

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 PoolFormerImageProcessor







class       __lowerCAmelCase   (      unittest.TestCase						):



			def __init__(						self       :  Union[str, Any]			,		A       :  Dict			,		A       :  Tuple=7			,		A       :  Optional[int]=3			,		A       :  Dict=30			,		A       :  int=4_00			,		A       :  str=True			,		A       :  Optional[Any]=None			,		A       :  Dict=0.9			,		A       :  Dict=None			,		A       :  Tuple=True			,		A       :  List[Any]=[0.5, 0.5, 0.5]			,		A       :  Union[str, Any]=[0.5, 0.5, 0.5]			,		)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       size if size is not None else {'shortest_edge': 30}
						_UpperCAmelCase			=       crop_size if crop_size is not None else {'height': 30, 'width': 30}
						_UpperCAmelCase			=       parent
						_UpperCAmelCase			=       batch_size
						_UpperCAmelCase			=       num_channels
						_UpperCAmelCase			=       min_resolution
						_UpperCAmelCase			=       max_resolution
						_UpperCAmelCase			=       do_resize_and_center_crop
						_UpperCAmelCase			=       size
						_UpperCAmelCase			=       crop_pct
						_UpperCAmelCase			=       crop_size
						_UpperCAmelCase			=       do_normalize
						_UpperCAmelCase			=       image_mean
						_UpperCAmelCase			=       image_std



			def    _lowerCamelCase	(						self       :  Tuple)       ->   int:



						"""simple docstring"""






						return {
						    "size": self.size,
						    "do_resize_and_center_crop": self.do_resize_and_center_crop,
						    "crop_pct": self.crop_pct,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}







@require_torch
@require_vision
class       __lowerCAmelCase   (      lowerCAmelCase__    ,						unittest.TestCase						):
			UpperCamelCase        =					PoolFormerImageProcessor if is_vision_available() else None



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



						"""simple docstring"""






						_UpperCAmelCase			=       PoolFormerImageProcessingTester(self)



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'do_resize_and_center_crop'))
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'size'))
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'crop_pct'))
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'do_normalize'))
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'image_mean'))
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,		'image_std'))



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size			,		{'shortest_edge': 30})
						self.assertEqual(image_processor.crop_size			,		{'height': 30, 'width': 30})

						_UpperCAmelCase			=       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       :  Dict)       ->   Union[str, Any]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  List[str])       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase			=       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=_SCREAMING_SNAKE_CASE)
						for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE			,		Image.Image)

						# Test not batched input
						_UpperCAmelCase			=       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
						_UpperCAmelCase			=       image_processing(_SCREAMING_SNAKE_CASE			,		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       :  str)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase			=       prepare_image_inputs(self.image_processor_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
						_UpperCAmelCase			=       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
						_UpperCAmelCase			=       image_processing(_SCREAMING_SNAKE_CASE			,		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])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase			=       prepare_image_inputs(self.image_processor_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
						_UpperCAmelCase			=       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
						_UpperCAmelCase			=       image_processing(_SCREAMING_SNAKE_CASE			,		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 typing import TYPE_CHECKING

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


UpperCAmelCase__       =	{
    "configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig"],
    "tokenization_deberta": ["DebertaTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["DebertaTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "DebertaForMaskedLM",
							    "DebertaForQuestionAnswering",
							    "DebertaForSequenceClassification",
							    "DebertaForTokenClassification",
							    "DebertaModel",
							    "DebertaPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "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

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
