




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						__magic_name__     :     Dict  							=					'platform'

						import jax
						import jax.numpy as jnp

						from transformers import MarianTokenizer
						from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=None      ,   __lowerCAmelCase=None      ,   ):
		if attention_mask is None:
				lowerCAmelCase__								=			np.where(input_ids != config.pad_token_id      ,   1      ,   0							)
		if decoder_attention_mask is None:
				lowerCAmelCase__								=			np.where(decoder_input_ids != config.pad_token_id      ,   1      ,   0							)
		if head_mask is None:
				lowerCAmelCase__								=			np.ones((config.encoder_layers, config.encoder_attention_heads)							)
		if decoder_head_mask is None:
				lowerCAmelCase__								=			np.ones((config.decoder_layers, config.decoder_attention_heads)							)
		if cross_attn_head_mask is None:
				lowerCAmelCase__								=			np.ones((config.decoder_layers, config.decoder_attention_heads)							)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": attention_mask,
		}





class 						SCREAMING_SNAKE_CASE__		:
					def __init__(	self  :		Union[str, Any]					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		List[str]=13					,	__lowerCamelCase  :		List[Any]=7					,	__lowerCamelCase  :		str=True					,	__lowerCamelCase  :		List[str]=False					,	__lowerCamelCase  :		Optional[int]=99					,	__lowerCamelCase  :		int=16					,	__lowerCamelCase  :		int=2					,	__lowerCamelCase  :		Optional[Any]=4					,	__lowerCamelCase  :		Optional[int]=4					,	__lowerCamelCase  :		Union[str, Any]="gelu"					,	__lowerCamelCase  :		Optional[int]=0.1					,	__lowerCamelCase  :		Any=0.1					,	__lowerCamelCase  :		str=32					,	__lowerCamelCase  :		Union[str, Any]=2					,	__lowerCamelCase  :		List[str]=1					,	__lowerCamelCase  :		List[str]=0					,	__lowerCamelCase  :		Optional[Any]=0.02					,	):



							"""simple docstring"""
							lowerCAmelCase__								=			parent
							lowerCAmelCase__								=			batch_size
							lowerCAmelCase__								=			seq_length
							lowerCAmelCase__								=			is_training
							lowerCAmelCase__								=			use_labels
							lowerCAmelCase__								=			vocab_size
							lowerCAmelCase__								=			hidden_size
							lowerCAmelCase__								=			num_hidden_layers
							lowerCAmelCase__								=			num_attention_heads
							lowerCAmelCase__								=			intermediate_size
							lowerCAmelCase__								=			hidden_act
							lowerCAmelCase__								=			hidden_dropout_prob
							lowerCAmelCase__								=			attention_probs_dropout_prob
							lowerCAmelCase__								=			max_position_embeddings
							lowerCAmelCase__								=			eos_token_id
							lowerCAmelCase__								=			pad_token_id
							lowerCAmelCase__								=			bos_token_id
							lowerCAmelCase__								=			initializer_range
					def 					A__			(	self  :		int	):



							"""simple docstring"""
							lowerCAmelCase__								=			np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,	self.vocab_size	)					,	3					,	self.vocab_size	)
							lowerCAmelCase__								=			np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,	dtype=np.intaa	))					,	-1	)

							lowerCAmelCase__								=			shift_tokens_right(__a					,	1					,	2	)

							lowerCAmelCase__								=			MarianConfig(
							    vocab_size=self.vocab_size					,	d_model=self.hidden_size					,	encoder_layers=self.num_hidden_layers					,	decoder_layers=self.num_hidden_layers					,	encoder_attention_heads=self.num_attention_heads					,	decoder_attention_heads=self.num_attention_heads					,	encoder_ffn_dim=self.intermediate_size					,	decoder_ffn_dim=self.intermediate_size					,	dropout=self.hidden_dropout_prob					,	attention_dropout=self.attention_probs_dropout_prob					,	max_position_embeddings=self.max_position_embeddings					,	eos_token_id=self.eos_token_id					,	bos_token_id=self.bos_token_id					,	pad_token_id=self.pad_token_id					,	initializer_range=self.initializer_range					,	use_cache=__a					,	)
							lowerCAmelCase__								=			prepare_marian_inputs_dict(__a					,	__a					,	__a	)
							return config, inputs_dict
					def 					A__			(	self  :		Optional[int]	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.prepare_config_and_inputs()
							return config, inputs_dict
					def 					A__			(	self  :		Optional[int]					,	__lowerCamelCase  :		List[str]					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Dict	):



