





import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402



class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     __lowerCAmelCase								=	0






  def 				lowercase		(				self :		Optional[Any]     )							->						Dict:
     __lowerCAmelCase								=	AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'     )
     self.assertIsInstance(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Any     )							->						List[Any]:
     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'preprocessor_config.json'
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'config.json'
        json.dump(
            {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}					,  open(lowerCAmelCase_					,  'w'     )					,  )
        json.dump({'model_type': 'clip'}					,  open(lowerCAmelCase_					,  'w'     )     )

        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
        self.assertIsInstance(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[int]     )							->						Dict:
     # Ensure we can load the image processor from the feature extractor config
     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'preprocessor_config.json'
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'config.json'
        json.dump(
            {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}					,  open(lowerCAmelCase_					,  'w'     )					,  )
        json.dump({'model_type': 'clip'}					,  open(lowerCAmelCase_					,  'w'     )     )

        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
        self.assertIsInstance(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		Dict     )							->						str:
     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase								=	CLIPConfig()

        # Create a dummy config file with image_proceesor_type
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'preprocessor_config.json'
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'config.json'
        json.dump(
            {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}					,  open(lowerCAmelCase_					,  'w'     )					,  )
        json.dump({'model_type': 'clip'}					,  open(lowerCAmelCase_					,  'w'     )     )

        # remove image_processor_type to make sure config.json alone is enough to load image processor locally
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_     ).to_dict()

        config_dict.pop('image_processor_type'     )
        __lowerCAmelCase								=	CLIPImageProcessor(**lowerCAmelCase_     )

        # save in new folder
        model_config.save_pretrained(lowerCAmelCase_     )
        config.save_pretrained(lowerCAmelCase_     )

        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_     )

        # make sure private variable is not incorrectly saved
        __lowerCAmelCase								=	json.loads(config.to_json_string()     )
        self.assertTrue('_processor_class' not in dict_as_saved     )

     self.assertIsInstance(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		List[str]     )							->						Optional[Any]:
     with tempfile.TemporaryDirectory() as tmpdirname:
        __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'preprocessor_config.json'
        json.dump(
            {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}					,  open(lowerCAmelCase_					,  'w'     )					,  )

        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
        self.assertIsInstance(lowerCAmelCase_					,  lowerCAmelCase_     )






  def 				lowercase		(				self :		str     )							->						Optional[int]:
     with self.assertRaisesRegex(
         lowerCAmelCase_					,  'clip-base is not a local folder and is not a valid model identifier'     ):
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained('clip-base'     )






  def 				lowercase		(				self :		int     )							->						Any:
     with self.assertRaisesRegex(
         lowerCAmelCase_					,  R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'     ):
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_					,  revision='aaaaaa'     )






  def 				lowercase		(				self :		Tuple     )							->						List[Any]:
     with self.assertRaisesRegex(
         lowerCAmelCase_					,  'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'					,  ):
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'     )






  def 				lowercase		(				self :		str     )							->						List[str]:
     # If remote code is not set, we will time out when asking whether to load the model.
     with self.assertRaises(lowerCAmelCase_     ):
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'     )
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(lowerCAmelCase_     ):
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(
            'hf-internal-testing/test_dynamic_image_processor'					,  trust_remote_code=lowerCAmelCase_     )

     __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(
         'hf-internal-testing/test_dynamic_image_processor'					,  trust_remote_code=lowerCAmelCase_     )
     self.assertEqual(image_processor.__class__.__name__					,  'NewImageProcessor'     )

     # Test image processor can be reloaded.
     with tempfile.TemporaryDirectory() as tmp_dir:
        image_processor.save_pretrained(lowerCAmelCase_     )
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_					,  trust_remote_code=lowerCAmelCase_     )
     self.assertEqual(reloaded_image_processor.__class__.__name__					,  'NewImageProcessor'     )






  def 				lowercase		(				self :		str     )							->						Dict:
     try:
        AutoConfig.register('custom'					,  lowerCAmelCase_     )
        AutoImageProcessor.register(lowerCAmelCase_					,  lowerCAmelCase_     )
        # Trying to register something existing in the Transformers library will raise an error
        with self.assertRaises(lowerCAmelCase_     ):
           AutoImageProcessor.register(lowerCAmelCase_					,  lowerCAmelCase_     )

        with tempfile.TemporaryDirectory() as tmpdirname:
           __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'preprocessor_config.json'
           __lowerCAmelCase								=	Path(lowerCAmelCase_     ) / 'config.json'
           json.dump(
               {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}					,  open(lowerCAmelCase_					,  'w'     )					,  )
           json.dump({'model_type': 'clip'}					,  open(lowerCAmelCase_					,  'w'     )     )

           __lowerCAmelCase								=	CustomImageProcessor.from_pretrained(lowerCAmelCase_     )

        # Now that the config is registered, it can be used as any other config with the auto-API
        with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(lowerCAmelCase_     )
           __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(lowerCAmelCase_     )
           self.assertIsInstance(lowerCAmelCase_					,  lowerCAmelCase_     )

     finally:
        if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
        if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]






  def 				lowercase		(				self :		List[Any]     )							->						Optional[Any]:



     class 				_UpperCAmelCase    (    _UpperCamelCase    ):

       """simple docstring"""


       a_        = True

     try:
        AutoConfig.register('custom'					,  lowerCAmelCase_     )
        AutoImageProcessor.register(lowerCAmelCase_					,  lowerCAmelCase_     )
        # If remote code is not set, the default is to use local
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'     )
        self.assertEqual(image_processor.__class__.__name__					,  'NewImageProcessor'     )
        self.assertTrue(image_processor.is_local     )

        # If remote code is disabled, we load the local one.
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(
            'hf-internal-testing/test_dynamic_image_processor'					,  trust_remote_code=lowerCAmelCase_     )
        self.assertEqual(image_processor.__class__.__name__					,  'NewImageProcessor'     )
        self.assertTrue(image_processor.is_local     )

        # If remote is enabled, we load from the Hub
        __lowerCAmelCase								=	AutoImageProcessor.from_pretrained(
            'hf-internal-testing/test_dynamic_image_processor'					,  trust_remote_code=lowerCAmelCase_     )
        self.assertEqual(image_processor.__class__.__name__					,  'NewImageProcessor'     )
        self.assertTrue(not hasattr(lowerCAmelCase_					,  'is_local'     )     )

     finally:
        if "custom" in CONFIG_MAPPING._extra_content:
           del CONFIG_MAPPING._extra_content["custom"]
        if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
           del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

