
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		(  A_  ):
     UpperCAmelCase__    :							BigBirdConfig
     UpperCAmelCase__    :							jnp.dtype									=		jnp.floataa
     UpperCAmelCase__    :							bool									=		True





     def 		UpperCAmelCase(self				:   Any       )						->						Optional[Any]:
         super().setup()
         snake_case								= nn.Dense(5    ,   dtype=self.dtype       )





     def __call__(self				:   Optional[Any]    ,   *_A				:   Optional[Any]    ,   **_A				:   Dict       )						->						str:
         snake_case								= super().__call__(*_A    ,   **_A       )
         snake_case								= self.cls(outputs[2]       )
         return outputs[:2] + (cls_out,)







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Dict									=		FlaxBigBirdForNaturalQuestionsModule



def 						lowercase_      (       A__ :    List[str]			,		A__ :    int			,		A__ :    str			,		A__ :    Optional[int]			,		A__ :    str			,		A__ :    str		)							->							Any:
    """simple docstring"""

    def cross_entropy(A__ :    Tuple			,		A__ :    Tuple			,		A__ :    int=None		):
        snake_case								= logits.shape[-1]
        snake_case								= (labels[..., None] == jnp.arange(A__		)[None]).astype("f4"		)
        snake_case								= jax.nn.log_softmax(A__			,		axis=-1		)
        snake_case								= -jnp.sum(labels * logits			,		axis=-1		)
        if reduction is not None:
            snake_case								= reduction(A__		)
        return loss

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







@dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							str									=		"google/bigbird-roberta-base"
     UpperCAmelCase__    :							int									=		30_00
     UpperCAmelCase__    :							int									=		1_05_00

     UpperCAmelCase__    :							int									=		1_28
     UpperCAmelCase__    :							int									=		3

     UpperCAmelCase__    :							int									=		1
     UpperCAmelCase__    :							int									=		5

     # tx_args
     UpperCAmelCase__    :							float									=		3e-5
     UpperCAmelCase__    :							float									=		0.0
     UpperCAmelCase__    :							int									=		2_00_00
     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 		UpperCAmelCase(self				:   str       )						->						str:
         os.makedirs(self.base_dir    ,   exist_ok=_A       )
         snake_case								= os.path.join(self.base_dir    ,   self.save_dir       )
         snake_case								= self.batch_size_per_device * jax.device_count()







@dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							int
     UpperCAmelCase__    :							int									=		40_96  # no dynamic padding on TPUs





     def __call__(self				:   Union[str, Any]    ,   _A				:   Optional[int]       )						->						List[str]:
         snake_case								= self.collate_fn(_A       )
         snake_case								= jax.tree_util.tree_map(_A    ,   _A       )
         return batch





     def 		UpperCAmelCase(self				:   str    ,   _A				:   List[Any]       )						->						str:
         snake_case					,							snake_case								= self.fetch_inputs(features["input_ids"]       )
         snake_case								= {
             "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 		UpperCAmelCase(self				:   Any    ,   _A				:   list       )						->						str:
         snake_case								= [self._fetch_inputs(_A       ) for ids in input_ids]
         return zip(*_A       )





     def 		UpperCAmelCase(self				:   List[Any]    ,   _A				:   list       )						->						Union[str, Any]:
         snake_case								= [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 						lowercase_      (       A__ :    List[str]			,		A__ :    Optional[int]			,		A__ :    Tuple=None		)							->							Dict:
    """simple docstring"""

    if seed is not None:
        snake_case								= dataset.shuffle(seed=A__		)
    for i in range(len(A__		) // batch_size		):
        snake_case								= dataset[i * batch_size : (i + 1) * batch_size]
        yield dict(A__		)



@partial(jax.pmap			,		axis_name="batch"		)
def 						lowercase_      (       A__ :    Tuple			,		A__ :    List[Any]			,		**A__ :    str		)							->							Optional[int]:
    """simple docstring"""

    def loss_fn(A__ :    Optional[Any]		):
        snake_case								= model_inputs.pop("start_labels"		)
        snake_case								= model_inputs.pop("end_labels"		)
        snake_case								= model_inputs.pop("pooled_labels"		)

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

        return state.loss_fn(
            A__			,		A__			,		A__			,		A__			,		A__			,		A__			,		)

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

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



@partial(jax.pmap			,		axis_name="batch"		)
def 						lowercase_      (       A__ :    Optional[Any]			,		**A__ :    int		)							->							List[str]:
    """simple docstring"""

    snake_case								= model_inputs.pop("start_labels"		)
    snake_case								= model_inputs.pop("end_labels"		)
    snake_case								= model_inputs.pop("pooled_labels"		)

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

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







class 					lowerCamelCase		(  train_state.TrainState  ):
     UpperCAmelCase__    :							Callable									=		struct.field(pytree_node=A_  )







@dataclass
class 					lowerCamelCase		:
     UpperCAmelCase__    :							Args
     UpperCAmelCase__    :							Callable
     UpperCAmelCase__    :							Callable
     UpperCAmelCase__    :							Callable
     UpperCAmelCase__    :							Callable
     UpperCAmelCase__    :							wandb
     UpperCAmelCase__    :							Callable									=		None





