





"""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
       __UpperCamelCase			:		Optional[Any]		    =   Args()
       __UpperCamelCase			:		Optional[Any]		    =   wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
       __UpperCamelCase			:		int		    =   dict(logger.config)
       del wandb_args["batch_size"]
       __UpperCamelCase			:		List[Any]		    =   replace(args, **wandb_args)
       __UpperCamelCase			:		Tuple		    =   args.base_dir + '''-''' + wandb.run.id
       __UpperCamelCase			:		Dict		    =   replace(args, base_dir=base_dir)
       print(args)

       __UpperCamelCase			:		Optional[Any]		    =   load_dataset('''json''', data_files=args.tr_data_path)['''train''']
       __UpperCamelCase			:		str		    =   load_dataset('''json''', data_files=args.val_data_path)['''train''']

       # drop extra batch for now
       __UpperCamelCase			:		Any		    =   range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
       __UpperCamelCase			:		Optional[int]		    =   tr_dataset.shuffle().select(indices)
       __UpperCamelCase			:		Optional[Any]		    =   range(len(val_dataset) - len(val_dataset) % args.batch_size)
       __UpperCamelCase			:		Optional[Any]		    =   val_dataset.shuffle().select(indices)

       if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
              __UpperCamelCase			:		Union[str, Any]		    =   tr_dataset.shuffle().select(range(8_0000))
              __UpperCamelCase			:		Dict		    =   val_dataset.shuffle().select(range(8000))

       print(tr_dataset)
       print(val_dataset)

       __UpperCamelCase			:		Union[str, Any]		    =   FlaxBigBirdForNaturalQuestions.from_pretrained(
           args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
       )
       __UpperCamelCase			:		int		    =   BigBirdTokenizerFast.from_pretrained(args.model_id)
       __UpperCamelCase			:		Union[str, Any]		    =   DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

       __UpperCamelCase			:		Optional[Any]		    =   {
           '''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,
       }
       __UpperCamelCase    ,__UpperCamelCase			:		Dict		    =   build_tx(**tx_args)

       __UpperCamelCase			:		Optional[int]		    =   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,
       )

       __UpperCamelCase			:		Any		    =   None
       __UpperCamelCase			:		Optional[int]		    =   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`''')
       __UpperCamelCase			:		List[Any]		    =   jax_utils.unreplicate(state.params)
       model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)








import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__magic_name__ (				unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     UpperCamelCase_     :Any									=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

         UpperCamelCase_          =		datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,		"image"						,		split="test"     )

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		image_classifier(
             [
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 # RGBA
                 dataset[0]["file"],
                 # LA
                 dataset[1]["file"],
                 # L
                 dataset[2]["file"],
             ]     )
         self.assertEqual(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     @require_tf
     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         UpperCamelCase_          =		pipeline(
             "image-classification"						,		model="hf-internal-testing/tiny-random-vit"						,		tokenizer=_lowercase     )

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							self     )->			int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.4_385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0_502, "label": "Egyptian cat"},
                 {"score": 0.0_137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.5_658, "label": "tabby, tabby cat"},
                 {"score": 0.1_309, "label": "tiger cat"},
                 {"score": 0.0_722, "label": "Egyptian cat"},
                 {"score": 0.0_707, "label": "remote control, remote"},
                 {"score": 0.0_082, "label": "computer keyboard, keypad"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.3_022, "label": "tabby, tabby cat"},
                 {"score": 0.2_362, "label": "Egyptian cat"},
                 {"score": 0.1_856, "label": "tiger cat"},
                 {"score": 0.0_324, "label": "remote control, remote"},
                 {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
             ]						,		)
