
"""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	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		42
     lowercase		     =		jnp.floataa
     lowercase		     =		True





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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







     def __call__(			self			,  *__UpperCAmelCase			,  **__UpperCAmelCase   ):

      '''simple docstring'''




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


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		FlaxBigBirdForNaturalQuestionsModule






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

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

@dataclass
class __lowerCAmelCase	:
     lowercase		     =		"google/bigbird-roberta-base"
     lowercase		     =		3_000
     lowercase		     =		10_500

     lowercase		     =		128
     lowercase		     =		3

     lowercase		     =		1
     lowercase		     =		5

     # tx_args
     lowercase		     =		3e-5
     lowercase		     =		0.0
     lowercase		     =		20_000
     lowercase		     =		0.0_095

     lowercase		     =		"bigbird-roberta-natural-questions"
     lowercase		     =		"training-expt"
     lowercase		     =		"data/nq-training.jsonl"
     lowercase		     =		"data/nq-validation.jsonl"





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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


@dataclass
class __lowerCAmelCase	:
     lowercase		     =		42
     lowercase		     =		4_096  # no dynamic padding on TPUs





     def __call__(			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.collate_fn(__UpperCAmelCase   )
      __UpperCamelCase       						= jax.tree_util.tree_map(__UpperCAmelCase			,  __UpperCAmelCase   )
      return batch





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.fetch_inputs(features['input_ids']   )
      __UpperCamelCase       						= {
          'input_ids': jnp.array(__UpperCAmelCase			,  dtype=jnp.intaa   ),
          'attention_mask': jnp.array(__UpperCAmelCase			,  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			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= [self._fetch_inputs(__UpperCAmelCase   ) for ids in input_ids]
      return zip(*__UpperCAmelCase   )







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

      '''simple docstring'''




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






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






@partial(jax.pmap       ,      axis_name='batch'	)
def 							A		(       snake_case   :Union[str, Any]       ,      snake_case   :List[str]       ,      **snake_case   :Union[str, Any]	)   ->			List[str]:
 def loss_fn(snake_case   :int	):
  __UpperCamelCase       						= model_inputs.pop('start_labels'	)
  __UpperCamelCase       						= model_inputs.pop('end_labels'	)
  __UpperCamelCase       						= model_inputs.pop('pooled_labels'	)

  __UpperCamelCase       						= state.apply_fn(**snake_case       ,      params=snake_case       ,      dropout_rng=snake_case       ,      train=snake_case	)
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= outputs

  return state.loss_fn(
      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      )

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

 __UpperCamelCase       						= state.apply_gradients(grads=snake_case	)
 return state, metrics, new_drp_rng






@partial(jax.pmap       ,      axis_name='batch'	)
def 							A		(       snake_case   :Optional[int]       ,      **snake_case   :List[str]	)   ->			Any:
 __UpperCamelCase       						= model_inputs.pop('start_labels'	)
 __UpperCamelCase       						= model_inputs.pop('end_labels'	)
 __UpperCamelCase       						= model_inputs.pop('pooled_labels'	)

 __UpperCamelCase       						= state.apply_fn(**snake_case       ,      params=state.params       ,      train=snake_case	)
 __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= outputs

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

class __lowerCAmelCase	(   train_state.TrainState    ):
     lowercase		     =		struct.field(pytree_node=__SCREAMING_SNAKE_CASE    )


@dataclass
class __lowerCAmelCase	:
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		42
     lowercase		     =		None





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase=None   ):

      '''simple docstring'''




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





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.args
      __UpperCamelCase       						= len(__UpperCAmelCase   ) // args.batch_size

