"""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      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    BigBirdConfig
      lowerCamelCase__		:    jnp.dtype   =  jnp.floataa
      lowerCamelCase__		:    bool   =  True


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             super().setup()
             lowercase__			:		Optional[int]       =	nn.Dense(5		,			dtype=self.dtype							)


      def __call__(   self		,			*a		,			**a							)       ->					Union[str, Any]:
             lowercase__			:		List[str]       =	super().__call__(*a		,			**a							)
             lowercase__			:		str       =	self.cls(outputs[2]							)
             return outputs[:2] + (cls_out,)





class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Optional[int]   =  FlaxBigBirdForNaturalQuestionsModule





def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : str     ):





       '''simple docstring'''

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

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


@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    str   =  "google/bigbird-roberta-base"
      lowerCamelCase__		:    int   =  3_0_0_0
      lowerCamelCase__		:    int   =  1_0_5_0_0

      lowerCamelCase__		:    int   =  1_2_8
      lowerCamelCase__		:    int   =  3

      lowerCamelCase__		:    int   =  1
      lowerCamelCase__		:    int   =  5

      # tx_args
      lowerCamelCase__		:    float   =  3E-5
      lowerCamelCase__		:    float   =  0.0
      lowerCamelCase__		:    int   =  2_0_0_0_0
      lowerCamelCase__		:    float   =  0.0095

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


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             os.makedirs(self.base_dir		,			exist_ok=a							)
             lowercase__			:		Tuple       =	os.path.join(self.base_dir		,			self.save_dir							)
             lowercase__			:		int       =	self.batch_size_per_device * jax.device_count()





@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    int
      lowerCamelCase__		:    int   =  4_0_9_6  # no dynamic padding on TPUs


      def __call__(   self		,			a							)       ->					List[str]:
             lowercase__			:		Any       =	self.collate_fn(a							)
             lowercase__			:		Dict       =	jax.tree_util.tree_map(a		,			a							)
             return batch


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Union[str, Any]:
             lowercase__    ,      lowercase__			:		Any       =	self.fetch_inputs(features['input_ids']							)
             lowercase__			:		Optional[Any]       =	{
                 '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		,			a							)       ->					List[str]:
             lowercase__			:		List[Any]       =	[self._fetch_inputs(a							) for ids in input_ids]
             return zip(*a							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Tuple:
             lowercase__			:		Any       =	[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_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : Union[str, Any]=None     ):





       '''simple docstring'''

       if seed is not None:
              lowercase__			:		Tuple       =	dataset.shuffle(seed=_lowerCAmelCase     )
       for i in range(len(_lowerCAmelCase     ) // batch_size     ):
              lowercase__			:		Optional[int]       =	dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(_lowerCAmelCase     )





@partial(jax.pmap   , axis_name='batch'     )
def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Union[str, Any]   , **_lowerCAmelCase  : Optional[int]     ):





       '''simple docstring'''

       def loss_fn(_lowerCAmelCase  : Union[str, Any]     ):
              lowercase__			:		Any       =	model_inputs.pop('start_labels'     )
              lowercase__			:		List[str]       =	model_inputs.pop('end_labels'     )
              lowercase__			:		Any       =	model_inputs.pop('pooled_labels'     )

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

              return state.loss_fn(
                  _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , )

       lowercase__    ,      lowercase__			:		Dict       =	jax.random.split(_lowerCAmelCase     )
       lowercase__			:		Dict       =	jax.value_and_grad(_lowerCAmelCase     )
       lowercase__    ,      lowercase__			:		Dict       =	grad_fn(state.params     )
       lowercase__			:		Optional[Any]       =	jax.lax.pmean({'loss': loss}   , axis_name='batch'     )
       lowercase__			:		Tuple       =	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 				a_     (      _lowerCAmelCase  : Tuple   , **_lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

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

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

       lowercase__			:		Optional[Any]       =	state.loss_fn(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
       lowercase__			:		int       =	jax.lax.pmean({'loss': loss}   , axis_name='batch'     )
       return metrics


class      UpperCAmelCase_	(    train_state.TrainState):
      lowerCamelCase__		:    Callable   =  struct.field(pytree_node=_a)





@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    Args
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    wandb
      lowerCamelCase__		:    Callable   =  None


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


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Optional[int]:
             lowercase__			:		str       =	self.args
             lowercase__			:		str       =	len(a							) // args.batch_size

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

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


      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					List[Any]:
             lowercase__			:		int       =	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_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Any     ):





       '''simple docstring'''

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

       with open(os.path.join(_lowerCAmelCase   , 'opt_state.msgpack'     )   , 'rb'     ) as f:
              lowercase__			:		List[str]       =	from_bytes(state.opt_state   , f.read()     )

