




"""simple docstring"""




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 ViTImageProcessor






class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a=13	,				_a=3	,				_a=224	,				_a=30	,				_a=400	,				_a=True	,				_a=None	,				_a=True	,				_a=[0.5, 0.5, 0.5]	,				_a=[0.5, 0.5, 0.5]	,				):





							"""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      _lowerCAmelCase       ( self				):





							"""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 					__magic_name__							( UpperCAmelCase__		,      unittest.TestCase       ):






				'''simple docstring'''
				__UpperCamelCase    						=		ViTImageProcessor if is_vision_available() else None





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      EfficientFormerImageProcessorTester(self				)





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self.image_proc_tester.prepare_image_processor_dict()





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(_a	,				"""image_mean"""				)				)
							self.assertTrue(hasattr(_a	,				"""image_std"""				)				)
							self.assertTrue(hasattr(_a	,				"""do_normalize"""				)				)
							self.assertTrue(hasattr(_a	,				"""do_resize"""				)				)
							self.assertTrue(hasattr(_a	,				"""size"""				)				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							pass





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processor
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							lowerCamelCase       =      prepare_image_inputs(self.image_proc_tester	,				equal_resolution=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				Image.Image				)

							# Test not batched input
							lowerCamelCase       =      image_processor(image_inputs[0]	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )	,				)

							# Test batched
							lowerCamelCase       =      image_processor(_a	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processor
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							lowerCamelCase       =      prepare_image_inputs(self.image_proc_tester	,				equal_resolution=_a	,				numpify=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				np.ndarray				)

							# Test not batched input
							lowerCamelCase       =      image_processor(image_inputs[0]	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )	,				)

							# Test batched
							lowerCamelCase       =      image_processor(_a	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )	,				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							# Initialize image_processor
							lowerCamelCase       =      self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							lowerCamelCase       =      prepare_image_inputs(self.image_proc_tester	,				equal_resolution=_a	,				torchify=_a				)
							for image in image_inputs:
										self.assertIsInstance(_a	,				torch.Tensor				)

							# Test not batched input
							lowerCamelCase       =      image_processor(image_inputs[0]	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )	,				)

							# Test batched
							lowerCamelCase       =      image_processor(_a	,				return_tensors="""pt"""				).pixel_values
							self.assertEqual(
							    encoded_images.shape	,				(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size["""height"""],
							        self.image_proc_tester.size["""width"""],
							    )	,				)









"""simple docstring"""




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowerCAmelCase							:    List[Any]  							=							logging.get_logger(__name__)


lowerCAmelCase							:    int  							=							TypeVar("""DatasetType""", Dataset, IterableDataset)



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = "first_exhausted" ,				)		->      DatasetType:
			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("""Unable to interleave an empty list of datasets."""       )
			for i, dataset in enumerate(snake_case__       ):
						if not isinstance(snake_case__ ,				(Dataset, IterableDataset)       ):
									if isinstance(snake_case__ ,				(DatasetDict, IterableDatasetDict)       ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    """is an empty dataset dictionary."""       )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(snake_case__       )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__       )       )}\']'       )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__       ).__name__}.'       )
						if i == 0:
									lowerCamelCase   ,     lowerCamelCase       =      (
									    (Dataset, IterableDataset) if isinstance(snake_case__ ,				snake_case__       ) else (IterableDataset, Dataset)
									)
						elif not isinstance(snake_case__ ,				snake_case__       ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'       )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				stopping_strategy=snake_case__       )
			else:
						return _interleave_iterable_datasets(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				stopping_strategy=snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = 0 ,				)		->      DatasetType:

			if not dsets:
						raise ValueError("""Unable to concatenate an empty list of datasets."""       )
			for i, dataset in enumerate(snake_case__       ):
						if not isinstance(snake_case__ ,				(Dataset, IterableDataset)       ):
									if isinstance(snake_case__ ,				(DatasetDict, IterableDatasetDict)       ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    """is an empty dataset dictionary."""       )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(snake_case__       )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__       )       )}\']'       )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__       ).__name__}.'       )
						if i == 0:
									lowerCamelCase   ,     lowerCamelCase       =      (
									    (Dataset, IterableDataset) if isinstance(snake_case__ ,				snake_case__       ) else (IterableDataset, Dataset)
									)
						elif not isinstance(snake_case__ ,				snake_case__       ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				axis=snake_case__       )
			else:
						return _concatenate_iterable_datasets(snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				axis=snake_case__       )



