
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 PoolFormerImageProcessor





class 				UpperCAmelCase (    unittest.TestCase    ):




	'''simple docstring'''





	def __init__(			self   :					Any				,A   :					Dict				,A   :					List[Any]=7				,A   :					Any=3				,A   :					List[Any]=30				,A   :					List[Any]=4_00				,A   :					Union[str, Any]=True				,A   :					List[str]=None				,A   :					Optional[Any]=0.9				,A   :					Any=None				,A   :					List[Any]=True				,A   :					List[Any]=[0.5, 0.5, 0.5]				,A   :					Union[str, Any]=[0.5, 0.5, 0.5]				,):
					__A       =					size if size is not None else {"shortest_edge": 30}
					__A       =					crop_size if crop_size is not None else {"height": 30, "width": 30}
					__A       =					parent
					__A       =					batch_size
					__A       =					num_channels
					__A       =					min_resolution
					__A       =					max_resolution
					__A       =					do_resize_and_center_crop
					__A       =					size
					__A       =					crop_pct
					__A       =					crop_size
					__A       =					do_normalize
					__A       =					image_mean
					__A       =					image_std







	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return {
					    "size": self.size,
					    "do_resize_and_center_crop": self.do_resize_and_center_crop,
					    "crop_pct": self.crop_pct,
					    "crop_size": self.crop_size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					}





@require_torch
@require_vision
class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE     ,     unittest.TestCase    ):




	'''simple docstring'''





	snake_case_          =	PoolFormerImageProcessor if is_vision_available() else None






	def 							UpperCamelCase_    (			self   :					Optional[Any]					):
					__A       =					PoolFormerImageProcessingTester(self					)






	@property
	def 							UpperCamelCase_    (			self   :					Optional[int]					):
					return self.image_processor_tester.prepare_image_processor_dict()






	def 							UpperCamelCase_    (			self   :					List[Any]					):
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					self.assertTrue(hasattr(A				,"do_resize_and_center_crop"					)					)
					self.assertTrue(hasattr(A				,"size"					)					)
					self.assertTrue(hasattr(A				,"crop_pct"					)					)
					self.assertTrue(hasattr(A				,"do_normalize"					)					)
					self.assertTrue(hasattr(A				,"image_mean"					)					)
					self.assertTrue(hasattr(A				,"image_std"					)					)






	def 							UpperCamelCase_    (			self   :					Tuple					):
					__A       =					self.image_processing_class.from_dict(self.image_processor_dict					)
					self.assertEqual(image_processor.size				,{"shortest_edge": 30}					)
					self.assertEqual(image_processor.crop_size				,{"height": 30, "width": 30}					)

					__A       =					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 							UpperCamelCase_    (			self   :					Optional[Any]					):
					pass






	def 							UpperCamelCase_    (			self   :					str					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PIL images
					__A       =					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       =					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       =					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 							UpperCamelCase_    (			self   :					Union[str, Any]					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random numpy tensors
					__A       =					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       =					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       =					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 							UpperCamelCase_    (			self   :					Any					):
					# Initialize image_processing
					__A       =					self.image_processing_class(**self.image_processor_dict					)
					# create random PyTorch tensors
					__A       =					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       =					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       =					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"],
					    )				,)

'''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

