

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor



class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=7   ,	A__=3   ,	A__=18   ,	A__=30   ,	A__=400   ,	A__=True   ,	A__=None   ,	A__=True   ,	A__=None   ,	A__=True   ,	A__=[0.5, 0.5, 0.5]   ,	A__=[0.5, 0.5, 0.5]   ,	):
						A__							:   Dict					       =		size if size is not None else {"""shortest_edge""": 18}
						A__							:   Optional[int]					       =		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
						A__							:   str					       =		parent
						A__							:   Optional[int]					       =		batch_size
						A__							:   Any					       =		num_channels
						A__							:   Any					       =		image_size
						A__							:   Any					       =		min_resolution
						A__							:   Optional[Any]					       =		max_resolution
						A__							:   Dict					       =		do_resize
						A__							:   Optional[Any]					       =		size
						A__							:   Dict					       =		do_center_crop
						A__							:   str					       =		crop_size
						A__							:   List[Any]					       =		do_normalize
						A__							:   List[str]					       =		image_mean
						A__							:   Optional[int]					       =		image_std






					def   __A      (			self      ):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}



@require_torch
@require_vision
class    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[int]  							=	LevitImageProcessor if is_vision_available() else None






					def   __A      (			self      ):
						A__							:   Dict					       =		LevitImageProcessingTester(self      )






					@property
					def   __A      (			self      ):
						return self.image_processor_tester.prepare_image_processor_dict()






					def   __A      (			self      ):
						A__							:   Union[str, Any]					       =		self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(A__   ,	"""image_mean"""      )      )
						self.assertTrue(hasattr(A__   ,	"""image_std"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_normalize"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_resize"""      )      )
						self.assertTrue(hasattr(A__   ,	"""do_center_crop"""      )      )
						self.assertTrue(hasattr(A__   ,	"""size"""      )      )






					def   __A      (			self      ):
						A__							:   Dict					       =		self.image_processing_class.from_dict(self.image_processor_dict      )
						self.assertEqual(image_processor.size   ,	{"""shortest_edge""": 18}      )
						self.assertEqual(image_processor.crop_size   ,	{"""height""": 18, """width""": 18}      )

						A__							:   List[Any]					       =		self.image_processing_class.from_dict(self.image_processor_dict   ,	size=42   ,	crop_size=84      )
						self.assertEqual(image_processor.size   ,	{"""shortest_edge""": 42}      )
						self.assertEqual(image_processor.crop_size   ,	{"""height""": 84, """width""": 84}      )






					def   __A      (			self      ):
						pass






					def   __A      (			self      ):
						# Initialize image_processing
						A__							:   List[Any]					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						A__							:   int					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=A__      )
						for image in image_inputs:
							self.assertIsInstance(A__   ,	Image.Image      )

						# Test not batched input
						A__							:   Union[str, Any]					       =		image_processing(image_inputs[0]   ,	return_tensors="""pt"""      ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,	(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )   ,	)

						# Test batched
						A__							:   Optional[int]					       =		image_processing(A__   ,	return_tensors="""pt"""      ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )   ,	)






					def   __A      (			self      ):
						# Initialize image_processing
						A__							:   List[str]					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						A__							:   Optional[int]					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=A__   ,	numpify=A__      )
						for image in image_inputs:
							self.assertIsInstance(A__   ,	np.ndarray      )

						# Test not batched input
						A__							:   Tuple					       =		image_processing(image_inputs[0]   ,	return_tensors="""pt"""      ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,	(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )   ,	)

						# Test batched
						A__							:   Optional[int]					       =		image_processing(A__   ,	return_tensors="""pt"""      ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )   ,	)






					def   __A      (			self      ):
						# Initialize image_processing
						A__							:   str					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						A__							:   Tuple					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=A__   ,	torchify=A__      )
						for image in image_inputs:
							self.assertIsInstance(A__   ,	torch.Tensor      )

						# Test not batched input
						A__							:   Any					       =		image_processing(image_inputs[0]   ,	return_tensors="""pt"""      ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,	(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )   ,	)

						# Test batched
						A__							:   str					       =		image_processing(A__   ,	return_tensors="""pt"""      ).pixel_values
						self.assertEqual(
						    encoded_images.shape   ,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["""height"""],
						        self.image_processor_tester.crop_size["""width"""],
						    )   ,	)






from argparse import ArgumentParser

from . import BaseTransformersCLICommand

def 						UpperCamelCase (lowercase_:			int )					->				str:
	return DownloadCommand(args.model   ,				args.cache_dir   ,				args.force   ,				args.trust_remote_code )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		parser.add_parser("""download"""      )
						download_parser.add_argument(
						    """--cache-dir"""   ,	type=A__   ,	default=A__   ,	help="""Path to location to store the models"""      )
						download_parser.add_argument(
						    """--force"""   ,	action="""store_true"""   ,	help="""Force the model to be download even if already in cache-dir"""      )
						download_parser.add_argument(
						    """--trust-remote-code"""   ,	action="""store_true"""   ,	help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""   ,	)
						download_parser.add_argument("""model"""   ,	type=A__   ,	help="""Name of the model to download"""      )
						download_parser.set_defaults(func=A__      )






					def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__      ):
						A__							:   Union[str, Any]					       =		model
						A__							:   Dict					       =		cache
						A__							:   str					       =		force
						A__							:   Tuple					       =		trust_remote_code






					def   __A      (			self      ):
						from ..models.auto import AutoModel, AutoTokenizer

						AutoModel.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )
						AutoTokenizer.from_pretrained(
						    self._model   ,	cache_dir=self._cache   ,	force_download=self._force   ,	trust_remote_code=self._trust_remote_code      )



