






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 ViTImageProcessor




class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE=13		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=30		,				_SCREAMING_SNAKE_CASE=400		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]		,				_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]		,				) ->				Optional[int]:
								snake_case_				:							List[str]						 =       size if size is not None else {"height": 18, "width": 18}
								snake_case_				:							Optional[int]						 =       parent
								snake_case_				:							str						 =       batch_size
								snake_case_				:							List[str]						 =       num_channels
								snake_case_				:							Any						 =       image_size
								snake_case_				:							Any						 =       min_resolution
								snake_case_				:							Any						 =       max_resolution
								snake_case_				:							Any						 =       do_resize
								snake_case_				:							Optional[int]						 =       size
								snake_case_				:							str						 =       do_normalize
								snake_case_				:							Any						 =       image_mean
								snake_case_				:							Union[str, Any]						 =       image_std






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}




@require_torch
@require_vision
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[int]						=					ViTImageProcessor if is_vision_available() else None






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							Tuple						 =       EfficientFormerImageProcessorTester(self				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								return self.image_proc_tester.prepare_image_processor_dict()






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							List[str]						 =       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		,				"size"				)				)






						def       _lowerCAmelCase       (	self				) ->				int:
								pass






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								# Initialize image_processor
								snake_case_				:							Optional[int]						 =       self.image_processing_class(**self.image_processor_dict				)
								# create random PIL images
								snake_case_				:							Optional[int]						 =       prepare_image_inputs(self.image_proc_tester		,				equal_resolution=__A				)
								for image in image_inputs:
										self.assertIsInstance(__A		,				Image.Image				)

								# Test not batched input
								snake_case_				:							int						 =       image_processor(image_inputs[0]		,				return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,				(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["height"],
								        self.image_proc_tester.size["width"],
								    )		,				)

								# Test batched
								snake_case_				:							Any						 =       image_processor(__A		,				return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,				(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["height"],
								        self.image_proc_tester.size["width"],
								    )		,				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								# Initialize image_processor
								snake_case_				:							Dict						 =       self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								snake_case_				:							int						 =       prepare_image_inputs(self.image_proc_tester		,				equal_resolution=__A		,				numpify=__A				)
								for image in image_inputs:
										self.assertIsInstance(__A		,				np.ndarray				)

								# Test not batched input
								snake_case_				:							Union[str, Any]						 =       image_processor(image_inputs[0]		,				return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,				(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["height"],
								        self.image_proc_tester.size["width"],
								    )		,				)

								# Test batched
								snake_case_				:							List[Any]						 =       image_processor(__A		,				return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,				(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["height"],
								        self.image_proc_tester.size["width"],
								    )		,				)






						def       _lowerCAmelCase       (	self				) ->				Any:
								# Initialize image_processor
								snake_case_				:							Union[str, Any]						 =       self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								snake_case_				:							Any						 =       prepare_image_inputs(self.image_proc_tester		,				equal_resolution=__A		,				torchify=__A				)
								for image in image_inputs:
										self.assertIsInstance(__A		,				torch.Tensor				)

								# Test not batched input
								snake_case_				:							Any						 =       image_processor(image_inputs[0]		,				return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,				(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["height"],
								        self.image_proc_tester.size["width"],
								    )		,				)

								# Test batched
								snake_case_				:							List[Any]						 =       image_processor(__A		,				return_tensors="pt"				).pixel_values
								self.assertEqual(
								    encoded_images.shape		,				(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size["height"],
								        self.image_proc_tester.size["width"],
								    )		,				)








import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str  ,      **_a       :			Optional[int]    ):
		snake_case_				:							Any						 =       AutoConfig.from_pretrained(_a  ,      **_a    )
		snake_case_				:							str						 =       AutoModelForSeqaSeqLM.from_config(_a    )
		model.save_pretrained(_a    )
		AutoTokenizer.from_pretrained(_a    ).save_pretrained(_a    )
		return model


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