
"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case          =						{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	pass

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_snake_case          =						[
	    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RoCBertForCausalLM''',
	    '''RoCBertForMaskedLM''',
	    '''RoCBertForMultipleChoice''',
	    '''RoCBertForPreTraining''',
	    '''RoCBertForQuestionAnswering''',
	    '''RoCBertForSequenceClassification''',
	    '''RoCBertForTokenClassification''',
	    '''RoCBertLayer''',
	    '''RoCBertModel''',
	    '''RoCBertPreTrainedModel''',
	    '''load_tf_weights_in_roc_bert''',
	]

if TYPE_CHECKING:
	from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
	from .tokenization_roc_bert import RoCBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		raise OptionalDependencyNotAvailable()

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roc_bert import (
		    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RoCBertForCausalLM,
		    RoCBertForMaskedLM,
		    RoCBertForMultipleChoice,
		    RoCBertForPreTraining,
		    RoCBertForQuestionAnswering,
		    RoCBertForSequenceClassification,
		    RoCBertForTokenClassification,
		    RoCBertLayer,
		    RoCBertModel,
		    RoCBertPreTrainedModel,
		    load_tf_weights_in_roc_bert,
		)


else:
	import sys

	_snake_case          =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








"""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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ChineseCLIPImageProcessor
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



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







											"""simple docstring"""




											_lowerCAmelCase  	=			size if size is not None else {'height': 224, 'width': 224}
											_lowerCAmelCase  	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			min_resolution
											_lowerCAmelCase  	=			max_resolution
											_lowerCAmelCase  	=			do_resize
											_lowerCAmelCase  	=			size
											_lowerCAmelCase  	=			do_center_crop
											_lowerCAmelCase  	=			crop_size
											_lowerCAmelCase  	=			do_normalize
											_lowerCAmelCase  	=			image_mean
											_lowerCAmelCase  	=			image_std
											_lowerCAmelCase  	=			do_convert_rgb


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







											"""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	:			Dict				,     UpperCAmelCase_	:			Dict=False				,     UpperCAmelCase_	:			int=False				,     UpperCAmelCase_	:			Dict=False  )		->  str:







											"""simple docstring"""




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

											if equal_resolution:
																		_lowerCAmelCase  	=			[]
																		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  	=			[]
																		for i in range(self.batch_size  ):
																									_lowerCAmelCase	,		_lowerCAmelCase  	=			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  	=			[Image.fromarray(np.moveaxis(UpperCAmelCase_				,     0				,     -1  )  ) for x in image_inputs]

											if torchify:
																		_lowerCAmelCase  	=			[torch.from_numpy(UpperCAmelCase_  ) for x in image_inputs]

											return image_inputs






@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[Any]							=					ChineseCLIPImageProcessor if is_vision_available() else None


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







											"""simple docstring"""




											_lowerCAmelCase  	=			ChineseCLIPImageProcessingTester(self				,     do_center_crop=UpperCAmelCase_  )


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







											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_resize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'size'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_normalize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_mean'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_std'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_convert_rgb'  )  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			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  	=			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	:			Union[str, Any]  )		->  List[str]:







											"""simple docstring"""




											pass


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







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_lowerCAmelCase  	=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     Image.Image  )

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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	:			List[str]  )		->  int:







											"""simple docstring"""




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

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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	:			List[Any]  )		->  Dict:







											"""simple docstring"""




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

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							str							=					ChineseCLIPImageProcessor if is_vision_available() else None


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			ChineseCLIPImageProcessingTester(self				,     num_channels=4				,     do_center_crop=UpperCAmelCase_  )
											_lowerCAmelCase  	=			3


				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict:







											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()


				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_resize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'size'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_normalize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_mean'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_std'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_convert_rgb'  )  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											pass







				def 				__lowerCamelCase	(					self	:			int  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_lowerCAmelCase  	=			self.image_processor_tester.prepare_inputs(equal_resolution=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     Image.Image  )

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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'],
											    )				,     )






