




"""simple docstring"""




import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import EfficientNetForImageClassification, EfficientNetModel
			from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class        UpperCAmelCase_  :
		def __init__(		self							,		UpperCamelCase_							,		UpperCamelCase_=13							,		UpperCamelCase_=32							,		UpperCamelCase_=3							,		UpperCamelCase_=[3, 3, 5]							,		UpperCamelCase_=[32, 16, 24]							,		UpperCamelCase_=[16, 24, 40]							,		UpperCamelCase_=[1, 1, 2]							,		UpperCamelCase_=[1, 1, 2]							,		UpperCamelCase_=[1, 6, 6]							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_=37							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=10							,		)		->   List[str]:
					__lowercase	:       Union[str, Any]				=   parent
					__lowercase	:       Dict				=   batch_size
					__lowercase	:       int				=   image_size
					__lowercase	:       str				=   num_channels
					__lowercase	:       Optional[int]				=   kernel_sizes
					__lowercase	:       Optional[int]				=   in_channels
					__lowercase	:       str				=   out_channels
					__lowercase	:       List[str]				=   strides
					__lowercase	:       Optional[Any]				=   num_block_repeats
					__lowercase	:       Any				=   expand_ratios
					__lowercase	:       List[Any]				=   is_training
					__lowercase	:       Union[str, Any]				=   hidden_act
					__lowercase	:       Dict				=   num_labels
					__lowercase	:       str				=   use_labels
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					__lowercase	:       Optional[Any]				=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					__lowercase	:       Optional[int]				=   None
					if self.use_labels:
								__lowercase	:       Dict				=   ids_tensor([self.batch_size]							,		self.num_labels   )

					__lowercase	:       Union[str, Any]				=   self.get_config()
					return config, pixel_values, labels
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					return EfficientNetConfig(
					    num_channels=self.num_channels							,		kernel_sizes=self.kernel_sizes							,		in_channels=self.in_channels							,		out_channels=self.out_channels							,		strides=self.strides							,		num_block_repeats=self.num_block_repeats							,		expand_ratios=self.expand_ratios							,		hidden_act=self.hidden_act							,		num_labels=self.num_labels							,		)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   str:
					__lowercase	:       Union[str, Any]				=   EfficientNetModel(config=UpperCamelCase_   )
					model.to(UpperCamelCase_   )
					model.eval()
					__lowercase	:       Optional[int]				=   model(UpperCamelCase_   )
					# expected last hidden states: B, C, H // 4, W // 4
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,		(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)							,		)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   List[str]:
					__lowercase	:       List[str]				=   EfficientNetForImageClassification(UpperCamelCase_   )
					model.to(UpperCamelCase_   )
					model.eval()
					__lowercase	:       Tuple				=   model(UpperCamelCase_							,		labels=UpperCamelCase_   )
					self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels)   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       str				=   self.prepare_config_and_inputs()
					__lowercase	,__lowercase	,__lowercase	:       int				=   config_and_inputs
					__lowercase	:       Tuple				=   {'''pixel_values''': pixel_values}
					return config, inputs_dict


@require_torch
class        UpperCAmelCase_  (	snake_case ,			snake_case ,			unittest.TestCase						):
		UpperCamelCase									=(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
		UpperCamelCase									=(
		    {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
		    if is_torch_available()
		    else {}
		)

		UpperCamelCase									=False
		UpperCamelCase									=False
		UpperCamelCase									=False
		UpperCamelCase									=False
		UpperCamelCase									=False
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[Any]				=   EfficientNetModelTester(self   )
					__lowercase	:       str				=   ConfigTester(
					    self							,		config_class=UpperCamelCase_							,		has_text_modality=UpperCamelCase_							,		hidden_size=37   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					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 					_lowerCamelCase    (		self   )		->   Any:
					return
		@unittest.skip(reason='''EfficientNet does not use inputs_embeds'''   )
		def 					_lowerCamelCase    (		self   )		->   int:
					pass
		@unittest.skip(reason='''EfficientNet does not support input and output embeddings'''   )
		def 					_lowerCamelCase    (		self   )		->   Tuple:
					pass
		@unittest.skip(reason='''EfficientNet does not use feedforward chunking'''   )
		def 					_lowerCamelCase    (		self   )		->   int:
					pass
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	,__lowercase	:       Any				=   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__lowercase	:       Optional[Any]				=   model_class(UpperCamelCase_   )
								__lowercase	:       Union[str, Any]				=   inspect.signature(model.forward   )
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								__lowercase	:       List[str]				=   [*signature.parameters.keys()]

								__lowercase	:       List[str]				=   ['''pixel_values''']
								self.assertListEqual(arg_names[:1]							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Dict				=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   int:
					def check_hidden_states_output(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   ):
								__lowercase	:       Optional[int]				=   model_class(UpperCamelCase_   )
								model.to(UpperCamelCase_   )
								model.eval()

								with torch.no_grad():
											__lowercase	:       str				=   model(**self._prepare_for_class(UpperCamelCase_							,		UpperCamelCase_   )   )

								__lowercase	:       str				=   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
								__lowercase	:       Any				=   sum(config.num_block_repeats   ) * 4
								self.assertEqual(len(UpperCamelCase_   )							,		UpperCamelCase_   )

								# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]   )							,		[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,		)

					__lowercase	,__lowercase	:       str				=   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								__lowercase	:       List[str]				=   True
								check_hidden_states_output(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								__lowercase	:       str				=   True

								check_hidden_states_output(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					__lowercase	:       Union[str, Any]				=   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_   )
		@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					pass
		@slow
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowercase	:       Tuple				=   EfficientNetModel.from_pretrained(UpperCamelCase_   )
								self.assertIsNotNone(UpperCamelCase_   )
		@is_pipeline_test
		@require_vision
		@slow
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					super().test_pipeline_image_classification()







def       __UpperCAmelCase      (				):
			__lowercase	:       Optional[int]				=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
			return image


@require_torch
@require_vision
class        UpperCAmelCase_  (	unittest.TestCase						):
		@cached_property
		def 					_lowerCamelCase    (		self   )		->   int:
					return AutoImageProcessor.from_pretrained('''google/efficientnet-b7'''   ) if is_vision_available() else None
		@slow
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       Tuple				=   EfficientNetForImageClassification.from_pretrained('''google/efficientnet-b7'''   ).to(UpperCamelCase_   )

					__lowercase	:       Any				=   self.default_image_processor
					__lowercase	:       List[str]				=   prepare_img()
					__lowercase	:       Tuple				=   image_processor(images=UpperCamelCase_							,		return_tensors='''pt'''   ).to(UpperCamelCase_   )

					# forward pass
					with torch.no_grad():
								__lowercase	:       Union[str, Any]				=   model(**UpperCamelCase_   )

					# verify the logits
					__lowercase	:       Any				=   torch.Size((1, 10_00)   )
					self.assertEqual(outputs.logits.shape							,		UpperCamelCase_   )

					__lowercase	:       Union[str, Any]				=   torch.tensor([0.0_0_0_1, 0.0_0_0_2, 0.0_0_0_2]   ).to(UpperCamelCase_   )
					self.assertTrue(torch.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_0_0_0_0))
						a_            =   val_dataset.shuffle().select(range(8_0_0_0))

			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=4_0_9_6)

			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)



