
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    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  BigBirdConfig
 __lowerCAmelCase	:  jnp.dtype	   =      jnp.floataa
 __lowerCAmelCase	:  bool	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setup()
   lowercase__	: Dict     		=   nn.Dense(5       ,	dtype=self.dtype)



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


   '''simple docstring'''




   lowercase__	: List[str]     		=   super().__call__(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   self.cls(outputs[2])
   return outputs[:2] + (cls_out,)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      FlaxBigBirdForNaturalQuestionsModule

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






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

  lowercase__	: int     		=   partial(lowercase_			,			reduction=jnp.mean )
  lowercase__	: Tuple     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: List[Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: Union[str, Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  return (start_loss + end_loss + pooled_loss) / 3



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

 __lowerCAmelCase	:  int	   =      128
 __lowerCAmelCase	:  int	   =      3

 __lowerCAmelCase	:  int	   =      1
 __lowerCAmelCase	:  int	   =      5

 # tx_args
 __lowerCAmelCase	:  float	   =      3e-5
 __lowerCAmelCase	:  float	   =      0.0
 __lowerCAmelCase	:  int	   =      20_000
 __lowerCAmelCase	:  float	   =      0.0_095

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


 def   lowercase__  (       self):


   '''simple docstring'''




   os.makedirs(self.base_dir       ,	exist_ok=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   os.path.join(self.base_dir       ,	self.save_dir)
   lowercase__	: str     		=   self.batch_size_per_device * jax.device_count()



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  int
 __lowerCAmelCase	:  int	   =      4_096  # no dynamic padding on TPUs


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.collate_fn(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   jax.tree_util.tree_map(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return batch


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.fetch_inputs(features["""input_ids"""])
   lowercase__	: str     		=   {
       """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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   [self._fetch_inputs(SCREAMING_SNAKE_CASE_) for ids in input_ids]
   return zip(*SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   [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 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






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

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			**lowercase_ )		->						int:


  '''simple docstring'''






  def loss_fn(lowercase_ ):
    lowercase__	: Dict     		=   model_inputs.pop("""start_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""end_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""pooled_labels""" )

    lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=lowercase_			,			dropout_rng=lowercase_			,			train=lowercase_ )
    lowercase__		,     lowercase__		,     lowercase__	: Any     		=   outputs

    return state.loss_fn(
        lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

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

  lowercase__	: str     		=   state.apply_gradients(grads=lowercase_ )
  return state, metrics, new_drp_rng

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			**lowercase_ )		->						str:


  '''simple docstring'''






  lowercase__	: Tuple     		=   model_inputs.pop("""start_labels""" )
  lowercase__	: List[str]     		=   model_inputs.pop("""end_labels""" )
  lowercase__	: int     		=   model_inputs.pop("""pooled_labels""" )

  lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=state.params			,			train=lowercase_ )
  lowercase__		,     lowercase__		,     lowercase__	: Optional[int]     		=   outputs

  lowercase__	: Optional[Any]     		=   state.loss_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: List[str]     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  return metrics



class    _snake_case (						train_state.TrainState				):
 __lowerCAmelCase	:  Callable	   =      struct.field(pytree_node=UpperCAmelCase_				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Args
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  wandb
 __lowerCAmelCase	:  Callable	   =      None


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




   lowercase__	: List[str]     		=   model.params
   lowercase__	: Dict     		=   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:
     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__		,     lowercase__	: str     		=   restore_checkpoint(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
     lowercase__	: str     		=   {
         """lr""": args.lr,
         """init_lr""": args.init_lr,
         """warmup_steps""": args.warmup_steps,
         """num_train_steps""": num_train_steps,
         """weight_decay""": args.weight_decay,
     }
     lowercase__		,     lowercase__	: Any     		=   build_tx(**SCREAMING_SNAKE_CASE_)
     lowercase__	: 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_       ,	)
     lowercase__	: Optional[Any]     		=   args
     lowercase__	: Union[str, Any]     		=   data_collator
     lowercase__	: str     		=   lr
     lowercase__	: Union[str, Any]     		=   params
   lowercase__	: Dict     		=   jax_utils.replicate(SCREAMING_SNAKE_CASE_)
   return state


