
'''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 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						42
		lowerCamelCase     							=						jnp.floataa
		lowerCamelCase     							=						True



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Optional[int]:






				"""simple docstring"""







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



		def __call__(      self				:			int  ,	*UpperCamelCase__				:			int  ,	**UpperCamelCase__				:			Any      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						super().__call__(*UpperCamelCase__  ,	**UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						self.cls(outputs[2]      )
				return outputs[:2] + (cls_out,)







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						FlaxBigBirdForNaturalQuestionsModule







def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


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

		snake_case  :       str     			=						partial(SCREAMING_SNAKE_CASE__ ,       reduction=jnp.mean			)
		snake_case  :       Union[str, Any]     			=						cross_entropy(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		snake_case  :       Optional[int]     			=						cross_entropy(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		snake_case  :       Union[str, Any]     			=						cross_entropy(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
		return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class 	snake_case__       :







		"""simple docstring"""

		lowerCamelCase     							=						"google/bigbird-roberta-base"
		lowerCamelCase     							=						3000
		lowerCamelCase     							=						10500

		lowerCamelCase     							=						128
		lowerCamelCase     							=						3

		lowerCamelCase     							=						1
		lowerCamelCase     							=						5

		# tx_args
		lowerCamelCase     							=						3E-5
		lowerCamelCase     							=						0.0
		lowerCamelCase     							=						20000
		lowerCamelCase     							=						0.0_0_9_5

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



		def       lowerCAmelCase				(      self				:			str      )						->			Tuple:






				"""simple docstring"""







				os.makedirs(self.base_dir  ,	exist_ok=UpperCamelCase__      )
				snake_case  :       Tuple     			=						os.path.join(self.base_dir  ,	self.save_dir      )
				snake_case  :       Union[str, Any]     			=						self.batch_size_per_device * jax.device_count()







@dataclass
class 	snake_case__       :







		"""simple docstring"""

		lowerCamelCase     							=						42
		lowerCamelCase     							=						4096  # no dynamic padding on TPUs



		def __call__(      self				:			Any  ,	UpperCamelCase__				:			str      )						->			str:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						self.collate_fn(UpperCamelCase__      )
				snake_case  :       str     			=						jax.tree_util.tree_map(UpperCamelCase__  ,	UpperCamelCase__      )
				return batch



		def       lowerCAmelCase				(      self				:			Any  ,	UpperCamelCase__				:			Optional[int]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						self.fetch_inputs(features['''input_ids''']      )
				snake_case  :       Any     			=						{
				    '''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       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			list      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       int     			=						[self._fetch_inputs(UpperCamelCase__      ) for ids in input_ids]
				return zip(*UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			list      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						[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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							List[Any]:


		'''simple docstring'''


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




@partial(jax.pmap ,       axis_name='''batch'''			)
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       **SCREAMING_SNAKE_CASE__			)  ->							Union[str, Any]:


		'''simple docstring'''


		def loss_fn(SCREAMING_SNAKE_CASE__			):
				snake_case  :       Union[str, Any]     			=						model_inputs.pop('''start_labels'''			)
				snake_case  :       Optional[int]     			=						model_inputs.pop('''end_labels'''			)
				snake_case  :       Tuple     			=						model_inputs.pop('''pooled_labels'''			)

				snake_case  :       Optional[Any]     			=						state.apply_fn(**SCREAMING_SNAKE_CASE__ ,       params=SCREAMING_SNAKE_CASE__ ,       dropout_rng=SCREAMING_SNAKE_CASE__ ,       train=SCREAMING_SNAKE_CASE__			)
				snake_case			,snake_case			,snake_case  :       Dict     			=						outputs

				return state.loss_fn(
				    SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       )

		snake_case			,snake_case  :       Optional[Any]     			=						jax.random.split(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Tuple     			=						jax.value_and_grad(SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case  :       Dict     			=						grad_fn(state.params			)
		snake_case  :       Tuple     			=						jax.lax.pmean({'''loss''': loss} ,       axis_name='''batch'''			)
		snake_case  :       Dict     			=						jax.lax.pmean(SCREAMING_SNAKE_CASE__ ,       '''batch'''			)

		snake_case  :       Dict     			=						state.apply_gradients(grads=SCREAMING_SNAKE_CASE__			)
		return state, metrics, new_drp_rng




@partial(jax.pmap ,       axis_name='''batch'''			)
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       **SCREAMING_SNAKE_CASE__			)  ->							str:


		'''simple docstring'''


		snake_case  :       Any     			=						model_inputs.pop('''start_labels'''			)
		snake_case  :       str     			=						model_inputs.pop('''end_labels'''			)
		snake_case  :       Tuple     			=						model_inputs.pop('''pooled_labels'''			)

		snake_case  :       List[Any]     			=						state.apply_fn(**SCREAMING_SNAKE_CASE__ ,       params=state.params ,       train=SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case			,snake_case  :       Union[str, Any]     			=						outputs