							"""simple docstring"""
							lowerCAmelCase__								=			20
							lowerCAmelCase__								=			model_class_name(__a	)

							lowerCAmelCase__								=			model.encode(inputs_dict['''input_ids''']	)

							lowerCAmelCase__								=			(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							lowerCAmelCase__								=			model.init_cache(decoder_input_ids.shape[0]					,	__a					,	__a	)
							lowerCAmelCase__								=			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,	dtype='''i4'''	)

							lowerCAmelCase__								=			jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]					,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,	)
							lowerCAmelCase__								=			model.decode(
							    decoder_input_ids[:, :-1]					,	__a					,	decoder_attention_mask=__a					,	past_key_values=__a					,	decoder_position_ids=__a					,	)

							lowerCAmelCase__								=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,	dtype='''i4'''	)
							lowerCAmelCase__								=			model.decode(
							    decoder_input_ids[:, -1:]					,	__a					,	decoder_attention_mask=__a					,	past_key_values=outputs_cache.past_key_values					,	decoder_position_ids=__a					,	)

							lowerCAmelCase__								=			model.decode(__a					,	__a	)

							lowerCAmelCase__								=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
							self.parent.assertTrue(diff < 1e-3					,	msg=F"""Max diff is {diff}"""	)



					def 					A__			(	self  :		int					,	__lowerCamelCase  :		Dict					,	__lowerCamelCase  :		List[Any]					,	__lowerCamelCase  :		List[str]	):



							"""simple docstring"""
							lowerCAmelCase__								=			20
							lowerCAmelCase__								=			model_class_name(__a	)

							lowerCAmelCase__								=			model.encode(inputs_dict['''input_ids''']	)

							lowerCAmelCase__								=			(
							    inputs_dict['decoder_input_ids'],
							    inputs_dict['decoder_attention_mask'],
							)

							lowerCAmelCase__								=			jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
							    ]					,	axis=-1					,	)

							lowerCAmelCase__								=			model.init_cache(decoder_input_ids.shape[0]					,	__a					,	__a	)
							lowerCAmelCase__								=			jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]					,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,	)

							lowerCAmelCase__								=			model.decode(
							    decoder_input_ids[:, :-1]					,	__a					,	decoder_attention_mask=__a					,	past_key_values=__a					,	decoder_position_ids=__a					,	)
							lowerCAmelCase__								=			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,	dtype='''i4'''	)
							lowerCAmelCase__								=			model.decode(
							    decoder_input_ids[:, -1:]					,	__a					,	past_key_values=outputs_cache.past_key_values					,	decoder_attention_mask=__a					,	decoder_position_ids=__a					,	)

							lowerCAmelCase__								=			model.decode(__a					,	__a					,	decoder_attention_mask=__a	)

							lowerCAmelCase__								=			np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
							self.parent.assertTrue(diff < 1e-3					,	msg=F"""Max diff is {diff}"""	)





@require_flax
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_						,				unittest.TestCase						,				lowerCAmelCase_ ):
					lowercase_   :			List[Any]   =   True
					lowercase_   :			Dict   =   (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					lowercase_   :			Any   =   (FlaxMarianMTModel,) if is_flax_available() else ()
					def 					A__			(	self  :		Optional[Any]	):



							"""simple docstring"""
							lowerCAmelCase__								=			FlaxMarianModelTester(self	)
					def 					A__			(	self  :		List[str]	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward(__a					,	__a					,	__a	)
					def 					A__			(	self  :		str	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
									self.model_tester.check_use_cache_forward_with_attn_mask(__a					,	__a					,	__a	)
					def 					A__			(	self  :		int	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__	):
											lowerCAmelCase__								=			self._prepare_for_class(__a					,	__a	)
											lowerCAmelCase__								=			model_class(__a	)

											@jax.jit
											def encode_jitted(__lowerCamelCase  :		Any					,	__lowerCamelCase  :		str=None					,	**__lowerCamelCase  :		str	):
													return model.encode(input_ids=__a					,	attention_mask=__a	)

											with self.subTest('''JIT Enabled'''	):
													lowerCAmelCase__								=			encode_jitted(**__a	).to_tuple()

											with self.subTest('''JIT Disabled'''	):
													with jax.disable_jit():
															lowerCAmelCase__								=			encode_jitted(**__a	).to_tuple()

											self.assertEqual(len(__a	)					,	len(__a	)	)
											for jitted_output, output in zip(__a					,	__a	):
													self.assertEqual(jitted_output.shape					,	output.shape	)
					def 					A__			(	self  :		Optional[int]	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__	):
											lowerCAmelCase__								=			model_class(__a	)
											lowerCAmelCase__								=			model.encode(inputs_dict['''input_ids''']					,	inputs_dict['''attention_mask''']	)

											lowerCAmelCase__								=			{
											    'decoder_input_ids': inputs_dict['decoder_input_ids'],
											    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
											    'encoder_outputs': encoder_outputs,
											}