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

       with open(os.path.join(_lowerCAmelCase   , 'training_state.json'     )   , 'r'     ) as f:
              lowercase__			:		Union[str, Any]       =	json.load(_lowerCAmelCase     )
       lowercase__			:		Tuple       =	training_state['step']

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





def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : Any     ):





       '''simple docstring'''

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






def 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

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

       lowercase__			:		List[str]       =	scheduler_fn(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )

       lowercase__			:		List[Any]       =	optax.adamw(learning_rate=_lowerCAmelCase   , weight_decay=_lowerCAmelCase   , mask=_lowerCAmelCase     )
       return tx, lr





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase											=       '▁'

lowerCamelCase											=       get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Tuple      =BigBirdTokenizer
      UpperCamelCase__							:  Union[str, Any]      =BigBirdTokenizerFast
      UpperCamelCase__							:  Any      =True
      UpperCamelCase__							:  Optional[Any]      =True






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             super().setUp()

             _lowerCamelCase       :		List[Any]		=self.tokenizer_class(lowercase_			,   keep_accents=lowercase_ )
             tokenizer.save_pretrained(self.tmpdirname )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		='<s>'
             _lowerCamelCase       :		Optional[Any]		=1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ )			,   lowercase_ )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ )			,   lowercase_ )






      def        lowerCamelCase       (					self						:     int )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]			,   '<unk>' )
             self.assertEqual(vocab_keys[1]			,   '<s>' )
             self.assertEqual(vocab_keys[-1]			,   '[MASK]' )
             self.assertEqual(len(lowercase_ )			,   1004 )






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             self.assertEqual(self.get_tokenizer().vocab_size			,   1000 )






      def        lowerCamelCase       (					self						:     Any )					->						Dict:
             """simple docstring"""




             if not self.test_rust_tokenizer:
                    return

             _lowerCamelCase       :		Union[str, Any]		=self.get_tokenizer()
             _lowerCamelCase       :		int		=self.get_rust_tokenizer()

             _lowerCamelCase       :		int		='I was born in 92000, and this is falsé.'

             _lowerCamelCase       :		int		=tokenizer.tokenize(lowercase_ )
             _lowerCamelCase       :		List[Any]		=rust_tokenizer.tokenize(lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		Any		=tokenizer.encode(lowercase_			,   add_special_tokens=lowercase_ )
             _lowerCamelCase       :		str		=rust_tokenizer.encode(lowercase_			,   add_special_tokens=lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		str		=self.get_rust_tokenizer()
             _lowerCamelCase       :		Union[str, Any]		=tokenizer.encode(lowercase_ )
             _lowerCamelCase       :		List[Any]		=rust_tokenizer.encode(lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=BigBirdTokenizer(lowercase_			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		int		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(lowercase_ )			,   [285, 46, 10, 170, 382]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Any		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,   )

             _lowerCamelCase       :		Optional[int]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]			,   )






      @cached_property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )






      @slow
      def        lowerCamelCase       (					self						:     Any )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		='Hello World!'
             _lowerCamelCase       :		Tuple		=[65, 1_8536, 2260, 101, 66]

             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @slow
      def        lowerCamelCase       (					self						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             # fmt: off
             _lowerCamelCase       :		Tuple		=[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
             # fmt: on
             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @require_torch
      @slow
      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             import torch

             from transformers import BigBirdConfig, BigBirdModel

             # Build sequence
             _lowerCamelCase       :		Union[str, Any]		=list(self.big_tokenizer.get_vocab().keys() )[:10]
             _lowerCamelCase       :		List[Any]		=' '.join(lowercase_ )
             _lowerCamelCase       :		List[str]		=self.big_tokenizer.encode_plus(lowercase_			,   return_tensors='pt'			,   return_token_type_ids=lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.big_tokenizer.batch_encode_plus(
                 [sequence + ' ' + sequence]			,   return_tensors='pt'			,   return_token_type_ids=lowercase_ )

             _lowerCamelCase       :		List[str]		=BigBirdConfig(attention_type='original_full' )
             _lowerCamelCase       :		Optional[Any]		=BigBirdModel(lowercase_ )

             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                    model(**lowercase_ )
                    model(**lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
             _lowerCamelCase       :		int		=tokenizer.decode(tokenizer('Paris is the [MASK].' ).input_ids )

             self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]' )






      @slow
      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={'input_ids': [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase_			,   model_name='google/bigbird-roberta-base'			,   revision='215c99f1600e06f83acce68422f2035b2b5c3510'			,   )