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Dict    ,   _A				:   Any    ,   _A				:   str    ,   _A				:   Any=None       )						->						List[Any]:
         snake_case								= model.params
         snake_case								= 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								= restore_checkpoint(_A    ,   _A       )
             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,
             }
             snake_case					,							snake_case								= build_tx(**_A       )
             snake_case								= train_state.TrainState(
                 step=_A    ,   apply_fn=model.__call__    ,   params=_A    ,   tx=_A    ,   opt_state=_A    ,   )
             snake_case								= args
             snake_case								= data_collator
             snake_case								= lr
             snake_case								= params
         snake_case								= jax_utils.replicate(_A       )
         return state





     def 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   Optional[Any]    ,   _A				:   Optional[int]    ,   _A				:   int       )						->						int:
         snake_case								= self.args
         snake_case								= len(_A       ) // args.batch_size

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

                     snake_case								= self.evaluate(_A    ,   _A       )
                     snake_case								= {
                         "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 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Dict    ,   _A				:   List[str]       )						->						Tuple:
         snake_case								= get_batched_dataset(_A    ,   self.args.batch_size       )
         snake_case								= len(_A       ) // self.args.batch_size
         snake_case								= jnp.array(0    ,   dtype=jnp.floataa       )
         snake_case								= 0
         for batch in tqdm(_A    ,   total=_A    ,   desc="Evaluating ... "       ):
             snake_case								= self.data_collator(_A       )
             snake_case								= self.val_step_fn(_A    ,   **_A       )
             running_loss += jax_utils.unreplicate(metrics["loss"]       )
             i += 1
         return running_loss / i





     def 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   Optional[int]    ,   _A				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= 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 						lowercase_      (       A__ :    List[Any]			,		A__ :    Optional[Any]		)							->							Any:
    """simple docstring"""

    print(F'RESTORING CHECKPOINT FROM {save_dir}'			,		end=" ... "		)
    with open(os.path.join(A__			,		"flax_model.msgpack"		)			,		"rb"		) as f:
        snake_case								= from_bytes(state.params			,		f.read()		)

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

    snake_case								= joblib.load(os.path.join(A__			,		"args.joblib"		)		)
    snake_case								= joblib.load(os.path.join(A__			,		"data_collator.joblib"		)		)

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

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



def 						lowercase_      (       A__ :    str			,		A__ :    str			,		A__ :    Dict			,		A__ :    List[Any]		)							->							Union[str, Any]:
    """simple docstring"""

    snake_case								= num_train_steps - warmup_steps
    snake_case								= optax.linear_schedule(init_value=A__			,		end_value=A__			,		transition_steps=A__		)
    snake_case								= optax.linear_schedule(init_value=A__			,		end_value=1e-7			,		transition_steps=A__		)
    snake_case								= optax.join_schedules(schedules=[warmup_fn, decay_fn]			,		boundaries=[warmup_steps]		)
    return lr





def 						lowercase_      (       A__ :    int			,		A__ :    Optional[Any]			,		A__ :    Dict			,		A__ :    Union[str, Any]			,		A__ :    int		)							->							Tuple:
    """simple docstring"""

    def weight_decay_mask(A__ :    List[Any]		):
        snake_case								= traverse_util.flatten_dict(A__		)
        snake_case								= {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
        return traverse_util.unflatten_dict(A__		)

    snake_case								= scheduler_fn(A__			,		A__			,		A__			,		A__		)

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



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


_A   =			logging.get_logger(__name__)

_A   =			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_A   =			{
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

_A   =			{"mobilebert-uncased": 5_12}


_A   =			{}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Tuple									=		VOCAB_FILES_NAMES
     UpperCAmelCase__    :							Any									=		PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase__    :							Tuple									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__    :							Optional[Any]									=		MobileBertTokenizer





     def __init__(self				:   Any    ,   _A				:   str=None    ,   _A				:   str=None    ,   _A				:   Union[str, Any]=True    ,   _A				:   Optional[Any]="[UNK]"    ,   _A				:   int="[SEP]"    ,   _A				:   Dict="[PAD]"    ,   _A				:   int="[CLS]"    ,   _A				:   Union[str, Any]="[MASK]"    ,   _A				:   Any=True    ,   _A				:   Dict=None    ,   **_A				:   List[str]    ,   )						->						List[str]:
         super().__init__(
             _A    ,   tokenizer_file=_A    ,   do_lower_case=_A    ,   unk_token=_A    ,   sep_token=_A    ,   pad_token=_A    ,   cls_token=_A    ,   mask_token=_A    ,   tokenize_chinese_chars=_A    ,   strip_accents=_A    ,   **_A    ,   )

         snake_case								= json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("lowercase"    ,   _A       ) != do_lower_case
             or normalizer_state.get("strip_accents"    ,   _A       ) != strip_accents
             or normalizer_state.get("handle_chinese_chars"    ,   _A       ) != tokenize_chinese_chars
         ):
             snake_case								= getattr(_A    ,   normalizer_state.pop("type"       )       )
             snake_case								= do_lower_case
             snake_case								= strip_accents
             snake_case								= tokenize_chinese_chars
             snake_case								= normalizer_class(**_A       )

         snake_case								= do_lower_case





     def 		UpperCAmelCase(self				:   List[str]    ,   _A				:   Union[str, Any]    ,   _A				:   Dict=None       )						->						Optional[Any]:
         snake_case								= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
             output += token_ids_a + [self.sep_token_id]

         return output





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   List[int]    ,   _A				:   Optional[List[int]] = None       )						->						List[int]:
         snake_case								= [self.sep_token_id]
         snake_case								= [self.cls_token_id]
         if token_ids_a is None:
             return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





     def 		UpperCAmelCase(self				:   int    ,   _A				:   str    ,   _A				:   Optional[str] = None       )						->						Tuple[str]:
         snake_case								= self._tokenizer.model.save(_A    ,   name=_A       )
         return tuple(_A       )

