

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


_UpperCAmelCase    	=			logging.get_logger(__name__)

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



class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				'''mpnet'''







  def __init__( self	: int		,						UpperCamelCase__	: Optional[Any]=30527		,						UpperCamelCase__	: int=768		,						UpperCamelCase__	: Union[str, Any]=12		,						UpperCamelCase__	: str=12		,						UpperCamelCase__	: Dict=3072		,						UpperCamelCase__	: Any="gelu"		,						UpperCamelCase__	: List[Any]=0.1		,						UpperCamelCase__	: List[Any]=0.1		,						UpperCamelCase__	: str=512		,						UpperCamelCase__	: Any=0.02		,						UpperCamelCase__	: List[str]=1e-1_2		,						UpperCamelCase__	: Optional[Any]=32		,						UpperCamelCase__	: List[Any]=1		,						UpperCamelCase__	: Any=0		,						UpperCamelCase__	: int=2		,						**UpperCamelCase__	: int		,						):
        super().__init__(pad_token_id=UpperCamelCase__		,						bos_token_id=UpperCamelCase__		,						eos_token_id=UpperCamelCase__		,						**UpperCamelCase__ )

        A    =				vocab_size
        A    =				hidden_size
        A    =				num_hidden_layers
        A    =				num_attention_heads
        A    =				hidden_act
        A    =				intermediate_size
        A    =				hidden_dropout_prob
        A    =				attention_probs_dropout_prob
        A    =				max_position_embeddings
        A    =				initializer_range
        A    =				layer_norm_eps
        A    =				relative_attention_num_buckets






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







  def 	UpperCamelCase       ( self	: str ):
        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 (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				Dict					      =				EfficientNetImageProcessor if is_vision_available() else None







  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				EfficientNetImageProcessorTester(self )







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







  def 	UpperCamelCase       ( self	: List[str] ):
        A    =				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 	UpperCamelCase       ( self	: Union[str, Any] ):
        A    =				self.image_processing_class.from_dict(self.image_processor_dict )
        self.assertEqual(image_processor.size		,						{'height': 18, 'width': 18} )

        A    =				self.image_processing_class.from_dict(self.image_processor_dict		,						size=42 )
        self.assertEqual(image_processor.size		,						{'height': 42, 'width': 42} )







  def 	UpperCamelCase       ( self	: Optional[int] ):
        # 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=UpperCamelCase__ )
        for image in image_inputs:
              self.assertIsInstance(UpperCamelCase__		,						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.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)

        # Test batched
        A    =				image_processing(UpperCamelCase__		,						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	: Optional[int] ):
        # 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=UpperCamelCase__		,						numpify=UpperCamelCase__ )
        for image in image_inputs:
              self.assertIsInstance(UpperCamelCase__		,						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.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)

        # Test batched
        A    =				image_processing(UpperCamelCase__		,						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	: Optional[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=UpperCamelCase__		,						torchify=UpperCamelCase__ )
        for image in image_inputs:
              self.assertIsInstance(UpperCamelCase__		,						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.size['height'],
                self.image_processor_tester.size['width'],
            )		,						)

        # Test batched
        A    =				image_processing(UpperCamelCase__		,						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'],
            )		,						)



