




import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch





@require_flax
class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		Any					,	__lowerCamelCase  :		List[str]					,	__lowerCamelCase  :		Dict=14					,	__lowerCamelCase  :		Optional[Any]=7					,	__lowerCamelCase  :		str=True					,	__lowerCamelCase  :		List[Any]=True					,	__lowerCamelCase  :		int=True					,	__lowerCamelCase  :		int=99					,	__lowerCamelCase  :		Optional[Any]=32					,	__lowerCamelCase  :		Union[str, Any]=5					,	__lowerCamelCase  :		List[str]=4					,	__lowerCamelCase  :		Optional[int]=37					,	__lowerCamelCase  :		Optional[Any]="gelu"					,	__lowerCamelCase  :		Optional[Any]=0.1					,	__lowerCamelCase  :		List[Any]=0.1					,	__lowerCamelCase  :		List[str]=5_12					,	__lowerCamelCase  :		Tuple=0.02					,	__lowerCamelCase  :		Optional[int]=None					,	):



       """simple docstring"""
       lowerCAmelCase__								=			parent
       lowerCAmelCase__								=			batch_size
       lowerCAmelCase__								=			seq_length
       lowerCAmelCase__								=			is_training
       lowerCAmelCase__								=			use_input_mask
       lowerCAmelCase__								=			use_labels
       lowerCAmelCase__								=			vocab_size
       lowerCAmelCase__								=			d_model
       lowerCAmelCase__								=			num_hidden_layers
       lowerCAmelCase__								=			num_attention_heads
       lowerCAmelCase__								=			ffn_dim
       lowerCAmelCase__								=			activation_function
       lowerCAmelCase__								=			activation_dropout
       lowerCAmelCase__								=			attention_dropout
       lowerCAmelCase__								=			max_position_embeddings
       lowerCAmelCase__								=			initializer_range
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			0
       lowerCAmelCase__								=			2
       lowerCAmelCase__								=			1
     def 					A__			(	self  :		Dict	):



       """simple docstring"""
       lowerCAmelCase__								=			np.clip(ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size	)					,	3					,	self.vocab_size	)

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

       lowerCAmelCase__								=			XGLMConfig(
           vocab_size=self.vocab_size					,	d_model=self.hidden_size					,	num_layers=self.num_hidden_layers					,	attention_heads=self.num_attention_heads					,	ffn_dim=self.ffn_dim					,	activation_function=self.activation_function					,	activation_dropout=self.activation_dropout					,	attention_dropout=self.attention_dropout					,	max_position_embeddings=self.max_position_embeddings					,	initializer_range=self.initializer_range					,	use_cache=__lowerCamelCase					,	bos_token_id=self.bos_token_id					,	eos_token_id=self.eos_token_id					,	pad_token_id=self.pad_token_id					,	)

       return (config, input_ids, input_mask)
     def 					A__			(	self  :		Dict	):



       """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  :		Optional[Any]	):



       """simple docstring"""
       lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__								=			self.prepare_config_and_inputs()

       lowerCAmelCase__								=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
       lowerCAmelCase__								=			ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2	)

       return (
           config,
           input_ids,
           attention_mask,
           encoder_hidden_states,
           encoder_attention_mask,
       )
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		List[str]					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		Optional[Any]					,	__lowerCamelCase  :		List[Any]	):



       """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  :		Dict					,	__lowerCamelCase  :		List[Any]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		List[str]					,	__lowerCamelCase  :		Union[str, Any]	):



       """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_sentencepiece
@require_flax
class 						SCREAMING_SNAKE_CASE__		(_a						,				_a						,				unittest.TestCase ):
     lowercase_   :			List[Any]   =   (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
     lowercase_   :			List[Any]   =   (FlaxXGLMForCausalLM,) if is_flax_available() else ()
     def 					A__			(	self  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			FlaxXGLMModelTester(self	)
     def 					A__			(	self  :		Optional[int]	):



       """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  :		List[str]	):



       """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  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			XGLMTokenizer.from_pretrained('''XGLM'''					,	padding_side='''left'''	)
       lowerCAmelCase__								=			tokenizer(['''Hello this is a long string''', '''Hey''']					,	return_tensors='''np'''					,	padding=__lowerCamelCase					,	truncation=__lowerCamelCase	)

       lowerCAmelCase__								=			FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''	)
       lowerCAmelCase__								=			1
       lowerCAmelCase__								=			False

       lowerCAmelCase__								=			jax.jit(model.generate	)

       lowerCAmelCase__								=			jit_generate(inputs['''input_ids''']					,	attention_mask=inputs['''attention_mask''']	).sequences

       lowerCAmelCase__								=			tokenizer.batch_decode(__lowerCamelCase					,	skip_special_tokens=__lowerCamelCase	)

       lowerCAmelCase__								=			[
           '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
           '''Hey, I\'m a newbie to the forum and I\'''',
       ]

       self.assertListEqual(__lowerCamelCase					,	__lowerCamelCase	)
     @is_pt_flax_cross_test
     def 					A__			(	self  :		int	):



       """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()
           # Flax models don't use the `use_cache` option and cache is not returned as a default.
           # So we disable `use_cache` here for PyTorch model.
           lowerCAmelCase__								=			False
           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  :		Any	):



       """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__								=			False
           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  :		List[Any]	):



       """simple docstring"""
       for model_class_name in self.all_model_classes:
         lowerCAmelCase__								=			model_class_name.from_pretrained('''facebook/xglm-564M'''	)
         lowerCAmelCase__								=			model(np.ones((1, 1)	)	)
         self.assertIsNotNone(__lowerCamelCase	)







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)

