




"""simple docstring"""





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   a__					(						unittest.TestCase ):


			def __init__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     int   ,	UpperCamelCase_   :     Any=13   ,	UpperCamelCase_   :     Optional[int]=3   ,	UpperCamelCase_   :     int=224   ,	UpperCamelCase_   :     int=30   ,	UpperCamelCase_   :     str=400   ,	UpperCamelCase_   :     List[Any]=True   ,	UpperCamelCase_   :     Optional[Any]=None   ,	UpperCamelCase_   :     Optional[int]=True   ,	UpperCamelCase_   :     Optional[int]=[0.5, 0.5, 0.5]   ,	UpperCamelCase_   :     Optional[Any]=[0.5, 0.5, 0.5]   ,	):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       size if size is not None else {"height": 18, "width": 18}
						__UpperCAmelCase						:			List[Any]												=       parent
						__UpperCAmelCase						:			Tuple												=       batch_size
						__UpperCAmelCase						:			Tuple												=       num_channels
						__UpperCAmelCase						:			List[Any]												=       image_size
						__UpperCAmelCase						:			str												=       min_resolution
						__UpperCAmelCase						:			Tuple												=       max_resolution
						__UpperCAmelCase						:			Optional[Any]												=       do_resize
						__UpperCAmelCase						:			Any												=       size
						__UpperCAmelCase						:			Any												=       do_normalize
						__UpperCAmelCase						:			Any												=       image_mean
						__UpperCAmelCase						:			Optional[Any]												=       image_std


			def    a_  (   self   :     str):


						"""simple docstring"""




						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   a__					(						__magic_name__				,     unittest.TestCase ):
			lowercase_		=						ViTImageProcessor if is_vision_available() else None


			def    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       EfficientFormerImageProcessorTester(self)


			@property
			def    a_  (   self   :     Union[str, Any]):


						"""simple docstring"""




						return self.image_proc_tester.prepare_image_processor_dict()


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Tuple												=       self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCamelCase_   ,	"image_mean"))
						self.assertTrue(hasattr(UpperCamelCase_   ,	"image_std"))
						self.assertTrue(hasattr(UpperCamelCase_   ,	"do_normalize"))
						self.assertTrue(hasattr(UpperCamelCase_   ,	"do_resize"))
						self.assertTrue(hasattr(UpperCamelCase_   ,	"size"))


			def    a_  (   self   :     Dict):


						"""simple docstring"""




						pass


			def    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			Optional[Any]												=       self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						__UpperCAmelCase						:			str												=       prepare_image_inputs(self.image_proc_tester   ,	equal_resolution=UpperCamelCase_)
						for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_   ,	Image.Image)

						# Test not batched input
						__UpperCAmelCase						:			Optional[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
						__UpperCAmelCase						:			Optional[int]												=       image_processor(UpperCamelCase_   ,	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    a_  (   self   :     Tuple):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						__UpperCAmelCase						:			Union[str, Any]												=       prepare_image_inputs(self.image_proc_tester   ,	equal_resolution=UpperCamelCase_   ,	numpify=UpperCamelCase_)
						for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_   ,	np.ndarray)

						# Test not batched input
						__UpperCAmelCase						:			Tuple												=       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
						__UpperCAmelCase						:			Any												=       image_processor(UpperCamelCase_   ,	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    a_  (   self   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			Dict												=       self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						__UpperCAmelCase						:			Any												=       prepare_image_inputs(self.image_proc_tester   ,	equal_resolution=UpperCamelCase_   ,	torchify=UpperCamelCase_)
						for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_   ,	torch.Tensor)

						# Test not batched input
						__UpperCAmelCase						:			Optional[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
						__UpperCAmelCase						:			Optional[int]												=       image_processor(UpperCamelCase_   ,	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"],
						    )   ,	)



'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

