

"""simple docstring"""





import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

   from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor




class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 def __init__(    self							:    Optional[Any],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    str=3,      UpperCAmelCase__							:    Dict=3_2,      UpperCAmelCase__							:    str=3,      UpperCAmelCase__							:    Union[str, Any]=1_0,      UpperCAmelCase__							:    Optional[int]=[1_0, 2_0, 3_0, 4_0],      UpperCAmelCase__							:    Any=[1, 1, 2, 1],      UpperCAmelCase__							:    Tuple=True,      UpperCAmelCase__							:    Dict=True,      UpperCAmelCase__							:    int="relu",      UpperCAmelCase__							:    int=3,      UpperCAmelCase__							:    str=None,      ):
    __lowercase			=		parent
    __lowercase			=		batch_size
    __lowercase			=		image_size
    __lowercase			=		num_channels
    __lowercase			=		embeddings_size
    __lowercase			=		hidden_sizes
    __lowercase			=		depths
    __lowercase			=		is_training
    __lowercase			=		use_labels
    __lowercase			=		hidden_act
    __lowercase			=		num_labels
    __lowercase			=		scope
    __lowercase			=		len(UpperCAmelCase__			)


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

    __lowercase			=		self.get_config()

    return config, pixel_values


 def       _lowercase				(    self							:    Any			):
    return ResNetConfig(
        num_channels=self.num_channels,      embeddings_size=self.embeddings_size,      hidden_sizes=self.hidden_sizes,      depths=self.depths,      hidden_act=self.hidden_act,      num_labels=self.num_labels,      image_size=self.image_size,      )


 def       _lowercase				(    self							:    Union[str, Any],      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    Union[str, Any]			):
    __lowercase			=		FlaxResNetModel(config=UpperCAmelCase__			)
    __lowercase			=		model(UpperCAmelCase__			)

    # Output shape (b, c, h, w)
    self.parent.assertEqual(
        result.last_hidden_state.shape,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),      )


 def       _lowercase				(    self							:    Optional[Any],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    List[str]			):
    __lowercase			=		self.num_labels
    __lowercase			=		FlaxResNetForImageClassification(config=UpperCAmelCase__			)
    __lowercase			=		model(UpperCAmelCase__			)
    self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.num_labels)			)



 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		self.prepare_config_and_inputs()
    __lowercase	,__lowercase			=		config_and_inputs
    __lowercase			=		{"pixel_values": pixel_values}
    return config, inputs_dict







@require_flax
class       _lowerCAmelCase						(			lowercase  ,unittest.TestCase     ):







 """simple docstring"""







 __UpperCAmelCase			:      Optional[int]     	=			(FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

 __UpperCAmelCase			:      Union[str, Any]     	=			False
 __UpperCAmelCase			:      Any     	=			False
 __UpperCAmelCase			:      Union[str, Any]     	=			False


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		FlaxResNetModelTester(self			)
    __lowercase			=		ConfigTester(self,      config_class=UpperCAmelCase__,      has_text_modality=UpperCAmelCase__			)


 def       _lowercase				(    self							:    Tuple			):
    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()


 def       _lowercase				(    self							:    List[str]			):
    return


 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCAmelCase__			)


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__			)


 @unittest.skip(reason="ResNet does not use inputs_embeds"			)
 def       _lowercase				(    self							:    List[str]			):
    pass


 @unittest.skip(reason="ResNet does not support input and output embeddings"			)
 def       _lowercase				(    self							:    Tuple			):
    pass


 def       _lowercase				(    self							:    int			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       __lowercase			=		model_class(UpperCAmelCase__			)
       __lowercase			=		inspect.signature(model.__call__			)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       __lowercase			=		[*signature.parameters.keys()]

       __lowercase			=		["pixel_values"]
       self.assertListEqual(arg_names[:1],      UpperCAmelCase__			)


 def       _lowercase				(    self							:    Optional[Any]			):
    def check_hidden_states_output(UpperCAmelCase__							:    Tuple,      UpperCAmelCase__							:    str,      UpperCAmelCase__							:    Optional[Any]			):
       __lowercase			=		model_class(UpperCAmelCase__			)

       __lowercase			=		model(**self._prepare_for_class(UpperCAmelCase__,      UpperCAmelCase__			)			)

       __lowercase			=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __lowercase			=		self.model_tester.num_stages
       self.assertEqual(len(UpperCAmelCase__			),      expected_num_stages + 1			)


 @unittest.skip(reason="ResNet does not use feedforward chunking"			)
 def       _lowercase				(    self							:    str			):
    pass



 def       _lowercase				(    self							:    Union[str, Any]			):
    __lowercase	,__lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__			):
          __lowercase			=		self._prepare_for_class(UpperCAmelCase__,      UpperCAmelCase__			)
          __lowercase			=		model_class(UpperCAmelCase__			)

          @jax.jit
          def model_jitted(UpperCAmelCase__							:    str,      **UpperCAmelCase__							:    Optional[int]			):
             return model(pixel_values=UpperCAmelCase__,      **UpperCAmelCase__			)

          with self.subTest("JIT Enabled"			):
             __lowercase			=		model_jitted(**UpperCAmelCase__			).to_tuple()

          with self.subTest("JIT Disabled"			):
             with jax.disable_jit():
                __lowercase			=		model_jitted(**UpperCAmelCase__			).to_tuple()

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







def 						_A						(						)			->		Optional[int]:
   '''simple docstring'''


   __lowercase			=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
   return image




@require_flax
class       _lowerCAmelCase						(			unittest.TestCase     ):







 """simple docstring"""







 @cached_property
 def       _lowercase				(    self							:    Dict			):
    return AutoImageProcessor.from_pretrained("microsoft/resnet-50"			) if is_vision_available() else None



 @slow
 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"			)

    __lowercase			=		self.default_image_processor
    __lowercase			=		prepare_img()
    __lowercase			=		image_processor(images=UpperCAmelCase__,      return_tensors="np"			)

    __lowercase			=		model(**UpperCAmelCase__			)

    # verify the logits
    __lowercase			=		(1, 1_0_0_0)
    self.assertEqual(outputs.logits.shape,      UpperCAmelCase__			)

    __lowercase			=		jnp.array([-11.1_069, -9.7_877, -8.3_777]			)

    self.assertTrue(jnp.allclose(outputs.logits[0, :3],      UpperCAmelCase__,      atol=1E-4			)			)







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

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

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

   if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
      _a				   =							tr_dataset.shuffle().select(range(8_00_00))
      _a				   =							val_dataset.shuffle().select(range(80_00))

   print(tr_dataset)
   print(val_dataset)

   _a				   =							FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   _a				   =							BigBirdTokenizerFast.from_pretrained(args.model_id)
   _a				   =							DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

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

   _a				   =							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,
   )

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




