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 CLIPImageProcessor




class        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=7				,       lowerCAmelCase__=3				,       lowerCAmelCase__=18				,       lowerCAmelCase__=30				,       lowerCAmelCase__=4_00				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       lowerCAmelCase__=True				,       lowerCAmelCase__=None				,       lowerCAmelCase__=True				,       lowerCAmelCase__=[0.4814_5466, 0.457_8275, 0.4082_1073]				,       lowerCAmelCase__=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,       lowerCAmelCase__=True				,       )					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							size if size is not None else {'''shortest_edge''': 20}
							__lowercase			      =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							num_channels
							__lowercase			      =							image_size
							__lowercase			      =							min_resolution
							__lowercase			      =							max_resolution
							__lowercase			      =							do_resize
							__lowercase			      =							size
							__lowercase			      =							do_center_crop
							__lowercase			      =							crop_size
							__lowercase			      =							do_normalize
							__lowercase			      =							image_mean
							__lowercase			      =							image_std
							__lowercase			      =							do_convert_rgb





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

							'''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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__=False				,       lowerCAmelCase__=False				,       lowerCAmelCase__=False						)					-> List[str]:

							'''simple docstring'''


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

							if equal_resolution:
											__lowercase			      =							[]
											for i in range(self.batch_size						):
															image_inputs.append(
															    np.random.randint(
															        2_55				,       size=(self.num_channels, self.max_resolution, self.max_resolution)				,       dtype=np.uinta						)						)
							else:
											__lowercase			      =							[]
											for i in range(self.batch_size						):
															__lowercase      ,					__lowercase			      =							np.random.choice(np.arange(self.min_resolution				,       self.max_resolution						)				,       2						)
															image_inputs.append(np.random.randint(2_55				,       size=(self.num_channels, width, height)				,       dtype=np.uinta						)						)

							if not numpify and not torchify:
											# PIL expects the channel dimension as last dimension
											__lowercase			      =							[Image.fromarray(np.moveaxis(lowerCAmelCase__				,       0				,       -1						)						) for x in image_inputs]

							if torchify:
											__lowercase			      =							[torch.from_numpy(lowerCAmelCase__						) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        _UpperCamelCase				(						_UpperCAmelCase						,unittest.TestCase			):


			"""simple docstring"""






			__a  :			Tuple      			=						CLIPImageProcessor if is_vision_available() else None





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

							'''simple docstring'''


							__lowercase			      =							CLIPImageProcessingTester(self						)





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

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





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

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.image_processing_class.from_dict(self.image_processor_dict						)
							self.assertEqual(image_processor.size				,       {'''shortest_edge''': 20}						)
							self.assertEqual(image_processor.crop_size				,       {'''height''': 18, '''width''': 18}						)

							__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							pass





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

							'''simple docstring'''


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

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


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

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


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

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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        _UpperCamelCase				(						_UpperCAmelCase						,unittest.TestCase			):


			"""simple docstring"""






			__a  :			Tuple      			=						CLIPImageProcessor if is_vision_available() else None





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							CLIPImageProcessingTester(self				,       num_channels=4						)
							__lowercase			      =							3





			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							pass





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


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

							# Test not batched input
							__lowercase			      =							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
							__lowercase			      =							image_processing(lowerCAmelCase__				,       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'''],
							    )				,       )
from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


__a  :					Union[str, Any]          =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if isinstance(lowercase							,      lowercase							):
								return _split_by_node_map_style_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)
				else:
								return _split_by_node_iterable_dataset(lowercase							,      rank=lowercase							,      world_size=lowercase							)