





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a						: str     	=			{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a						: Tuple     	=			['''DebertaTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a						: Optional[int]     	=			[
		    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DebertaForMaskedLM''',
		    '''DebertaForQuestionAnswering''',
		    '''DebertaForSequenceClassification''',
		    '''DebertaForTokenClassification''',
		    '''DebertaModel''',
		    '''DebertaPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a						: Dict     	=			[
		    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDebertaForMaskedLM''',
		    '''TFDebertaForQuestionAnswering''',
		    '''TFDebertaForSequenceClassification''',
		    '''TFDebertaForTokenClassification''',
		    '''TFDebertaModel''',
		    '''TFDebertaPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
		from .tokenization_deberta import DebertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_fast import DebertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta import (
				    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaForMaskedLM,
				    DebertaForQuestionAnswering,
				    DebertaForSequenceClassification,
				    DebertaForTokenClassification,
				    DebertaModel,
				    DebertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta import (
				    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaForMaskedLM,
				    TFDebertaForQuestionAnswering,
				    TFDebertaForSequenceClassification,
				    TFDebertaForTokenClassification,
				    TFDebertaModel,
				    TFDebertaPreTrainedModel,
				)


else:
		import sys

		a						: int     	=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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 MobileNetVaImageProcessor







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase=7      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=None      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					Optional[Any]			 = size if size is not None else {'''shortest_edge''': 2_0}
										lowerCAmelCase_       :					Any			 = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_       :					Any			 = parent
										lowerCAmelCase_       :					Any			 = batch_size
										lowerCAmelCase_       :					Optional[int]			 = num_channels
										lowerCAmelCase_       :					Tuple			 = image_size
										lowerCAmelCase_       :					List[str]			 = min_resolution
										lowerCAmelCase_       :					Dict			 = max_resolution
										lowerCAmelCase_       :					Tuple			 = do_resize
										lowerCAmelCase_       :					Optional[Any]			 = size
										lowerCAmelCase_       :					Union[str, Any]			 = do_center_crop
										lowerCAmelCase_       :					Optional[Any]			 = crop_size




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}







@require_torch
@require_vision
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							MobileNetVaImageProcessor if is_vision_available() else None




						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = MobileNetVaImageProcessingTester(self							)




						@property
						def   lowercase_       ( self							)    ->			Dict:
										return self.image_processor_tester.prepare_image_processor_dict()




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = 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      ,  '''crop_size'''							)							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					str			 = self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 2_0}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 1_8, '''width''': 1_8}							)

										lowerCAmelCase_       :					Tuple			 = self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 8_4, '''width''': 8_4}							)




						def   lowercase_       ( self							)    ->			Tuple:
										pass




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										# Initialize image_processing
										lowerCAmelCase_       :					Optional[int]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_       :					Union[str, Any]			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  Image.Image							)

										# Test not batched input
										lowerCAmelCase_       :					Tuple			 = 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_       :					Tuple			 = 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   lowercase_       ( self							)    ->			Optional[int]:
										# Initialize image_processing
										lowerCAmelCase_       :					Tuple			 = self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_       :					Any			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  numpify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  np.ndarray							)

										# 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_       :					Tuple			 = 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   lowercase_       ( self							)    ->			Any:
										# Initialize image_processing
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_       :					int			 = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  torchify=__lowercase							)
										for image in image_inputs:
														self.assertIsInstance(__lowercase      ,  torch.Tensor							)

										# Test not batched input
										lowerCAmelCase_       :					List[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(__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'''],
										    )      ,  )