

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 _UpperCamelCase							(							unittest.TestCase				):
			'''simple docstring'''







			def __init__(     self							:	List[Any] , a							:	Union[str, Any] , a							:	List[Any]=13 , a							:	Optional[Any]=3 , a							:	int=18 , a							:	int=30 , a							:	List[Any]=400 , a							:	List[Any]=True , a							:	Tuple=None , a							:	int=True , a							:	List[str]=[0.5, 0.5, 0.5] , a							:	Dict=[0.5, 0.5, 0.5] , )							->    Optional[int]:

				"""simple docstring"""

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






			def 							__UpperCamelCase	(     self							:	int		)							->    Optional[int]:

				"""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 _UpperCamelCase							(							__A							,		unittest.TestCase				):
			'''simple docstring'''







			lowerCamelCase__          =EfficientNetImageProcessor if is_vision_available() else None
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    EfficientNetImageProcessorTester(self		)
			@property
			def 							__UpperCamelCase	(     self							:	Any		)							->    Union[str, Any]:

				"""simple docstring"""

				return self.image_processor_tester.prepare_image_processor_dict()
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: 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 , "size"		)		)
			def 							__UpperCamelCase	(     self							:	Tuple		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size , {"height": 18, "width": 18}		)

				SCREAMING_SNAKE_CASE				: int			       =    self.image_processing_class.from_dict(self.image_processor_dict , size=42		)
				self.assertEqual(image_processor.size , {"height": 42, "width": 42}		)
			def 							__UpperCamelCase	(     self							:	Any		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				SCREAMING_SNAKE_CASE				: Tuple			       =    prepare_image_inputs(self.image_processor_tester , equal_resolution=a		)
				for image in image_inputs:
					self.assertIsInstance(a , Image.Image		)

				# Test not batched input
				SCREAMING_SNAKE_CASE				: Dict			       =    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
				SCREAMING_SNAKE_CASE				: Union[str, 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 							__UpperCamelCase	(     self							:	List[str]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				SCREAMING_SNAKE_CASE				: Union[str, 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
				SCREAMING_SNAKE_CASE				: 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
				SCREAMING_SNAKE_CASE				: 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.size["height"],
				        self.image_processor_tester.size["width"],
				    ) , )






			def 							__UpperCamelCase	(     self							:	Tuple		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    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
				SCREAMING_SNAKE_CASE				: str			       =    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
				SCREAMING_SNAKE_CASE				: Optional[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"],
				    ) , )


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


a_         =      logging.get_logger(__name__)

a_         =      {
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class _UpperCamelCase							(							__A				):
			'''simple docstring'''







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets