


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     ):
       lowercase_				:			BigBirdConfig
       lowercase_				:			jnp.dtype									=    jnp.floataa
       lowercase_				:			bool									=    True
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           super().setup()
           _lowercase :							Optional[int]   	=	nn.Dense(5,   dtype=self.dtype)




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							List[str]   	=	super().__call__(*lowerCamelCase,   **lowerCamelCase)
           _lowercase :							Optional[Any]   	=	self.cls(outputs[2])
           return outputs[:2] + (cls_out,)






class _lowerCamelCase(		_a     ):
       lowercase_				:			int									=    FlaxBigBirdForNaturalQuestionsModule



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

    _lowercase :							Dict   	=	partial(lowerCamelCase_							,				reduction=jnp.mean				)
    _lowercase :							Optional[int]   	=	cross_entropy(lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							List[str]   	=	cross_entropy(lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							Tuple   	=	cross_entropy(lowerCamelCase_							,				lowerCamelCase_				)
    return (start_loss + end_loss + pooled_loss) / 3






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

       lowercase_				:			int									=    1_28
       lowercase_				:			int									=    3

       lowercase_				:			int									=    1
       lowercase_				:			int									=    5

       # tx_args
       lowercase_				:			float									=    3e-5
       lowercase_				:			float									=    0.0
       lowercase_				:			int									=    2_00_00
       lowercase_				:			float									=    0.0095

       lowercase_				:			str									=    "bigbird-roberta-natural-questions"
       lowercase_				:			str									=    "training-expt"
       lowercase_				:			str									=    "data/nq-training.jsonl"
       lowercase_				:			str									=    "data/nq-validation.jsonl"
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           os.makedirs(self.base_dir,   exist_ok=lowerCamelCase)
           _lowercase :							Any   	=	os.path.join(self.base_dir,   self.save_dir)
           _lowercase :							List[Any]   	=	self.batch_size_per_device * jax.device_count()






@dataclass
class _lowerCamelCase:
       lowercase_				:			int
       lowercase_				:			int									=    40_96  # no dynamic padding on TPUs
       def __call__(      self,   lowerCamelCase) ->     Tuple:




           """simple docstring"""

           _lowercase :							int   	=	self.collate_fn(lowerCamelCase)
           _lowercase :							Optional[Any]   	=	jax.tree_util.tree_map(lowerCamelCase,   lowerCamelCase)
           return batch
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase	,       _lowercase :							List[str]   	=	self.fetch_inputs(features['input_ids'])
           _lowercase :							List[Any]   	=	{
               '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     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Dict   	=	[self._fetch_inputs(lowerCamelCase) for ids in input_ids]
           return zip(*lowerCamelCase)




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     List[Any]:




           """simple docstring"""

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



@partial(jax.pmap							,				axis_name='batch'				)
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				**lowerCamelCase_				)					->		str:
    def loss_fn(lowerCamelCase_				):
        _lowercase :							Optional[int]   	=	model_inputs.pop('start_labels'				)
        _lowercase :							List[str]   	=	model_inputs.pop('end_labels'				)
        _lowercase :							str   	=	model_inputs.pop('pooled_labels'				)

        _lowercase :							Any   	=	state.apply_fn(**lowerCamelCase_							,				params=lowerCamelCase_							,				dropout_rng=lowerCamelCase_							,				train=lowerCamelCase_				)
        _lowercase	,       _lowercase	,       _lowercase :							str   	=	outputs

        return state.loss_fn(
            lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				)

    _lowercase	,       _lowercase :							str   	=	jax.random.split(lowerCamelCase_				)
    _lowercase :							List[str]   	=	jax.value_and_grad(lowerCamelCase_				)
    _lowercase	,       _lowercase :							List[str]   	=	grad_fn(state.params				)
    _lowercase :							Optional[Any]   	=	jax.lax.pmean({'loss': loss}							,				axis_name='batch'				)
    _lowercase :							List[str]   	=	jax.lax.pmean(lowerCamelCase_							,				'batch'				)

    _lowercase :							Tuple   	=	state.apply_gradients(grads=lowerCamelCase_				)
    return state, metrics, new_drp_rng



@partial(jax.pmap							,				axis_name='batch'				)
def        UpperCamelCase_(   lowerCamelCase_							,				**lowerCamelCase_				)					->		Dict:
    _lowercase :							Optional[int]   	=	model_inputs.pop('start_labels'				)
    _lowercase :							List[str]   	=	model_inputs.pop('end_labels'				)
    _lowercase :							Optional[int]   	=	model_inputs.pop('pooled_labels'				)