      __UpperCamelCase       						= jax.random.PRNGKey(0   )
      __UpperCamelCase       						= jax.random.split(__UpperCAmelCase			,  jax.device_count()   )
      for epoch in range(args.max_epochs   ):
       __UpperCamelCase       						= jnp.array(0			,  dtype=jnp.floataa   )
       __UpperCamelCase       						= get_batched_dataset(__UpperCAmelCase			,  args.batch_size			,  seed=__UpperCAmelCase   )
       __UpperCamelCase       						= 0
       for batch in tqdm(__UpperCAmelCase			,  total=__UpperCAmelCase			,  desc=F'Running EPOCH-{epoch}'   ):
        __UpperCamelCase       						= self.data_collator(__UpperCAmelCase   )
        __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= self.train_step_fn(__UpperCAmelCase			,  __UpperCAmelCase			,  **__UpperCAmelCase   )
        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(__UpperCAmelCase			,  __UpperCAmelCase   )
         __UpperCamelCase       						= {
             'step': state_step.item(),
             'eval_loss': eval_loss.item(),
             'tr_loss': tr_loss,
             'lr': lr.item(),
         }
         tqdm.write(str(__UpperCAmelCase   )   )
         self.logger.log(__UpperCAmelCase			,  commit=__UpperCAmelCase   )

        if i % args.save_steps == 0:
         self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}'			,  state=__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




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






def 							A		(       snake_case   :Optional[int]       ,      snake_case   :Dict	)   ->			str:
 print(f'RESTORING CHECKPOINT FROM {save_dir}'       ,      end=' ... '	)
 with open(os.path.join(snake_case       ,      'flax_model.msgpack'	)       ,      'rb'	) as f:
  __UpperCamelCase       						= from_bytes(state.params       ,      f.read()	)

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

 __UpperCamelCase       						= joblib.load(os.path.join(snake_case       ,      'args.joblib'	)	)
 __UpperCamelCase       						= joblib.load(os.path.join(snake_case       ,      'data_collator.joblib'	)	)

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

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






def 							A		(       snake_case   :List[Any]       ,      snake_case   :Any       ,      snake_case   :Tuple       ,      snake_case   :Union[str, Any]	)   ->			str:
 __UpperCamelCase       						= num_train_steps - warmup_steps
 __UpperCamelCase       						= optax.linear_schedule(init_value=snake_case       ,      end_value=snake_case       ,      transition_steps=snake_case	)
 __UpperCamelCase       						= optax.linear_schedule(init_value=snake_case       ,      end_value=1e-7       ,      transition_steps=snake_case	)
 __UpperCamelCase       						= optax.join_schedules(schedules=[warmup_fn, decay_fn]       ,      boundaries=[warmup_steps]	)
 return lr






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

 __UpperCamelCase       						= scheduler_fn(snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)

 __UpperCamelCase       						= optax.adamw(learning_rate=snake_case       ,      weight_decay=snake_case       ,      mask=snake_case	)
 return tx, lr





"""simple docstring"""

import argparse
from collections import defaultdict






def 							A		(       snake_case   :Tuple       ,      snake_case   :List[Any]       ,      snake_case   :List[Any]       ,      snake_case   :Union[str, Any]       ,      snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 __UpperCamelCase       						= f'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= f'class {class_name}('
 __UpperCamelCase       						= f'{4 * " "}def {test_name}('
 __UpperCamelCase       						= f'{8 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= f'{1_6 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= 0
 __UpperCamelCase       						= 0

 __UpperCamelCase       						= []
 for line in lines:
  if line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and in_func and (line.startswith(snake_case	) or line.startswith(snake_case	)):
   __UpperCamelCase       						= len(line.split(correct_line.split()[0]	)[0]	)
   count += 1

   if count == done_test[_id]:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(f'{spaces * " "}{correct_line}'	)
   __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= False
  else:
   new_lines.append(snake_case	)

 with open(snake_case       ,      'w'	) as f:
  for line in new_lines:
   f.write(snake_case	)






def 							A		(       snake_case   :List[str]       ,      snake_case   :str=None	)   ->			Tuple:
 if fail is not None:
  with open(snake_case       ,      'r'	) as f:
   __UpperCamelCase       						= {l.strip() for l in f.readlines()}
 else:
  __UpperCamelCase       						= None

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= defaultdict(snake_case	)
 for line in correct_lines:
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= line.split(';'	)
  if test_failures is None or "::".join([file, class_name, test_name]	) in test_failures:
   overwrite_file(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)


if __name__ == "__main__":
      UpperCamelCase			:       str									=				argparse.ArgumentParser()
      parser.add_argument("--correct_filename", help="filename of tests with expected result")
      parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
      UpperCamelCase			:       List[Any]									=				parser.parse_args()

      main(args.correct_filename, args.fail_filename)



