"""simple docstring"""


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )




@require_vision
class  UpperCamelCase					(				unittest.TestCase   ):







       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					tempfile.mkdtemp()

            snake_case_					       =					BlipImageProcessor()
            snake_case_					       =					GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')
            snake_case_					       =					BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert')

            snake_case_					       =					InstructBlipProcessor(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)

            processor.save_pretrained(self.tmpdirname)







       def 		a_						( self,							**lowerCAmelCase__)      ->      str:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).tokenizer







       def 		a_						( self,							**lowerCAmelCase__)      ->      Dict:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).image_processor







       def 		a_						( self,							**lowerCAmelCase__)      ->      Optional[Any]:
            return AutoProcessor.from_pretrained(self.tmpdirname,							**lowerCAmelCase__).qformer_tokenizer







       def 		a_						( self)      ->      Dict:
            shutil.rmtree(self.tmpdirname)







       def 		a_						( self)      ->      str:
            snake_case_					       =					[np.random.randint(255,							size=(3, 30, 400),							dtype=np.uinta)]

            snake_case_					       =					[Image.fromarray(np.moveaxis(lowerCAmelCase__,							0,							-1)) for x in image_inputs]

            return image_inputs







       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					InstructBlipProcessor(
                tokenizer=self.get_tokenizer(),							image_processor=self.get_image_processor(),							qformer_tokenizer=self.get_qformer_tokenizer(),							)
            processor.save_pretrained(self.tmpdirname)

            snake_case_					       =					self.get_tokenizer(bos_token='(BOS)',							eos_token='(EOS)')
            snake_case_					       =					self.get_image_processor(do_normalize=lowerCAmelCase__,							padding_value=1.0)

            snake_case_					       =					InstructBlipProcessor.from_pretrained(
                self.tmpdirname,							bos_token='(BOS)',							eos_token='(EOS)',							do_normalize=lowerCAmelCase__,							padding_value=1.0)

            self.assertEqual(processor.tokenizer.get_vocab(),							tokenizer_add_kwargs.get_vocab())
            self.assertIsInstance(processor.tokenizer,							lowerCAmelCase__)

            self.assertEqual(processor.image_processor.to_json_string(),							image_processor_add_kwargs.to_json_string())
            self.assertIsInstance(processor.image_processor,							lowerCAmelCase__)
            self.assertIsInstance(processor.qformer_tokenizer,							lowerCAmelCase__)







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					image_processor(lowerCAmelCase__,							return_tensors='np')
            snake_case_					       =					processor(images=lowerCAmelCase__,							return_tensors='np')

            for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum(),							input_processor[key].sum(),							delta=1e-2)







       def 		a_						( self)      ->      str:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					'lower newer'

            snake_case_					       =					processor(text=lowerCAmelCase__)

            snake_case_					       =					tokenizer(lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__)
            snake_case_					       =					qformer_tokenizer(lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__)

            for key in encoded_tokens.keys():
                 self.assertListEqual(encoded_tokens[key],							encoded_processor[key])

            for key in encoded_tokens_qformer.keys():
                 self.assertListEqual(encoded_tokens_qformer[key],							encoded_processor['qformer_' + key])







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					'lower newer'
            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

            self.assertListEqual(
                list(inputs.keys()),							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'],							)

            # test if it raises when no input is passed
            with pytest.raises(lowerCAmelCase__):
                 processor()







       def 		a_						( self)      ->      int:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            snake_case_					       =					processor.batch_decode(lowerCAmelCase__)
            snake_case_					       =					tokenizer.batch_decode(lowerCAmelCase__)

            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.get_image_processor()
            snake_case_					       =					self.get_tokenizer()
            snake_case_					       =					self.get_qformer_tokenizer()

            snake_case_					       =					InstructBlipProcessor(
                tokenizer=lowerCAmelCase__,							image_processor=lowerCAmelCase__,							qformer_tokenizer=lowerCAmelCase__)

            snake_case_					       =					'lower newer'
            snake_case_					       =					self.prepare_image_inputs()

            snake_case_					       =					processor(text=lowerCAmelCase__,							images=lowerCAmelCase__)

            self.assertListEqual(
                list(inputs.keys()),							['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'],							)








'''simple docstring'''




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def        _lowerCAmelCase			(  __snake_case  : Tuple			)     ->     Dict:
     return {key.lstrip('-'			): value for key, value in zip(unknown_args[::2] ,							unknown_args[1::2]			)}






def        _lowerCAmelCase			(  )     ->     Tuple:
     __A						: int      =       ArgumentParser(
         'HuggingFace Datasets CLI tool' ,							usage='datasets-cli <command> [<args>]' ,							allow_abbrev=__snake_case			)
     __A						: Optional[Any]      =       parser.add_subparsers(help='datasets-cli command helpers'			)
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__snake_case			)
     EnvironmentCommand.register_subcommand(__snake_case			)
     TestCommand.register_subcommand(__snake_case			)
     RunBeamCommand.register_subcommand(__snake_case			)
     DummyDataCommand.register_subcommand(__snake_case			)

     # Parse args
     __A ,__A						: Optional[Any]      =       parser.parse_known_args()
     if not hasattr(__snake_case ,							'func'			):
          parser.print_help()
          exit(1			)
     __A						: Any      =       parse_unknown_args(__snake_case			)

     # Run
     __A						: List[Any]      =       args.func(__snake_case ,							**__snake_case			)
     service.run()


if __name__ == "__main__":
  main()