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.args
   lowercase__	: List[str]     		=   len(SCREAMING_SNAKE_CASE_) // args.batch_size

   lowercase__	: int     		=   jax.random.PRNGKey(0)
   lowercase__	: Union[str, Any]     		=   jax.random.split(SCREAMING_SNAKE_CASE_       ,	jax.device_count())
   for epoch in range(args.max_epochs):
     lowercase__	: Tuple     		=   jnp.array(0       ,	dtype=jnp.floataa)
     lowercase__	: List[str]     		=   get_batched_dataset(SCREAMING_SNAKE_CASE_       ,	args.batch_size       ,	seed=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[str]     		=   0
     for batch in tqdm(SCREAMING_SNAKE_CASE_       ,	total=SCREAMING_SNAKE_CASE_       ,	desc=f'Running EPOCH-{epoch}'):
       lowercase__	: Tuple     		=   self.data_collator(SCREAMING_SNAKE_CASE_)
       lowercase__		,     lowercase__		,     lowercase__	: List[Any]     		=   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:
         lowercase__	: List[str]     		=   jax_utils.unreplicate(state.step)
         lowercase__	: str     		=   running_loss.item() / i
         lowercase__	: Tuple     		=   self.scheduler_fn(state_step - 1)

         lowercase__	: Tuple     		=   self.evaluate(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
         lowercase__	: List[Any]     		=   {
             """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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   get_batched_dataset(SCREAMING_SNAKE_CASE_       ,	self.args.batch_size)
   lowercase__	: Tuple     		=   len(SCREAMING_SNAKE_CASE_) // self.args.batch_size
   lowercase__	: Union[str, Any]     		=   jnp.array(0       ,	dtype=jnp.floataa)
   lowercase__	: Optional[Any]     		=   0
   for batch in tqdm(SCREAMING_SNAKE_CASE_       ,	total=SCREAMING_SNAKE_CASE_       ,	desc="""Evaluating ... """):
     lowercase__	: Tuple     		=   self.data_collator(SCREAMING_SNAKE_CASE_)
     lowercase__	: List[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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   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 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






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

  with open(os.path.join(lowercase_			,			"""opt_state.msgpack""" )			,			"""rb""" ) as f:
    lowercase__	: Dict     		=   from_bytes(state.opt_state			,			f.read() )

  lowercase__	: Any     		=   joblib.load(os.path.join(lowercase_			,			"""args.joblib""" ) )
  lowercase__	: Optional[int]     		=   joblib.load(os.path.join(lowercase_			,			"""data_collator.joblib""" ) )

  with open(os.path.join(lowercase_			,			"""training_state.json""" )			,			"""r""" ) as f:
    lowercase__	: int     		=   json.load(lowercase_ )
  lowercase__	: Optional[Any]     		=   training_state["""step"""]

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   num_train_steps - warmup_steps
  lowercase__	: int     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=lowercase_			,			transition_steps=lowercase_ )
  lowercase__	: Optional[int]     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=1E-7			,			transition_steps=lowercase_ )
  lowercase__	: Any     		=   optax.join_schedules(schedules=[warmup_fn, decay_fn]			,			boundaries=[warmup_steps] )
  return lr



def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






  def weight_decay_mask(lowercase_ ):
    lowercase__	: Dict     		=   traverse_util.flatten_dict(lowercase_ )
    lowercase__	: int     		=   {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
    return traverse_util.unflatten_dict(lowercase_ )

  lowercase__	: Optional[int]     		=   scheduler_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  lowercase__	: int     		=   optax.adamw(learning_rate=lowercase_			,			weight_decay=lowercase_			,			mask=lowercase_ )
  return tx, lr










'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


