
"""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 LevitImageProcessor






class    _UpperCAmelCase(   unittest.TestCase     ):



		def __init__( self    ,		__a    ,		__a=7    ,		__a=3    ,		__a=18    ,		__a=30    ,		__a=4_00    ,		__a=True    ,		__a=None    ,		__a=True    ,		__a=None    ,		__a=True    ,		__a=[0.5, 0.5, 0.5]    ,		__a=[0.5, 0.5, 0.5]    ,		)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  size if size is not None else {'''shortest_edge''': 18}
							_UpperCamelCase										=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							_UpperCamelCase										=  parent
							_UpperCamelCase										=  batch_size
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  image_size
							_UpperCamelCase										=  min_resolution
							_UpperCamelCase										=  max_resolution
							_UpperCamelCase										=  do_resize
							_UpperCamelCase										=  size
							_UpperCamelCase										=  do_center_crop
							_UpperCamelCase										=  crop_size
							_UpperCamelCase										=  do_normalize
							_UpperCamelCase										=  image_mean
							_UpperCamelCase										=  image_std




		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "do_center_crop": self.do_center_crop,
							    "size": self.size,
							    "crop_size": self.crop_size,
							}






@require_torch
@require_vision
class    _UpperCAmelCase(   lowerCamelCase				,					unittest.TestCase     ):
		lowercase__      =				LevitImageProcessor if is_vision_available() else None



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






							'''simple docstring'''
							_UpperCamelCase										=  LevitImageProcessingTester(self)



		@property
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()



		def 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  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    ,		'''do_center_crop'''))
							self.assertTrue(hasattr(__a    ,		'''size'''))



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size    ,		{'''shortest_edge''': 18})
							self.assertEqual(image_processor.crop_size    ,		{'''height''': 18, '''width''': 18})

							_UpperCamelCase										=  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)	->	Union[str, Any]:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							_UpperCamelCase										=  prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=__a)
							for image in image_inputs:
												self.assertIsInstance(__a    ,		Image.Image)

							# Test not batched input
							_UpperCamelCase										=  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
							_UpperCamelCase										=  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)	->	int:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							_UpperCamelCase										=  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
							_UpperCamelCase										=  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
							_UpperCamelCase										=  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)	->	List[Any]:






							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase										=  self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							_UpperCamelCase										=  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
							_UpperCamelCase										=  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
							_UpperCamelCase										=  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


_a														=		logging.get_logger(__name__)

_a														=		{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'biogpt'



		def __init__( self    ,		__a=4_23_84    ,		__a=10_24    ,		__a=24    ,		__a=16    ,		__a=40_96    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=10_24    ,		__a=0.02    ,		__a=1e-12    ,		__a=True    ,		__a=True    ,		__a=0.0    ,		__a=0.0    ,		__a=1    ,		__a=0    ,		__a=2    ,		**__a    ,		)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  scale_embedding
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  layerdrop
							_UpperCamelCase										=  activation_dropout
							super().__init__(pad_token_id=__a    ,		bos_token_id=__a    ,		eos_token_id=__a    ,		**__a)