											@jax.jit
											def decode_jitted(__lowerCamelCase  :		Any					,	__lowerCamelCase  :		List[Any]					,	__lowerCamelCase  :		Optional[Any]	):
													return model.decode(
													    decoder_input_ids=__a					,	decoder_attention_mask=__a					,	encoder_outputs=__a					,	)

											with self.subTest('''JIT Enabled'''	):
													lowerCAmelCase__								=			decode_jitted(**__a	).to_tuple()

											with self.subTest('''JIT Disabled'''	):
													with jax.disable_jit():
															lowerCAmelCase__								=			decode_jitted(**__a	).to_tuple()

											self.assertEqual(len(__a	)					,	len(__a	)	)
											for jitted_output, output in zip(__a					,	__a	):
													self.assertEqual(jitted_output.shape					,	output.shape	)



					@slow
					def 					A__			(	self  :		List[str]	):



							"""simple docstring"""





							for model_class_name in self.all_model_classes:
									lowerCAmelCase__								=			model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''	)
									# FlaxMarianForSequenceClassification expects eos token in input_ids
									lowerCAmelCase__								=			np.ones((1, 1)	) * model.config.eos_token_id
									lowerCAmelCase__								=			model(__a	)
									self.assertIsNotNone(__a	)





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(unittest.TestCase ):
					lowercase_   :			Dict   =   None
					lowercase_   :			Tuple   =   None
					@classmethod
					def 					A__			(	cls  :		Optional[Any]	):



							"""simple docstring"""
							lowerCAmelCase__								=			F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
							return cls
					@cached_property
					def 					A__			(	self  :		Union[str, Any]	):



							"""simple docstring"""
							return MarianTokenizer.from_pretrained(self.model_name	)
					@property
					def 					A__			(	self  :		Optional[Any]	):



							"""simple docstring"""
							return self.tokenizer.eos_token_id
					@cached_property
					def 					A__			(	self  :		Tuple	):



							"""simple docstring"""
							lowerCAmelCase__								=			FlaxMarianMTModel.from_pretrained(self.model_name	)
							self.assertEqual(model.config.decoder_start_token_id					,	model.config.pad_token_id	)
							return model
					def 					A__			(	self  :		str					,	**__lowerCamelCase  :		str	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.translate_src_text(**__a	)
							self.assertListEqual(self.expected_text					,	__a	)



					def 					A__			(	self  :		Union[str, Any]					,	**__lowerCamelCase  :		int	):



							"""simple docstring"""
							lowerCAmelCase__								=			self.tokenizer(self.src_text					,	padding=__a					,	return_tensors='''np'''					,	**__a	)
							lowerCAmelCase__								=			self.model.generate(
							    model_inputs.input_ids					,	attention_mask=model_inputs.attention_mask					,	num_beams=2					,	max_length=1_28					,	).sequences
							lowerCAmelCase__								=			self.tokenizer.batch_decode(__a					,	skip_special_tokens=__a	)
							return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			Dict   =   "en"
					lowercase_   :			Optional[int]   =   "fr"
					lowercase_   :			List[str]   =   [
					    "I am a small frog.",
					    "Now I can forget the 100 words of german that I know.",
					]
					lowercase_   :			int   =   [
					    "Je suis une petite grenouille.",
					    "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
					]
					@slow
					def 					A__			(	self  :		List[str]	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			str   =   "fr"
					lowercase_   :			Dict   =   "en"
					lowercase_   :			Optional[int]   =   [
					    "Donnez moi le micro.",
					    "Tom et Mary étaient assis à une table.",  # Accents
					]
					lowercase_   :			List[Any]   =   [
					    "Give me the microphone.",
					    "Tom and Mary were sitting at a table.",
					]
					@slow
					def 					A__			(	self  :		Union[str, Any]	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			int   =   "mt"
					lowercase_   :			List[Any]   =   "en"
					lowercase_   :			Optional[int]   =   ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
					lowercase_   :			str   =   ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
					@slow
					def 					A__			(	self  :		List[str]	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			int   =   "en"
					lowercase_   :			str   =   "de"
					lowercase_   :			List[str]   =   [
					    "I am a small frog.",
					    "Now I can forget the 100 words of german that I know.",
					    "Tom asked his teacher for advice.",
					    "That's how I would do it.",
					    "Tom really admired Mary's courage.",
					    "Turn around and close your eyes.",
					]
					lowercase_   :			Optional[Any]   =   [
					    "Ich bin ein kleiner Frosch.",
					    "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
					    "Tom bat seinen Lehrer um Rat.",
					    "So würde ich das machen.",
					    "Tom bewunderte Marias Mut wirklich.",
					    "Drehen Sie sich um und schließen Sie die Augen.",
					]
					@slow
					def 					A__			(	self  :		Optional[Any]	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			Optional[Any]   =   "en"
					lowercase_   :			int   =   "zh"
					lowercase_   :			List[Any]   =   ["My name is Wolfgang and I live in Berlin"]
					lowercase_   :			Optional[Any]   =   ["我叫沃尔夫冈 我住在柏林"]
					@slow
					def 					A__			(	self  :		Any	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			Union[str, Any]   =   "ru"
					lowercase_   :			Any   =   "fr"
					lowercase_   :			Dict   =   ["Он показал мне рукопись своей новой пьесы."]
					lowercase_   :			Union[str, Any]   =   ["Il m'a montré le manuscrit de sa nouvelle pièce."]
					@slow
					def 					A__			(	self  :		str	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class 						SCREAMING_SNAKE_CASE__		(lowerCAmelCase_ ):
					lowercase_   :			Dict   =   "en"
					lowercase_   :			str   =   "ROMANCE"
					lowercase_   :			Dict   =   [
					    ">>fr<< Don't spend so much time watching TV.",
					    ">>pt<< Your message has been sent.",
					    ">>es<< He's two years older than me.",
					]
					lowercase_   :			str   =   [
					    "Ne passez pas autant de temps à regarder la télé.",
					    "A sua mensagem foi enviada.",
					    "Es dos años más viejo que yo.",
					]
					@slow
					def 					A__			(	self  :		Tuple	):