    _lowercase :							Any   	=	state.apply_fn(**lowerCamelCase_							,				params=state.params							,				train=lowerCamelCase_				)
    _lowercase	,       _lowercase	,       _lowercase :							List[Any]   	=	outputs

    _lowercase :							List[Any]   	=	state.loss_fn(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							Dict   	=	jax.lax.pmean({'loss': loss}							,				axis_name='batch'				)
    return metrics






class _lowerCamelCase(		train_state.TrainState     ):
       lowercase_				:			Callable									=    struct.field(pytree_node=_a     )






@dataclass
class _lowerCamelCase:
       lowercase_				:			Args
       lowercase_				:			Callable
       lowercase_				:			Callable
       lowercase_				:			Callable
       lowercase_				:			Callable
       lowercase_				:			wandb
       lowercase_				:			Callable									=    None
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase=None) ->     Dict:




           """simple docstring"""

           _lowercase :							List[str]   	=	model.params
           _lowercase :							Dict   	=	TrainState.create(
               apply_fn=model.__call__,   params=lowerCamelCase,   tx=lowerCamelCase,   loss_fn=lowerCamelCase,   )
           if ckpt_dir is not None:
               _lowercase	,       _lowercase	,       _lowercase	,       _lowercase	,       _lowercase :							int   	=	restore_checkpoint(lowerCamelCase,   lowerCamelCase)
               _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 :							Dict   	=	build_tx(**lowerCamelCase)
               _lowercase :							str   	=	train_state.TrainState(
                   step=lowerCamelCase,   apply_fn=model.__call__,   params=lowerCamelCase,   tx=lowerCamelCase,   opt_state=lowerCamelCase,   )
               _lowercase :							int   	=	args
               _lowercase :							Dict   	=	data_collator
               _lowercase :							List[Any]   	=	lr
               _lowercase :							List[Any]   	=	params
           _lowercase :							int   	=	jax_utils.replicate(lowerCamelCase)
           return state
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							int   	=	self.args
           _lowercase :							Tuple   	=	len(lowerCamelCase) // args.batch_size

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

                       _lowercase :							List[Any]   	=	self.evaluate(lowerCamelCase,   lowerCamelCase)
                       _lowercase :							int   	=	{
                           '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     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

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




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     int:




           """simple docstring"""

           _lowercase :							Tuple   	=	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        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_				)					->		Tuple:
    print(F'''RESTORING CHECKPOINT FROM {save_dir}'''							,				end=' ... '				)
    with open(os.path.join(lowerCamelCase_							,				'flax_model.msgpack'				)							,				'rb'				) as f:
        _lowercase :							Any   	=	from_bytes(state.params							,				f.read()				)

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

    _lowercase :							Optional[Any]   	=	joblib.load(os.path.join(lowerCamelCase_							,				'args.joblib'				)				)
    _lowercase :							Any   	=	joblib.load(os.path.join(lowerCamelCase_							,				'data_collator.joblib'				)				)

    with open(os.path.join(lowerCamelCase_							,				'training_state.json'				)							,				'r'				) as f:
        _lowercase :							List[Any]   	=	json.load(lowerCamelCase_				)
    _lowercase :							Any   	=	training_state['step']

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



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		Optional[int]:
    _lowercase :							int   	=	num_train_steps - warmup_steps
    _lowercase :							int   	=	optax.linear_schedule(init_value=lowerCamelCase_							,				end_value=lowerCamelCase_							,				transition_steps=lowerCamelCase_				)
    _lowercase :							Tuple   	=	optax.linear_schedule(init_value=lowerCamelCase_							,				end_value=1e-7							,				transition_steps=lowerCamelCase_				)
    _lowercase :							Union[str, Any]   	=	optax.join_schedules(schedules=[warmup_fn, decay_fn]							,				boundaries=[warmup_steps]				)
    return lr



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

    _lowercase :							int   	=	scheduler_fn(lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)

    _lowercase :							Tuple   	=	optax.adamw(learning_rate=lowerCamelCase_							,				weight_decay=lowerCamelCase_							,				mask=lowerCamelCase_				)
    return tx, lr






from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE :   Tuple							      =				"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           return datasets.MetricInfo(
               description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
                   {
                       'predictions': datasets.Value('string',   id='sequence'),
                       'references': datasets.Value('string',   id='sequence'),
                   }),   codebase_urls=['https://github.com/jitsi/jiwer/'],   reference_urls=[
                   'https://en.wikipedia.org/wiki/Word_error_rate',
               ],   )




       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


