




'''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 ViTImageProcessor

class   _a							(		unittest.TestCase				):






     def __init__(					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=13   ,_SCREAMING_SNAKE_CASE=3   ,_SCREAMING_SNAKE_CASE=224   ,_SCREAMING_SNAKE_CASE=30   ,_SCREAMING_SNAKE_CASE=400   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=True   ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]   ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]   ,)				->     Optional[Any]:
          _snake_case				= size if size is not None else {"height": 18, "width": 18}
          _snake_case				= parent
          _snake_case				= batch_size
          _snake_case				= num_channels
          _snake_case				= image_size
          _snake_case				= min_resolution
          _snake_case				= max_resolution
          _snake_case				= do_resize
          _snake_case				= size
          _snake_case				= do_normalize
          _snake_case				= image_mean
          _snake_case				= image_std






     def 							_lowercase      (					self       )				->     int:
          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   _a							(		__lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		ViTImageProcessor if is_vision_available() else None






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= EfficientFormerImageProcessorTester(self       )






     @property
     def 							_lowercase      (					self       )				->     Optional[int]:
          return self.image_proc_tester.prepare_image_processor_dict()






     def 							_lowercase      (					self       )				->     Dict:
          _snake_case				= self.image_processing_class(**self.image_processor_dict       )
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,"image_mean"       )       )
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,"image_std"       )       )
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,"do_normalize"       )       )
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,"do_resize"       )       )
          self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE   ,"size"       )       )






     def 							_lowercase      (					self       )				->     List[str]:
          pass






     def 							_lowercase      (					self       )				->     Any:
          # Initialize image_processor
          _snake_case				= self.image_processing_class(**self.image_processor_dict       )
          # create random PIL images
          _snake_case				= prepare_image_inputs(self.image_proc_tester   ,equal_resolution=_SCREAMING_SNAKE_CASE       )
          for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,Image.Image       )

          # Test not batched input
          _snake_case				= image_processor(image_inputs[0]   ,return_tensors="pt"       ).pixel_values
          self.assertEqual(
              encoded_images.shape   ,(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )   ,)

          # Test batched
          _snake_case				= image_processor(_SCREAMING_SNAKE_CASE   ,return_tensors="pt"       ).pixel_values
          self.assertEqual(
              encoded_images.shape   ,(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )   ,)






     def 							_lowercase      (					self       )				->     Optional[int]:
          # Initialize image_processor
          _snake_case				= self.image_processing_class(**self.image_processor_dict       )
          # create random numpy tensors
          _snake_case				= prepare_image_inputs(self.image_proc_tester   ,equal_resolution=_SCREAMING_SNAKE_CASE   ,numpify=_SCREAMING_SNAKE_CASE       )
          for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,np.ndarray       )

          # Test not batched input
          _snake_case				= image_processor(image_inputs[0]   ,return_tensors="pt"       ).pixel_values
          self.assertEqual(
              encoded_images.shape   ,(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )   ,)

          # Test batched
          _snake_case				= image_processor(_SCREAMING_SNAKE_CASE   ,return_tensors="pt"       ).pixel_values
          self.assertEqual(
              encoded_images.shape   ,(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )   ,)






     def 							_lowercase      (					self       )				->     List[str]:
          # Initialize image_processor
          _snake_case				= self.image_processing_class(**self.image_processor_dict       )
          # create random PyTorch tensors
          _snake_case				= prepare_image_inputs(self.image_proc_tester   ,equal_resolution=_SCREAMING_SNAKE_CASE   ,torchify=_SCREAMING_SNAKE_CASE       )
          for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,torch.Tensor       )

          # Test not batched input
          _snake_case				= image_processor(image_inputs[0]   ,return_tensors="pt"       ).pixel_values
          self.assertEqual(
              encoded_images.shape   ,(
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )   ,)

          # Test batched
          _snake_case				= image_processor(_SCREAMING_SNAKE_CASE   ,return_tensors="pt"       ).pixel_values
          self.assertEqual(
              encoded_images.shape   ,(
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size["height"],
                  self.image_proc_tester.size["width"],
              )   ,)








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_		:     Optional[int]          =		logging.get_logger(__name__)

UpperCamelCase_		:     str          =		{
    '''kssteven/ibert-roberta-base''': '''https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large''': '''https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json''',
    '''kssteven/ibert-roberta-large-mnli''': (
        '''https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'''
    ),
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		"""ibert"""






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=30_522   ,_SCREAMING_SNAKE_CASE=768   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=12   ,_SCREAMING_SNAKE_CASE=3_072   ,_SCREAMING_SNAKE_CASE="gelu"   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=0.1   ,_SCREAMING_SNAKE_CASE=512   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE=0.0_2   ,_SCREAMING_SNAKE_CASE=1e-12   ,_SCREAMING_SNAKE_CASE=1   ,_SCREAMING_SNAKE_CASE=0   ,_SCREAMING_SNAKE_CASE=2   ,_SCREAMING_SNAKE_CASE="absolute"   ,_SCREAMING_SNAKE_CASE=False   ,_SCREAMING_SNAKE_CASE="none"   ,**_SCREAMING_SNAKE_CASE   ,)				->     Dict:
          super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE   ,bos_token_id=_SCREAMING_SNAKE_CASE   ,eos_token_id=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

          _snake_case				= vocab_size
          _snake_case				= hidden_size
          _snake_case				= num_hidden_layers
          _snake_case				= num_attention_heads
          _snake_case				= hidden_act
          _snake_case				= intermediate_size
          _snake_case				= hidden_dropout_prob
          _snake_case				= attention_probs_dropout_prob
          _snake_case				= max_position_embeddings
          _snake_case				= type_vocab_size
          _snake_case				= initializer_range
          _snake_case				= layer_norm_eps
          _snake_case				= position_embedding_type
          _snake_case				= quant_mode
          _snake_case				= force_dequant







class   _a							(		__lowerCAmelCase				):






     @property
     def 							_lowercase      (					self       )				->     Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
               _snake_case				= {0: "batch", 1: "choice", 2: "sequence"}
          else:
               _snake_case				= {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]       )