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






class 	snake_case__       (      train_state.TrainState					):







		"""simple docstring"""

		lowerCamelCase     							=						struct.field(pytree_node=__SCREAMING_SNAKE_CASE					)







@dataclass
class 	snake_case__       :







		"""simple docstring"""

		lowerCamelCase     							=						42
		lowerCamelCase     							=						42
		lowerCamelCase     							=						42
		lowerCamelCase     							=						42
		lowerCamelCase     							=						42
		lowerCamelCase     							=						42
		lowerCamelCase     							=						None



		def       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]=None      )						->			Any:






				"""simple docstring"""







				snake_case  :       Tuple     			=						model.params
				snake_case  :       int     			=						TrainState.create(
				    apply_fn=model.__call__  ,	params=UpperCamelCase__  ,	tx=UpperCamelCase__  ,	loss_fn=UpperCamelCase__  ,	)
				if ckpt_dir is not None:
						snake_case			,snake_case			,snake_case			,snake_case			,snake_case  :       Optional[int]     			=						restore_checkpoint(UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       List[Any]     			=						{
						    '''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  :       List[str]     			=						build_tx(**UpperCamelCase__      )
						snake_case  :       List[Any]     			=						train_state.TrainState(
						    step=UpperCamelCase__  ,	apply_fn=model.__call__  ,	params=UpperCamelCase__  ,	tx=UpperCamelCase__  ,	opt_state=UpperCamelCase__  ,	)
						snake_case  :       Tuple     			=						args
						snake_case  :       Any     			=						data_collator
						snake_case  :       Optional[int]     			=						lr
						snake_case  :       str     			=						params
				snake_case  :       str     			=						jax_utils.replicate(UpperCamelCase__      )
				return state



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Optional[int]  ,	UpperCamelCase__				:			Any  ,	UpperCamelCase__				:			Any      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.args
				snake_case  :       List[str]     			=						len(UpperCamelCase__      ) // args.batch_size

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

										snake_case  :       Optional[Any]     			=						self.evaluate(UpperCamelCase__  ,	UpperCamelCase__      )
										snake_case  :       Optional[Any]     			=						{
										    '''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       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Dict  ,	UpperCamelCase__				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			List[Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


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

		with open(os.path.join(SCREAMING_SNAKE_CASE__ ,       '''opt_state.msgpack'''			) ,       '''rb'''			) as f:
				snake_case  :       List[Any]     			=						from_bytes(state.opt_state ,       f.read()			)

		snake_case  :       int     			=						joblib.load(os.path.join(SCREAMING_SNAKE_CASE__ ,       '''args.joblib'''			)			)
		snake_case  :       Tuple     			=						joblib.load(os.path.join(SCREAMING_SNAKE_CASE__ ,       '''data_collator.joblib'''			)			)

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

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Tuple:


		'''simple docstring'''


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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							List[Any]:


		'''simple docstring'''


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

		snake_case  :       Optional[int]     			=						scheduler_fn(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		snake_case  :       Union[str, Any]     			=						optax.adamw(learning_rate=SCREAMING_SNAKE_CASE__ ,       weight_decay=SCREAMING_SNAKE_CASE__ ,       mask=SCREAMING_SNAKE_CASE__			)
		return tx, lr




'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
						from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
						lowercase__						       =      get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
						from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowercase__						       =      1_2_8_0_2_2
lowercase__						       =      1_2_8_0_2_8






@require_sentencepiece
class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						MaMaaaTokenizer
		lowerCamelCase     							=						False
		lowerCamelCase     							=						False
		lowerCamelCase     							=						True



		def       lowerCAmelCase				(      self				:			Dict      )						->			Optional[Any]:






				"""simple docstring"""







				super().setUp()

				snake_case  :       int     			=						['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
				snake_case  :       int     			=						dict(zip(UpperCamelCase__  ,	range(len(UpperCamelCase__      )      )      )      )
				snake_case  :       Optional[int]     			=						Path(self.tmpdirname      )
				save_json(UpperCamelCase__  ,	save_dir / VOCAB_FILES_NAMES['''vocab_file''']      )
				if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
						copyfile(UpperCamelCase__  ,	save_dir / VOCAB_FILES_NAMES['''spm_file''']      )

				snake_case  :       Optional[int]     			=						MaMaaaTokenizer.from_pretrained(self.tmpdirname      )
				tokenizer.save_pretrained(self.tmpdirname      )



		def       lowerCAmelCase				(      self				:			Dict  ,	**UpperCamelCase__				:			Optional[int]      )						->			Any:






