				0.0_095

      _a				:    str				      =				"bigbird-roberta-natural-questions"
      _a				:    str				      =				"training-expt"
      _a				:    str				      =				"data/nq-training.jsonl"
      _a				:    str				      =				"data/nq-validation.jsonl"
      def     lowercase__       (  self ):
       os.makedirs(self.base_dir					,      exist_ok=a )
       snake_case__	:     int											=os.path.join(self.base_dir					,      self.save_dir )
       snake_case__	:     Union[str, Any]											=self.batch_size_per_device * jax.device_count()







@dataclass
class 			_lowercase :
      _a				:    int
      _a				:    int				      =				4096  # no dynamic padding on TPUs
      def __call__(  self					,      a ):
       snake_case__	:     str											=self.collate_fn(a )
       snake_case__	:     List[str]											=jax.tree_util.tree_map(a					,      a )
       return batch
      def     lowercase__       (  self					,      a ):
       snake_case__							, snake_case__	:     Tuple											=self.fetch_inputs(features["""input_ids"""] )
       snake_case__	:     Optional[int]											={
           """input_ids""": jnp.array(a					,      dtype=jnp.intaa ),
           """attention_mask""": jnp.array(a					,      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     lowercase__       (  self					,      a ):
       snake_case__	:     str											=[self._fetch_inputs(a ) for ids in input_ids]
       return zip(*a )



      def     lowercase__       (  self					,      a ):
       snake_case__	:     Dict											=[1 for _ in range(len(a ) )]
       while len(a ) < self.max_length:
        input_ids.append(self.pad_id )
        attention_mask.append(0 )
       return input_ids, attention_mask







def       A__				(   _a	:       Union[str, Any]		,	_a	:       str		,	_a	:       Optional[int]=None						):





 '''simple docstring'''







 if seed is not None:
  snake_case__	:     List[str]											=dataset.shuffle(seed=_a						)
 for i in range(len(_a						) // batch_size						):
  snake_case__	:     Union[str, Any]											=dataset[i * batch_size : (i + 1) * batch_size]
  yield dict(_a						)







@partial(jax.pmap		,	axis_name="""batch"""						)
def       A__				(   _a	:       Dict		,	_a	:       Union[str, Any]		,	**_a	:       Optional[Any]						):





 '''simple docstring'''







 def loss_fn(_a	:       str						):
  snake_case__	:     Union[str, Any]											=model_inputs.pop("""start_labels"""						)
  snake_case__	:     Any											=model_inputs.pop("""end_labels"""						)
  snake_case__	:     Optional[int]											=model_inputs.pop("""pooled_labels"""						)

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

  return state.loss_fn(
      _a		,	_a		,	_a		,	_a		,	_a		,	_a		,	)

 snake_case__							, snake_case__	:     Optional[int]											=jax.random.split(_a						)
 snake_case__	:     Optional[int]											=jax.value_and_grad(_a						)
 snake_case__							, snake_case__	:     Any											=grad_fn(state.params						)
 snake_case__	:     Dict											=jax.lax.pmean({"""loss""": loss}		,	axis_name="""batch"""						)
 snake_case__	:     Optional[Any]											=jax.lax.pmean(_a		,	"""batch"""						)

 snake_case__	:     Optional[int]											=state.apply_gradients(grads=_a						)
 return state, metrics, new_drp_rng







@partial(jax.pmap		,	axis_name="""batch"""						)
def       A__				(   _a	:       int		,	**_a	:       int						):





 '''simple docstring'''







 snake_case__	:     int											=model_inputs.pop("""start_labels"""						)
 snake_case__	:     int											=model_inputs.pop("""end_labels"""						)
 snake_case__	:     int											=model_inputs.pop("""pooled_labels"""						)

 snake_case__	:     List[str]											=state.apply_fn(**_a		,	params=state.params		,	train=_a						)
 snake_case__							, snake_case__							, snake_case__	:     Optional[Any]											=outputs

 snake_case__	:     Union[str, Any]											=state.loss_fn(_a		,	_a		,	_a		,	_a		,	_a		,	_a						)
 snake_case__	:     Dict											=jax.lax.pmean({"""loss""": loss}		,	axis_name="""batch"""						)
 return metrics







class 			_lowercase ( train_state.TrainState    ):
      _a				:    Callable				      =				struct.field(pytree_node=__lowercase    )







