


'''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     _lowercase				(							unittest.TestCase):


       """simple docstring"""




       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    List[Any]=7      ,   __lowerCamelCase							:    Any=3      ,   __lowerCamelCase							:    Union[str, Any]=18      ,   __lowerCamelCase							:    Optional[int]=30      ,   __lowerCamelCase							:    Dict=400      ,   __lowerCamelCase							:    Dict=True      ,   __lowerCamelCase							:    Tuple=None      ,   __lowerCamelCase							:    str=True      ,   __lowerCamelCase							:    List[str]=None      ,   __lowerCamelCase							:    Optional[int]=True      ,   __lowerCamelCase							:    List[str]=[0.5, 0.5, 0.5]      ,   __lowerCamelCase							:    Optional[Any]=[0.5, 0.5, 0.5]      ,   ):


             '''simple docstring'''



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





       def 		lowerCAmelCase  (     self							:    str ):


             '''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     _lowercase				(							lowercase_							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    LevitImageProcessor if is_vision_available() else None





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						LevitImageProcessingTester(self )





       @property
       def 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.image_processing_class(**self.image_processor_dict )
             self.assertTrue(hasattr(__lowerCamelCase      ,   "image_mean" ) )
             self.assertTrue(hasattr(__lowerCamelCase      ,   "image_std" ) )
             self.assertTrue(hasattr(__lowerCamelCase      ,   "do_normalize" ) )
             self.assertTrue(hasattr(__lowerCamelCase      ,   "do_resize" ) )
             self.assertTrue(hasattr(__lowerCamelCase      ,   "do_center_crop" ) )
             self.assertTrue(hasattr(__lowerCamelCase      ,   "size" ) )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						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} )

             lowerCamelCase__	:       Union[str, Any]					=						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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



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

             # Test not batched input
             lowerCamelCase__	:       List[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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )      ,   )

             # Test batched
             lowerCamelCase__	:       Dict					=						image_processing(__lowerCamelCase      ,   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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.image_processing_class(**self.image_processor_dict )
             # create random numpy tensors
             lowerCamelCase__	:       List[str]					=						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
             lowerCamelCase__	:       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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )      ,   )

             # Test batched
             lowerCamelCase__	:       Union[str, Any]					=						image_processing(__lowerCamelCase      ,   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 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						self.image_processing_class(**self.image_processor_dict )
             # create random PyTorch tensors
             lowerCamelCase__	:       Any					=						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
             lowerCamelCase__	:       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.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )      ,   )

             # Test batched
             lowerCamelCase__	:       Dict					=						image_processing(__lowerCamelCase      ,   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"],
                 )      ,   )






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   :					Dict	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      {
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "roberta"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Dict=50265      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    int=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    Optional[int]=1E-1_2      ,   __lowerCamelCase							:    List[Any]=1      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Any=2      ,   __lowerCamelCase							:    Tuple="absolute"      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    str=None      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



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

             lowerCamelCase__	:       List[Any]					=						vocab_size
             lowerCamelCase__	:       str					=						hidden_size
             lowerCamelCase__	:       int					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       Optional[Any]					=						hidden_act
             lowerCamelCase__	:       Any					=						intermediate_size
             lowerCamelCase__	:       Tuple					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       int					=						type_vocab_size
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       Dict					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       Any					=						use_cache
             lowerCamelCase__	:       int					=						classifier_dropout







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__	:       int					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__	:       Optional[Any]					=						{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ] )