				"""simple docstring"""







				return MaMaaaTokenizer.from_pretrained(self.tmpdirname  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Tuple      )						->			str:






				"""simple docstring"""







				return (
				    "This is a test",
				    "This is a test",
				)



		def       lowerCAmelCase				(      self				:			List[str]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						'''</s>'''
				snake_case  :       str     			=						0

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



		def       lowerCAmelCase				(      self				:			Dict      )						->			int:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self.get_tokenizer()
				snake_case  :       Dict     			=						list(tokenizer.get_vocab().keys()      )

				self.assertEqual(vocab_keys[0]  ,	'''</s>'''      )
				self.assertEqual(vocab_keys[1]  ,	'''<unk>'''      )
				self.assertEqual(vocab_keys[-1]  ,	'''<s>'''      )
				self.assertEqual(len(UpperCamelCase__      )  ,	tokenizer.vocab_size + len(tokenizer.get_added_vocab()      )      )



		@unittest.skip('''Skip this test while all models are still to be uploaded.'''      )
		def       lowerCAmelCase				(      self				:			str      )						->			List[str]:






				"""simple docstring"""







				pass



		def       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self.get_tokenizer()

				snake_case  :       Optional[Any]     			=						tokenizer.tokenize('''This is a test'''      )
				self.assertListEqual(UpperCamelCase__  ,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(UpperCamelCase__      )  ,	[2, 3, 4, 5, 6]  ,	)

				snake_case  :       Any     			=						tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]      )
				self.assertListEqual(UpperCamelCase__  ,	['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

				snake_case  :       Optional[int]     			=						tokenizer.convert_tokens_to_string(UpperCamelCase__      )
				self.assertEqual(UpperCamelCase__  ,	'''This is a test'''      )



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						{'''input_ids''': [[12_8022, 11_0108, 397, 11, 3_8272, 2247, 12_4811, 285, 1_8105, 1586, 207, 7, 3_9534, 4428, 397, 1019, 1_8105, 1586, 207, 7, 4_1337, 1_6786, 241, 7, 2_0214, 17, 12_5690, 1_0398, 7, 4_4378, 5_8069, 6_8342, 7798, 7343, 11, 299, 3_3310, 4, 158, 3_7350, 9_4077, 4569, 299, 3_3310, 90, 4, 5_2840, 290, 4, 3_1270, 112, 299, 682, 4, 5_2840, 3_9953, 1_4079, 193, 5_2519, 9_0894, 1_7894, 12_0697, 11, 4_0445, 551, 17, 1019, 5_2519, 9_0894, 1_7756, 963, 11, 4_0445, 480, 17, 9792, 1120, 5173, 1393, 6240, 1_6786, 241, 12_0996, 28, 1245, 1393, 11_8240, 1_1123, 1019, 9_3612, 2691, 1_0618, 9_8058, 12_0409, 1928, 279, 4, 4_0683, 367, 178, 207, 1019, 103, 10_3121, 506, 6_5296, 5, 2], [12_8022, 2_1217, 367, 117, 12_5450, 128, 719, 7, 7308, 40, 9_3612, 1_2669, 1116, 1_6704, 71, 1_7785, 3699, 1_5592, 35, 144, 9584, 241, 1_1943, 713, 950, 799, 2247, 8_8427, 150, 149, 11_8813, 12_0706, 1019, 10_6906, 8_1518, 28, 1224, 2_2799, 397, 5, 2, 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], [12_8022, 1658, 12_3311, 5155, 5578, 4722, 279, 1_4947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 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]], '''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, 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, 0, 0, 0, 0], [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, 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=UpperCamelCase__  ,	model_name='''facebook/m2m100_418M'''  ,	revision='''c168bae485c864188cf9aa0e4108b0b6934dc91e'''  ,	)







@require_torch
@require_sentencepiece
@require_tokenizers
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						"""facebook/m2m100_418M"""
		lowerCamelCase     							=						[
		    """In my opinion, there are two levels of response from the French government.""",
		    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
		]
		lowerCamelCase     							=						[
		    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
		    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
		]

		# fmt: off
		lowerCamelCase     							=						[EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]



		@classmethod
		def       lowerCAmelCase				(      cls				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case  :       MaMaaaTokenizer     			=						MaMaaaTokenizer.from_pretrained(
				    cls.checkpoint_name  ,	src_lang='''en'''  ,	tgt_lang='''fr'''      )
				snake_case  :       List[str]     			=						1
				return cls