@dataclass
class 			_lowercase :
      _a				:    Args
      _a				:    Callable
      _a				:    Callable
      _a				:    Callable
      _a				:    Callable
      _a				:    wandb
      _a				:    Callable				      =				None
      def     lowercase__       (  self					,      a					,      a					,      a					,      a=None ):
       snake_case__	:     int											=model.params
       snake_case__	:     Union[str, Any]											=TrainState.create(
           apply_fn=model.__call__					,      params=a					,      tx=a					,      loss_fn=a					,      )
       if ckpt_dir is not None:
        snake_case__							, snake_case__							, snake_case__							, snake_case__							, snake_case__	:     Any											=restore_checkpoint(a					,      a )
        snake_case__	:     Optional[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,
        }
        snake_case__							, snake_case__	:     Optional[Any]											=build_tx(**a )
        snake_case__	:     Any											=train_state.TrainState(
            step=a					,      apply_fn=model.__call__					,      params=a					,      tx=a					,      opt_state=a					,      )
        snake_case__	:     str											=args
        snake_case__	:     Optional[int]											=data_collator
        snake_case__	:     str											=lr
        snake_case__	:     str											=params
       snake_case__	:     Union[str, Any]											=jax_utils.replicate(a )
       return state
      def     lowercase__       (  self					,      a					,      a					,      a ):
       snake_case__	:     int											=self.args
       snake_case__	:     List[str]											=len(a ) // args.batch_size

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

          snake_case__	:     List[Any]											=self.evaluate(a					,      a )
          snake_case__	:     Dict											={
              """step""": state_step.item(),
              """eval_loss""": eval_loss.item(),
              """tr_loss""": tr_loss,
              """lr""": lr.item(),
          }
          tqdm.write(str(a ) )
          self.logger.log(a					,      commit=a )

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



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







def       A__				(   _a	:       Optional[int]		,	_a	:       str						):





 '''simple docstring'''







 print(f"RESTORING CHECKPOINT FROM {save_dir}"		,	end=""" ... """						)
 with open(os.path.join(_a		,	"""flax_model.msgpack"""						)		,	"""rb"""						) as f:
  snake_case__	:     Optional[Any]											=from_bytes(state.params		,	f.read()						)

 with open(os.path.join(_a		,	"""opt_state.msgpack"""						)		,	"""rb"""						) as f:
  snake_case__	:     Optional[Any]											=from_bytes(state.opt_state		,	f.read()						)

 snake_case__	:     int											=joblib.load(os.path.join(_a		,	"""args.joblib"""						)						)
 snake_case__	:     str											=joblib.load(os.path.join(_a		,	"""data_collator.joblib"""						)						)

 with open(os.path.join(_a		,	"""training_state.json"""						)		,	"""r"""						) as f:
  snake_case__	:     int											=json.load(_a						)
 snake_case__	:     Union[str, Any]											=training_state["""step"""]

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







def       A__				(   _a	:       Optional[int]		,	_a	:       int		,	_a	:       Union[str, Any]		,	_a	:       Union[str, Any]						):





 '''simple docstring'''







 snake_case__	:     int											=num_train_steps - warmup_steps
 snake_case__	:     List[str]											=optax.linear_schedule(init_value=_a		,	end_value=_a		,	transition_steps=_a						)
 snake_case__	:     int											=optax.linear_schedule(init_value=_a		,	end_value=1E-7		,	transition_steps=_a						)
 snake_case__	:     List[str]											=optax.join_schedules(schedules=[warmup_fn, decay_fn]		,	boundaries=[warmup_steps]						)
 return lr







def       A__				(   _a	:       int		,	_a	:       List[Any]		,	_a	:       Dict		,	_a	:       str		,	_a	:       str						):





 '''simple docstring'''







 def weight_decay_mask(_a	:       Union[str, Any]						):
  snake_case__	:     Any											=traverse_util.flatten_dict(_a						)
  snake_case__	:     Optional[int]											={k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
  return traverse_util.unflatten_dict(_a						)

 snake_case__	:     Any											=scheduler_fn(_a		,	_a		,	_a		,	_a						)

 snake_case__	:     Optional[Any]											=optax.adamw(learning_rate=_a		,	weight_decay=_a		,	mask=_a						)
 return tx, lr







import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 				__A(lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