							"""simple docstring"""
							self._assert_generated_batch_equal_expected()







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
      print("""#################### AVAILABLE DEVICES ####################""")
      print(jax.devices())
      print("""###########################################################""")

      # setup for wandb sweep
      __magic_name__     :     Optional[Any]  							=					Args()
      __magic_name__     :     List[Any]  							=					wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
      __magic_name__     :     Any  							=					dict(logger.config)
      del wandb_args["batch_size"]
      __magic_name__     :     int  							=					replace(args, **wandb_args)
      __magic_name__     :     Tuple  							=					args.base_dir + """-""" + wandb.run.id
      __magic_name__     :     Union[str, Any]  							=					replace(args, base_dir=base_dir)
      print(args)

      __magic_name__     :     Union[str, Any]  							=					load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
      __magic_name__     :     List[Any]  							=					load_dataset("""json""", data_files=args.val_data_path)["""train"""]

      # drop extra batch for now
      __magic_name__     :     List[str]  							=					range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					tr_dataset.shuffle().select(indices)
      __magic_name__     :     Any  							=					range(len(val_dataset) - len(val_dataset) % args.batch_size)
      __magic_name__     :     Dict  							=					val_dataset.shuffle().select(indices)

      if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
            __magic_name__     :     Any  							=					tr_dataset.shuffle().select(range(8_00_00))
            __magic_name__     :     Optional[int]  							=					val_dataset.shuffle().select(range(80_00))

      print(tr_dataset)
      print(val_dataset)

      __magic_name__     :     Union[str, Any]  							=					FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      __magic_name__     :     Tuple  							=					BigBirdTokenizerFast.from_pretrained(args.model_id)
      __magic_name__     :     Tuple  							=					DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

      __magic_name__     :     int  							=					{
          """lr""": args.lr,
          """init_lr""": args.init_lr,
          """warmup_steps""": args.warmup_steps,
          """num_train_steps""": args.max_epochs * (len(tr_dataset) // args.batch_size),
          """weight_decay""": args.weight_decay,
      }
      __magic_name__							,   __magic_name__     :     Optional[int]  							=					build_tx(**tx_args)

      __magic_name__     :     List[str]  							=					Trainer(
          args=args,
          data_collator=data_collator,
          model_save_fn=model.save_pretrained,
          train_step_fn=train_step,
          val_step_fn=val_step,
          logger=logger,
          scheduler_fn=lr,
      )

      __magic_name__     :     Union[str, Any]  							=					None
      __magic_name__     :     Dict  							=					trainer.create_state(model, tx, num_train_steps=tx_args["""num_train_steps"""], ckpt_dir=ckpt_dir)
      try:
            trainer.train(state, tr_dataset, val_dataset)
      except KeyboardInterrupt:
            print("""Oooops; TRAINING STOPPED UNFORTUNATELY""")

      print("""SAVING WEIGHTS IN `final-weights`""")
      __magic_name__     :     List[Any]  							=					jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)

