






import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor


class      lowercase__     (			unittest.TestCase	):

	def __init__(							self		,    __UpperCAmelCase		,    __UpperCAmelCase=7		,    __UpperCAmelCase=3		,    __UpperCAmelCase=18		,    __UpperCAmelCase=30		,    __UpperCAmelCase=400		,    __UpperCAmelCase=True		,    __UpperCAmelCase=None		,    __UpperCAmelCase=True		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    __UpperCAmelCase=[0.5, 0.5, 0.5]		,    )->					str:







							'''simple docstring'''





							lowerCAmelCase__       =						size if 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_normalize
							lowerCAmelCase__       =						image_mean
							lowerCAmelCase__       =						image_std


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







							'''simple docstring'''





							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      lowercase__     (			_UpperCAmelCase,					unittest.TestCase	):
	a_         =DPTImageProcessor if is_vision_available() else None

	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						DPTImageProcessingTester(self       )

	@property
	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							return self.image_processor_tester.prepare_image_processor_dict()

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "image_mean"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "image_std"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "do_normalize"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "do_resize"       )       )
							self.assertTrue(hasattr(__UpperCAmelCase		,    "size"       )       )

	def 		UpperCAmelCase  (							self       )->					Tuple:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size		,    {"height": 18, "width": 18}       )

							lowerCAmelCase__       =						self.image_processing_class.from_dict(self.image_processor_dict		,    size=42       )
							self.assertEqual(image_processor.size		,    {"height": 42, "width": 42}       )

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							lowerCAmelCase__       =						prepare_image_inputs(self.image_processor_tester		,    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.size["height"],
							        self.image_processor_tester.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.size["height"],
							        self.image_processor_tester.size["width"],
							    )		,    )

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







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							lowerCAmelCase__       =						prepare_image_inputs(self.image_processor_tester		,    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.size["height"],
							        self.image_processor_tester.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.size["height"],
							        self.image_processor_tester.size["width"],
							    )		,    )


	def 		UpperCAmelCase  (							self       )->					Any:







							'''simple docstring'''





							lowerCAmelCase__       =						self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							lowerCAmelCase__       =						prepare_image_inputs(self.image_processor_tester		,    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.size["height"],
							        self.image_processor_tester.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.size["height"],
							        self.image_processor_tester.size["width"],
							    )		,    )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''IBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''IBertForMaskedLM''',
	    '''IBertForMultipleChoice''',
	    '''IBertForQuestionAnswering''',
	    '''IBertForSequenceClassification''',
	    '''IBertForTokenClassification''',
	    '''IBertModel''',
	    '''IBertPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ibert import (
		    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    IBertForMaskedLM,
		    IBertForMultipleChoice,
		    IBertForQuestionAnswering,
		    IBertForSequenceClassification,
		    IBertForTokenClassification,
		    IBertModel,
		    IBertPreTrainedModel,
		)

else:
	import sys

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