		_UpperCamelCase         =						os.path.join(args.tf_model_dir    ,	"""parameters.json"""			)
		_UpperCamelCase         =						json.loads(open(lowerCAmelCase			).read()			)
		if not params:
				raise ValueError(
				    F'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'			)
		if not args.output.endswith(""".pt"""			):
				_UpperCamelCase         =						args.output + """.pt"""
		_UpperCamelCase         =						OrderedDict()
		with tf.device("""/CPU:0"""			):
				_UpperCamelCase         =						tf.train.load_checkpoint(args.tf_model_dir			)
				_UpperCamelCase         =						reader.get_variable_to_shape_map()
				for key_name in shapes.keys():
						_UpperCamelCase         =						reader.get_tensor(lowerCAmelCase			).astype(np.floataa			)
						if key_name.endswith("""/adam_m"""			) or key_name.endswith("""/adam_v"""			):
								continue
						if key_name.startswith("""pasts/"""			):
								if key_name.startswith("""pasts/mlp"""			):
										_UpperCamelCase         =						int(key_name[9]			)
								elif key_name.startswith("""pasts/out"""			):
										_UpperCamelCase         =						8
								_UpperCamelCase         =						"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
								_UpperCamelCase         =						vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name.startswith("""model/moe"""			):
								_UpperCamelCase         =						int(key_name[9:].split("""/"""			)[0]			)
								if key_name.endswith("""/switch_gating/kernel"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
										_UpperCamelCase         =						vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/softmlp/kernel"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
										_UpperCamelCase         =						vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/wo/kernel"""			) or key_name.endswith("""/wi/kernel"""			):
										_UpperCamelCase         =						key_name[-9:-7]
										for i in range(1_6			):
												_UpperCamelCase         =						"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
												_UpperCamelCase         =						(
												    vnp[i].transpose([1, 0]			).copy()
												)  # In Mesh-Tensorflow, it is one array, so it is divided
												_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name.startswith("""model/mlp"""			):
								_UpperCamelCase         =						int(key_name[9:].split("""/"""			)[0]			)
								if key_name.endswith("""/p1/kernel"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
										_UpperCamelCase         =						vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/p1/bias"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
										_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/p2/kernel"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
										_UpperCamelCase         =						vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/p2/bias"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
										_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name.startswith("""model/ln"""			):
								_UpperCamelCase         =						int(key_name[8:].split("""/"""			)[0]			)
								if key_name.endswith("""/b"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.norm.bias""" % player
										_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/g"""			):
										_UpperCamelCase         =						"""model.blocks.%d.feed_forward.norm.weight""" % player
										_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name.startswith("""model/att"""			):
								_UpperCamelCase         =						int(key_name[9:].split("""/"""			)[0]			)
								if key_name.endswith("""/qkv/kernel"""			):
										_UpperCamelCase         =						vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
										_UpperCamelCase         =						state[:, 0, :, :]
										_UpperCamelCase         =						state[:, 1, :, :]
										_UpperCamelCase         =						state[:, 2, :, :]
										_UpperCamelCase         =						(
										    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						(
										    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						(
										    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
										    .transpose([1, 0]			)
										    .copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
										_UpperCamelCase         =						"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
										_UpperCamelCase         =						"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/o/kernel"""			):
										_UpperCamelCase         =						"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
										_UpperCamelCase         =						(
										    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
										)  # Mesh-Tensorflow is a diagonal matrix
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name.startswith("""model/an"""			):
								_UpperCamelCase         =						int(key_name[8:].split("""/"""			)[0]			)
								if key_name.endswith("""/b"""			):
										_UpperCamelCase         =						"""model.blocks.%d.self_attn.norm.bias""" % player
										_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								elif key_name.endswith("""/g"""			):
										_UpperCamelCase         =						"""model.blocks.%d.self_attn.norm.weight""" % player
										_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif (
						    key_name.startswith("""model/wte"""			)
						    or key_name.startswith("""model/wpe"""			)
						    or key_name.startswith("""model/ete"""			)
						):
								_UpperCamelCase         =						{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
								    key_name[-3:]
								]
								_UpperCamelCase         =						"""model.%s.weight""" % nlayer
								_UpperCamelCase         =						vnp.copy()  # same in embedded
								_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
								if key_name.startswith("""model/wte"""			):
										_UpperCamelCase         =						"""lm_head.weight"""
										_UpperCamelCase         =						vnp.copy()  # same in embedded
										_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name.startswith("""model/wob"""			):
								_UpperCamelCase         =						"""final_logits_bias"""
								_UpperCamelCase         =						vnp.copy()  # same in embedded
								_UpperCamelCase         =						state.reshape((1, -1)			)
								_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name == "model/dense/kernel":
								_UpperCamelCase         =						"""model.last_project.weight"""
								_UpperCamelCase         =						vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
								_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
						elif key_name == "model/dense_1/bias":
								_UpperCamelCase         =						"""model.last_project.bias"""
								_UpperCamelCase         =						vnp.copy()  # same because it is one dimensional
								_UpperCamelCase         =						torch.tensor(lowerCAmelCase			)
		torch.save(lowerCAmelCase    ,	args.output			)


if __name__ == "__main__":
		lowerCamelCase__						=				argparse.ArgumentParser(
		    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
		parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
		lowerCamelCase__						=				parser.parse_args()
		convert_tf_gptsan_to_pt(args)



