





"""simple docstring"""





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 		SCREAMING_SNAKE_CASE_							(    unittest.TestCase   ):




				def __init__(     self  :List[Any]						,    __lowercase  :List[Any]						,    __lowercase  :Tuple=7						,    __lowercase  :Dict=3						,    __lowercase  :Optional[int]=18						,    __lowercase  :Tuple=30						,    __lowercase  :Optional[Any]=400						,    __lowercase  :Dict=True						,    __lowercase  :List[Any]=None						,    __lowercase  :List[str]=True						,    __lowercase  :Union[str, Any]=[0.5, 0.5, 0.5]						,    __lowercase  :Optional[int]=[0.5, 0.5, 0.5]						,    ):
											__lowerCamelCase      :      List[str]         =size if size is not None else {'''height''': 18, '''width''': 18}
											__lowerCamelCase      :      Optional[Any]         =parent
											__lowerCamelCase      :      str         =batch_size
											__lowerCamelCase      :      List[Any]         =num_channels
											__lowerCamelCase      :      Any         =image_size
											__lowerCamelCase      :      int         =min_resolution
											__lowerCamelCase      :      Any         =max_resolution
											__lowerCamelCase      :      Dict         =do_resize
											__lowerCamelCase      :      Tuple         =size
											__lowerCamelCase      :      List[str]         =do_normalize
											__lowerCamelCase      :      Optional[Any]         =image_mean
											__lowerCamelCase      :      Optional[Any]         =image_std




				def 					__lowercase  (     self  :Union[str, Any]			):
											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 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  unittest.TestCase   ):
				__snake_case   :       Tuple	 =		DPTImageProcessor if is_vision_available() else None




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      List[Any]         =DPTImageProcessingTester(self			)




				@property
				def 					__lowercase  (     self  :int			):
											return self.image_processor_tester.prepare_image_processor_dict()




				def 					__lowercase  (     self  :Dict			):
											__lowerCamelCase      :      Optional[Any]         =self.image_processing_class(**self.image_processor_dict			)
											self.assertTrue(hasattr(__lowercase						,    '''image_mean'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''image_std'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''do_normalize'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''do_resize'''			)			)
											self.assertTrue(hasattr(__lowercase						,    '''size'''			)			)




				def 					__lowercase  (     self  :Tuple			):
											__lowerCamelCase      :      List[Any]         =self.image_processing_class.from_dict(self.image_processor_dict			)
											self.assertEqual(image_processor.size						,    {'''height''': 18, '''width''': 18}			)

											__lowerCamelCase      :      Any         =self.image_processing_class.from_dict(self.image_processor_dict						,    size=42			)
											self.assertEqual(image_processor.size						,    {'''height''': 42, '''width''': 42}			)




				def 					__lowercase  (     self  :Any			):
											# Initialize image_processing
											__lowerCamelCase      :      Union[str, Any]         =self.image_processing_class(**self.image_processor_dict			)
											# create random PIL images
											__lowerCamelCase      :      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      :      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.size['''height'''],
											        self.image_processor_tester.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.size['''height'''],
											        self.image_processor_tester.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      :      List[str]         =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      :      List[str]         =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      :      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.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )						,    )




				def 					__lowercase  (     self  :List[str]			):
											# Initialize image_processing
											__lowerCamelCase      :      str         =self.image_processing_class(**self.image_processor_dict			)
											# create random PyTorch tensors
											__lowerCamelCase      :      str         =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      :      Optional[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.size['''height'''],
											        self.image_processor_tester.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.image_processor_tester.num_channels,
											        self.image_processor_tester.size['''height'''],
											        self.image_processor_tester.size['''width'''],
											    )						,    )











"""simple docstring"""





from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    LineByLineWithSOPTextDataset,
    TextDataset,
    TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments




