

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 EfficientNetImageProcessor






class     lowerCamelCase_			(  unittest.TestCase  ):




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




	def 					lowerCAmelCase_					(    self	:    Tuple       ):
		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     lowerCamelCase_			(  _lowercase	,			unittest.TestCase  ):
	_lowercase		:      List[Any]    				=							EfficientNetImageProcessor if is_vision_available() else None




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		__A   : Union[str, Any]								= EfficientNetImageProcessorTester(self       )




	@property
	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		return self.image_processor_tester.prepare_image_processor_dict()




	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
		__A   : 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	:    str       ):
		__A   : List[str]								= self.image_processing_class.from_dict(self.image_processor_dict       )
		self.assertEqual(image_processor.size				,			{"""height""": 18, """width""": 18}       )

		__A   : Tuple								= self.image_processing_class.from_dict(self.image_processor_dict				,			size=42       )
		self.assertEqual(image_processor.size				,			{"""height""": 42, """width""": 42}       )




	def 					lowerCAmelCase_					(    self	:    Dict       ):
		# Initialize image_processing
		__A   : List[Any]								= self.image_processing_class(**self.image_processor_dict       )
		# create random PIL images
		__A   : str								= 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   : Optional[int]								= 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.size["""height"""],
		        self.image_processor_tester.size["""width"""],
		    )				,			)

		# Test batched
		__A   : Dict								= 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.size["""height"""],
		        self.image_processor_tester.size["""width"""],
		    )				,			)




	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
		# Initialize image_processing
		__A   : int								= self.image_processing_class(**self.image_processor_dict       )
		# create random numpy tensors
		__A   : Optional[Any]								= 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.size["""height"""],
		        self.image_processor_tester.size["""width"""],
		    )				,			)

		# Test batched
		__A   : Any								= 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.size["""height"""],
		        self.image_processor_tester.size["""width"""],
		    )				,			)




	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
		# Initialize image_processing
		__A   : Union[str, Any]								= 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   : 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.size["""height"""],
		        self.image_processor_tester.size["""width"""],
		    )				,			)

		# Test batched
		__A   : Tuple								= 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.size["""height"""],
		        self.image_processor_tester.size["""width"""],
		    )				,			)








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


UpperCAmelCase_       :				Optional[Any]		   =      logging.get_logger(__name__)

UpperCAmelCase_       :				List[str]		   =      {
    '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}






class     lowerCamelCase_			(  _lowercase  ):
	_lowercase		:      Optional[Any]    				=							'''nystromformer'''




	def __init__(    self	:    Optional[Any]				,			__A	:    Optional[Any]=3_0000				,			__A	:    Optional[int]=768				,			__A	:    List[str]=12				,			__A	:    str=12				,			__A	:    Tuple=3072				,			__A	:    List[Any]="gelu_new"				,			__A	:    Dict=0.1				,			__A	:    Optional[int]=0.1				,			__A	:    int=510				,			__A	:    Union[str, Any]=2				,			__A	:    Optional[int]=64				,			__A	:    str=64				,			__A	:    List[str]=65				,			__A	:    Tuple=False				,			__A	:    List[str]=0.0_2				,			__A	:    Tuple=1e-5				,			__A	:    List[str]=1				,			__A	:    int=0				,			__A	:    Tuple=2				,			**__A	:    Dict				,			):
		__A   : str								= vocab_size
		__A   : Union[str, Any]								= max_position_embeddings
		__A   : Optional[int]								= hidden_size
		__A   : Optional[int]								= num_hidden_layers
		__A   : List[Any]								= num_attention_heads
		__A   : Dict								= intermediate_size
		__A   : Union[str, Any]								= hidden_act
		__A   : int								= hidden_dropout_prob
		__A   : Tuple								= attention_probs_dropout_prob
		__A   : Optional[Any]								= initializer_range
		__A   : int								= type_vocab_size
		__A   : Optional[int]								= segment_means_seq_len
		__A   : List[str]								= num_landmarks
		__A   : Dict								= conv_kernel_size
		__A   : Union[str, Any]								= inv_coeff_init_option
		__A   : Union[str, Any]								= layer_norm_eps
		super().__init__(pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			**__A       )





