



'''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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						Union[str, Any]:







 """simple docstring"""
 return {key.lstrip("""-"""							): value for key, value in zip(unknown_args[::2]							,		unknown_args[1::2]							)}






def 		_snake_case		( )      ->						Tuple:







 """simple docstring"""
 lowerCAmelCase   =     ArgumentParser(
     """HuggingFace Datasets CLI tool"""							,		usage="""datasets-cli <command> [<args>]"""							,		allow_abbrev=_SCREAMING_SNAKE_CASE							)
 lowerCAmelCase   =     parser.add_subparsers(help="""datasets-cli command helpers"""							)
 set_verbosity_info()

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

 # Parse args
 lowerCAmelCase,       lowerCAmelCase   =     parser.parse_known_args()
 if not hasattr(_SCREAMING_SNAKE_CASE							,		"""func"""							):
  parser.print_help()
  exit(1							)
 lowerCAmelCase   =     parse_unknown_args(_SCREAMING_SNAKE_CASE							)

 # Run
 lowerCAmelCase   =     args.func(_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)
 service.run()


if __name__ == "__main__":
    main()




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)