'''simple docstring'''




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
						__lowercase				   =  Args()
						__lowercase				   =  wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
						__lowercase				   =  dict(logger.config)
						del wandb_args["batch_size"]
						__lowercase				   =  replace(args, **wandb_args)
						__lowercase				   =  args.base_dir + '''-''' + wandb.run.id
						__lowercase				   =  replace(args, base_dir=base_dir)
						print(args)

						__lowercase				   =  load_dataset('''json''', data_files=args.tr_data_path)['''train''']
						__lowercase				   =  load_dataset('''json''', data_files=args.val_data_path)['''train''']

						# drop extra batch for now
						__lowercase				   =  range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
						__lowercase				   =  tr_dataset.shuffle().select(indices)
						__lowercase				   =  range(len(val_dataset) - len(val_dataset) % args.batch_size)
						__lowercase				   =  val_dataset.shuffle().select(indices)

						if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
												__lowercase				   =  tr_dataset.shuffle().select(range(8_0_0_0_0))
												__lowercase				   =  val_dataset.shuffle().select(range(8_0_0_0))

						print(tr_dataset)
						print(val_dataset)

						__lowercase				   =  FlaxBigBirdForNaturalQuestions.from_pretrained(
						    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
						)
						__lowercase				   =  BigBirdTokenizerFast.from_pretrained(args.model_id)
						__lowercase				   =  DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

						__lowercase				   =  {
						    '''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,
						}
						__lowercase   ,					__lowercase				   =  build_tx(**tx_args)

						__lowercase				   =  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,
						)

						__lowercase				   =  None
						__lowercase				   =  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`''')
						__lowercase				   =  jax_utils.unreplicate(state.params)
						model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)





'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=14	,   __lowerCAmelCase=7	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   __lowerCAmelCase=99	,   __lowerCAmelCase=32	,   __lowerCAmelCase=4	,   __lowerCAmelCase=4	,   __lowerCAmelCase=[[["global", "local"], 2]]	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=7	,   __lowerCAmelCase=0.02	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  batch_size
					lowerCAmelCase        =  seq_length
					lowerCAmelCase        =  is_training
					lowerCAmelCase        =  use_input_mask
					lowerCAmelCase        =  use_token_type_ids
					lowerCAmelCase        =  use_labels
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  hidden_size
					lowerCAmelCase        =  num_hidden_layers
					lowerCAmelCase        =  num_attention_heads
					lowerCAmelCase        =  attention_types
					lowerCAmelCase        =  intermediate_size
					lowerCAmelCase        =  hidden_act
					lowerCAmelCase        =  hidden_dropout_prob
					lowerCAmelCase        =  attention_probs_dropout_prob
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  window_size
					lowerCAmelCase        =  initializer_range
					lowerCAmelCase        =  None
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size)

					lowerCAmelCase        =  None
					if self.use_input_mask:
							lowerCAmelCase        =  random_attention_mask([self.batch_size, self.seq_length])

					lowerCAmelCase        =  GPTNeoConfig(
					    vocab_size=self.vocab_size	,   hidden_size=self.hidden_size	,   num_layers=self.num_hidden_layers	,   num_heads=self.num_attention_heads	,   max_position_embeddings=self.max_position_embeddings	,   use_cache=__lowerCAmelCase	,   bos_token_id=self.bos_token_id	,   eos_token_id=self.eos_token_id	,   pad_token_id=self.pad_token_id	,   window_size=self.window_size	,   attention_types=self.attention_types	,   )

					return (config, input_ids, input_mask)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  config_and_inputs
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.ones((input_ids.shape[0], max_decoder_length)	,   dtype="""i4""")

					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))
					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype="""i4""")
					lowerCAmelCase        =  model(
					    input_ids[:, -1:]	,   attention_mask=__lowerCAmelCase	,   past_key_values=outputs_cache.past_key_values	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  model(__lowerCAmelCase)

					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	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

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

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))

					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )
					lowerCAmelCase        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype="""i4""")
					lowerCAmelCase        =  model(
					    input_ids[:, -1:]	,   past_key_values=outputs_cache.past_key_values	,   attention_mask=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase)

					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     a__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			UpperCAmelCase_      :					Optional[Any]								=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  FlaxGPTNeoModelTester(self)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  GPTaTokenizer.from_pretrained("""gpt2"""	,   pad_token="""<|endoftext|>"""	,   padding_side="""left""")
					lowerCAmelCase        =  tokenizer(["""Hello this is a long string""", """Hey"""]	,   return_tensors="""np"""	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase)

					lowerCAmelCase        =  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""")
					lowerCAmelCase        =  False
					lowerCAmelCase        =  model.config.eos_token_id

					lowerCAmelCase        =  jax.jit(model.generate)

					lowerCAmelCase        =  jit_generate(
					    inputs["""input_ids"""]	,   attention_mask=inputs["""attention_mask"""]	,   pad_token_id=tokenizer.pad_token_id).sequences

					lowerCAmelCase        =  tokenizer.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase)

					lowerCAmelCase        =  [
					    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
					    """Hey, I'm a little late to the party. I'm going to""",
					]

					self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase)



			@is_pt_flax_cross_test
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__):
									# prepare inputs
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCAmelCase        =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase        =  getattr(__lowerCAmelCase	,   __lowerCAmelCase)

									lowerCAmelCase ,					lowerCAmelCase        =  pt_inputs["""input_ids"""].shape
									lowerCAmelCase        =  np.random.randint(0	,   seq_length - 1	,   size=(batch_size,))
									for batch_idx, start_index in enumerate(__lowerCAmelCase):
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

									lowerCAmelCase        =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   __lowerCAmelCase)
									lowerCAmelCase        =  fx_state

									with torch.no_grad():
											lowerCAmelCase        =  pt_model(**__lowerCAmelCase).to_tuple()

									lowerCAmelCase        =  fx_model(**__lowerCAmelCase).to_tuple()
									self.assertEqual(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)

									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(__lowerCAmelCase)
											lowerCAmelCase        =  model_class.from_pretrained(__lowerCAmelCase	,   from_pt=__lowerCAmelCase)

									lowerCAmelCase        =  fx_model_loaded(**__lowerCAmelCase).to_tuple()
									self.assertEqual(
									    len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output_loaded, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output_loaded[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)



			@is_pt_flax_cross_test
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__):
									# prepare inputs
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCAmelCase        =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase        =  getattr(__lowerCAmelCase	,   __lowerCAmelCase)

									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

									lowerCAmelCase        =  load_flax_weights_in_pytorch_model(__lowerCAmelCase	,   fx_model.params)
									lowerCAmelCase ,					lowerCAmelCase        =  pt_inputs["""input_ids"""].shape
									lowerCAmelCase        =  np.random.randint(0	,   seq_length - 1	,   size=(batch_size,))
									for batch_idx, start_index in enumerate(__lowerCAmelCase):
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
											lowerCAmelCase        =  pt_model(**__lowerCAmelCase).to_tuple()

									lowerCAmelCase        =  fx_model(**__lowerCAmelCase).to_tuple()
									self.assertEqual(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)

									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(__lowerCAmelCase)
											lowerCAmelCase        =  pt_model_class.from_pretrained(__lowerCAmelCase	,   from_flax=__lowerCAmelCase)

									with torch.no_grad():
											lowerCAmelCase        =  pt_model_loaded(**__lowerCAmelCase).to_tuple()

									self.assertEqual(
									    len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase        =  model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
							lowerCAmelCase        =  model(np.ones((1, 1)))
							self.assertIsNotNone(__lowerCAmelCase)




