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 GLPNImageProcessor






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      str=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      str=4_00							,							__lowerCamelCase				:      Optional[Any]=True							,							__lowerCamelCase				:      str=32							,							__lowerCamelCase				:      Optional[Any]=True							,							)							->		str:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		image_size
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		size_divisor
									lowercase							=		do_rescale



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


									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size_divisor": self.size_divisor,
									    "do_rescale": self.do_rescale,
									}






@require_torch
@require_vision
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     GLPNImageProcessor if is_vision_available() else None

						def        __a		(	self				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		GLPNImageProcessingTester(self   )

						@property
						def        __a		(	self				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_resize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''size_divisor'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''resample'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_rescale'''   )   )

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									pass

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


									'''simple docstring'''

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

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )

						def        __a		(	self				:      int   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							numpify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							np.ndarray   )

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )



						def        __a		(	self				:      List[str]   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									lowercase							=		prepare_image_inputs(self.image_processor_tester							,							equal_resolution=__lowerCamelCase							,							torchify=__lowerCamelCase   )
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase							,							torch.Tensor   )

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )






import logging

from transformers import PretrainedConfig


A_      =      logging.getLogger(__name__)


A_      =      {
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}






class      __lowercase					(    _A    ):
						lowercase					 =     'bertabs'

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Tuple=3_05_22							,							__lowerCamelCase				:      Tuple=5_12							,							__lowerCamelCase				:      List[Any]=6							,							__lowerCamelCase				:      Any=5_12							,							__lowerCamelCase				:      Any=8							,							__lowerCamelCase				:      Union[str, Any]=5_12							,							__lowerCamelCase				:      Tuple=0.2							,							__lowerCamelCase				:      str=6							,							__lowerCamelCase				:      int=7_68							,							__lowerCamelCase				:      int=8							,							__lowerCamelCase				:      List[Any]=20_48							,							__lowerCamelCase				:      Union[str, Any]=0.2							,							**__lowerCamelCase				:      Dict							,							)							->		Dict:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )

									lowercase							=		vocab_size
									lowercase							=		max_pos

									lowercase							=		enc_layers
									lowercase							=		enc_hidden_size
									lowercase							=		enc_heads
									lowercase							=		enc_ff_size
									lowercase							=		enc_dropout

									lowercase							=		dec_layers
									lowercase							=		dec_hidden_size
									lowercase							=		dec_heads
									lowercase							=		dec_ff_size
									lowercase							=		dec_dropout





