


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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor




class A    (  unittest.TestCase	):






      def __init__(   self		:	List[str]     ,  lowercase_		:	Any     ,  lowercase_		:	List[Any]=7     ,  lowercase_		:	List[str]=3     ,  lowercase_		:	str=18     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	Optional[int]=400     ,  lowercase_		:	Dict=True     ,  lowercase_		:	List[str]=None     ,  lowercase_		:	int=True     ,  lowercase_		:	Tuple=None     ,  lowercase_		:	int=True     ,  lowercase_		:	Tuple=[0.48145466, 0.4578275, 0.40821073]     ,  lowercase_		:	Optional[int]=[0.26862954, 0.26130258, 0.27577711]     ,  lowercase_		:	Any=True     ,  )   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =size if size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						Tuple            =crop_size if crop_size is not None else {'height': 18, 'width': 18}
             _lowerCamelCase			:						Tuple            =parent
             _lowerCamelCase			:						Any            =batch_size
             _lowerCamelCase			:						Optional[Any]            =num_channels
             _lowerCamelCase			:						Dict            =image_size
             _lowerCamelCase			:						Tuple            =min_resolution
             _lowerCamelCase			:						List[Any]            =max_resolution
             _lowerCamelCase			:						int            =do_resize
             _lowerCamelCase			:						Dict            =size
             _lowerCamelCase			:						Any            =do_center_crop
             _lowerCamelCase			:						List[Any]            =crop_size
             _lowerCamelCase			:						Any            =do_normalize
             _lowerCamelCase			:						Dict            =image_mean
             _lowerCamelCase			:						List[str]            =image_std
             _lowerCamelCase			:						int            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_convert_rgb": self.do_convert_rgb,
             }






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	Tuple=False     ,  lowercase_		:	List[Any]=False     ,  lowercase_		:	str=False					)   ->		Union[str, Any]:
             """simple docstring"""




             assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

             if equal_resolution:
                    _lowerCamelCase			:						Any            =[]
                    for i in range(self.batch_size					):
                           image_inputs.append(
                               np.random.randint(
                                   255     ,  size=(self.num_channels, self.max_resolution, self.max_resolution)     ,  dtype=np.uinta					)					)
             else:
                    _lowerCamelCase			:						str            =[]
                    for i in range(self.batch_size					):
                           _lowerCamelCase      ,	_lowerCamelCase			:						Dict            =np.random.choice(np.arange(self.min_resolution     ,  self.max_resolution					)     ,  2					)
                           image_inputs.append(np.random.randint(255     ,  size=(self.num_channels, width, height)     ,  dtype=np.uinta					)					)

             if not numpify and not torchify:
                    # PIL expects the channel dimension as last dimension
                    _lowerCamelCase			:						Any            =[Image.fromarray(np.moveaxis(lowercase__     ,  0     ,  -1					)					) for x in image_inputs]

             if torchify:
                    _lowerCamelCase			:						List[str]            =[torch.from_numpy(lowercase__					) for x in image_inputs]

             return image_inputs




@require_torch
@require_vision
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    str =ChineseCLIPImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =ChineseCLIPImageProcessingTester(self     ,  do_center_crop=lowercase__					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Tuple:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase__     ,  'do_resize'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'do_center_crop'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'center_crop'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'image_std'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'do_convert_rgb'					)					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'height': 224, 'width': 224}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 18, 'width': 18}					)

             _lowerCamelCase			:						List[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		:	List[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Union[str, Any]            =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase__     ,  Image.Image					)

             # 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			:						str            =image_processing(lowercase__     ,  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		:	Any					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[int]            =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,  numpify=lowercase__					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase__     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						int            =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			:						List[str]            =image_processing(lowercase__     ,  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					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						str            =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__     ,  torchify=lowercase__					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase__     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						Union[str, 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			:						List[str]            =image_processing(lowercase__     ,  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'],
                 )     ,  )




@require_torch
@require_vision
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    int =ChineseCLIPImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =ChineseCLIPImageProcessingTester(self     ,  num_channels=4     ,  do_center_crop=lowercase__					)
             _lowerCamelCase			:						List[str]            =3






      @property
      def 			lowerCamelCase			(   self		:	Tuple					)   ->		str:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase__     ,  'do_resize'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'do_center_crop'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'center_crop'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'image_std'					)					)
             self.assertTrue(hasattr(lowercase__     ,  'do_convert_rgb'					)					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Tuple            =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase__					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase__     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						int            =image_processing(image_inputs[0]     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     1,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )     ,  )

             # Test batched
             _lowerCamelCase			:						Optional[Any]            =image_processing(lowercase__     ,  return_tensors='pt'					).pixel_values
             self.assertEqual(
                 encoded_images.shape     ,  (
                     self.image_processor_tester.batch_size,
                     self.expected_encoded_image_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


_lowercase:	Optional[Any]       			=		logging.get_logger(__name__)

_lowercase:	Tuple       			=		{
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="roberta-prelayernorm"





			def __init__(       self	:	int  ,  lowercase__	:	int=5_02_65  ,  lowercase__	:	str=7_68  ,  lowercase__	:	List[Any]=12  ,  lowercase__	:	Any=12  ,  lowercase__	:	Optional[Any]=30_72  ,  lowercase__	:	List[Any]="gelu"  ,  lowercase__	:	str=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Tuple=5_12  ,  lowercase__	:	Optional[int]=2  ,  lowercase__	:	Dict=0.0_2  ,  lowercase__	:	Tuple=1e-12  ,  lowercase__	:	Optional[int]=1  ,  lowercase__	:	Optional[int]=0  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Any="absolute"  ,  lowercase__	:	Any=True  ,  lowercase__	:	Any=None  ,  **lowercase__	:	Optional[int]  ,  ):
										super().__init__(pad_token_id=lowercase__  ,  bos_token_id=lowercase__  ,  eos_token_id=lowercase__  ,  **lowercase__						)

										_lowerCAmelCase							=							vocab_size
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							max_position_embeddings
										_lowerCAmelCase							=							type_vocab_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							layer_norm_eps
										_lowerCAmelCase							=							position_embedding_type
										_lowerCAmelCase							=							use_cache
										_lowerCAmelCase							=							classifier_dropout







class        lowerCamelCase__				(   UpperCAmelCase				):





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										if self.task == "multiple-choice":
																	_lowerCAmelCase							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
										else:
																	_lowerCAmelCase							=							{0: 'batch', 1: 'sequence'}
										return OrderedDict(
										    [
										        ('input_ids', dynamic_axis),
										        ('attention_mask', dynamic_axis),
										    ]						)