		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				self.assertEqual(self.tokenizer.get_lang_id('''ar'''      )  ,	12_8006      )
				self.assertEqual(self.tokenizer.get_lang_id('''en'''      )  ,	12_8022      )
				self.assertEqual(self.tokenizer.get_lang_id('''ro'''      )  ,	12_8076      )
				self.assertEqual(self.tokenizer.get_lang_id('''mr'''      )  ,	12_8063      )



		def       lowerCAmelCase				(      self				:			Any      )						->			Dict:






				"""simple docstring"""







				snake_case  :       List[str]     			=						self.tokenizer.get_vocab()
				self.assertEqual(len(UpperCamelCase__      )  ,	self.tokenizer.vocab_size      )
				self.assertEqual(vocab['''<unk>''']  ,	3      )
				self.assertIn(self.tokenizer.get_lang_token('''en'''      )  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       Dict     			=						'''en'''
				snake_case  :       Dict     			=						self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
				self.assertListEqual(self.expected_src_tokens  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				self.assertIn(UpperCamelCase__  ,	self.tokenizer.all_special_ids      )
				# fmt: off
				snake_case  :       str     			=						[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 1_4028, 136, 3286, 9706, 6, 9_0797, 6, 14_4012, 162, 8_8128, 3_0061, 5, 2]
				# fmt: on
				snake_case  :       int     			=						self.tokenizer.decode(UpperCamelCase__  ,	skip_special_tokens=UpperCamelCase__      )
				snake_case  :       List[Any]     			=						self.tokenizer.decode(generated_ids[1:]  ,	skip_special_tokens=UpperCamelCase__      )
				self.assertEqual(UpperCamelCase__  ,	UpperCamelCase__      )
				self.assertNotIn(self.tokenizer.eos_token  ,	UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Tuple:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						tempfile.mkdtemp()
				snake_case  :       Union[str, Any]     			=						self.tokenizer.lang_token_to_id
				self.tokenizer.save_pretrained(UpperCamelCase__      )
				snake_case  :       Optional[int]     			=						MaMaaaTokenizer.from_pretrained(UpperCamelCase__      )
				self.assertDictEqual(new_tok.lang_token_to_id  ,	UpperCamelCase__      )



		@require_torch
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						'''en'''
				snake_case  :       int     			=						'''fr'''

				snake_case  :       Dict     			=						self.tokenizer(self.src_text  ,	text_target=self.tgt_text  ,	padding=UpperCamelCase__  ,	return_tensors='''pt'''      )

				snake_case  :       List[str]     			=						shift_tokens_right(
				    batch['''labels''']  ,	self.tokenizer.pad_token_id  ,	self.tokenizer.eos_token_id      )

				for k in batch:
						snake_case  :       Tuple     			=						batch[k].tolist()
				# batch = {k: v.tolist() for k,v in batch.items()}
				# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
				# batch.decoder_inputs_ids[0][0] ==
				assert batch.input_ids[1][0] == EN_CODE
				assert batch.input_ids[1][-1] == 2
				assert batch.labels[1][0] == FR_CODE
				assert batch.labels[1][-1] == 2
				assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]



		@require_torch
		def       lowerCAmelCase				(      self				:			int      )						->			Union[str, Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						'''mr'''
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id('''mr'''      )]      )
				self.assertListEqual(self.tokenizer.suffix_tokens  ,	[self.tokenizer.eos_token_id]      )

				snake_case  :       Optional[int]     			=						'''zh'''
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id('''zh'''      )]      )
				self.assertListEqual(self.tokenizer.suffix_tokens  ,	[self.tokenizer.eos_token_id]      )



		@require_torch
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			Dict:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						'''mr'''
				self.tokenizer._switch_to_target_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id('''mr'''      )]      )
				self.assertListEqual(self.tokenizer.suffix_tokens  ,	[self.tokenizer.eos_token_id]      )
				self.tokenizer._switch_to_input_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang      )]      )

				snake_case  :       List[str]     			=						'''zh'''
				self.tokenizer._switch_to_target_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id('''zh'''      )]      )
				self.assertListEqual(self.tokenizer.suffix_tokens  ,	[self.tokenizer.eos_token_id]      )
				self.tokenizer._switch_to_input_mode()
				self.assertListEqual(self.tokenizer.prefix_tokens  ,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang      )]      )



		@require_torch
		def       lowerCAmelCase				(      self				:			Tuple      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						self.tokenizer._build_translation_inputs('''A test'''  ,	return_tensors='''pt'''  ,	src_lang='''en'''  ,	tgt_lang='''ar'''      )

				self.assertEqual(
				    nested_simplify(UpperCamelCase__      )  ,	{
				        # en_XX, A, test, EOS
				        '''input_ids''': [[12_8022, 58, 4183, 2]],
				        '''attention_mask''': [[1, 1, 1, 1]],
				        # ar_AR
				        '''forced_bos_token_id''': 12_8006,
				    }  ,	)


