'''simple docstring'''







from typing import List

import numpy as np


def    lowerCamelCase       (			UpperCAmelCase__				:  dict		)			->	int:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = {key: len(UpperCAmelCase__		) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		)}
							if len(set(lists_lengths.values()		)		) > 1:
														raise RuntimeError(
														    (
														        'Sharding is ambiguous for this dataset: '
														        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
														        + '\n'.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()		)
														        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
														        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
														    )		)
							SCREAMING_SNAKE_CASE__     :Any						    = max(lists_lengths.values()				,						default=0		)
							return max(1				,						UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int		)			->	List[range]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = []
							for group_idx in range(UpperCAmelCase__		):
														SCREAMING_SNAKE_CASE__     :List[Any]						    = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
														if num_shards_to_add == 0:
																					break
														SCREAMING_SNAKE_CASE__     :Tuple						    = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
														SCREAMING_SNAKE_CASE__     :Optional[Any]						    = range(UpperCAmelCase__				,						start + num_shards_to_add		)
														shards_indices_per_group.append(UpperCAmelCase__		)
							return shards_indices_per_group


def    lowerCamelCase       (			UpperCAmelCase__				:  dict				,						UpperCAmelCase__				:  int		)			->	List[dict]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Any						    = _number_of_shards_in_gen_kwargs(UpperCAmelCase__		)
							if num_shards == 1:
														return [dict(UpperCAmelCase__		)]
							else:
														SCREAMING_SNAKE_CASE__     :Any						    = _distribute_shards(num_shards=UpperCAmelCase__				,						max_num_jobs=UpperCAmelCase__		)
														return [
														    {
														        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
														        if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		)
														        else value
														        for key, value in gen_kwargs.items()
														    }
														    for group_idx in range(len(UpperCAmelCase__		)		)
														]


def    lowerCamelCase       (			UpperCAmelCase__				:  List[dict]		)			->	dict:


							'''simple docstring'''






							return {
							    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
							    if isinstance(gen_kwargs_list[0][key]				,						UpperCAmelCase__		)
							    else gen_kwargs_list[0][key]
							    for key in gen_kwargs_list[0]
							}





def    lowerCamelCase       (			UpperCAmelCase__				:  np.random.Generator				,						UpperCAmelCase__				:  dict		)			->	dict:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[str]						    = {len(UpperCAmelCase__		) for value in gen_kwargs.values() if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		)}
							SCREAMING_SNAKE_CASE__     :List[str]						    = {}
							for size in list_sizes:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = list(range(UpperCAmelCase__		)		)
														rng.shuffle(indices_per_size[size]		)
							# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
							SCREAMING_SNAKE_CASE__     :Any						    = dict(UpperCAmelCase__		)
							for key, value in shuffled_kwargs.items():
														if isinstance(UpperCAmelCase__				,						UpperCAmelCase__		):
																					SCREAMING_SNAKE_CASE__     :str						    = [value[i] for i in indices_per_size[len(UpperCAmelCase__		)]]
							return shuffled_kwargs







'''simple docstring'''







import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class _SCREAMING_SNAKE_CASE(							unittest.TestCase					):




						def __init__(    self :       Any							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Dict[str, int] = None							,							UpperCamelCase_ :       int = 32							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Union[int, float] = 1 / 2_55							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,							UpperCamelCase_ :       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       int=7							,							UpperCamelCase_ :       Dict=30							,							UpperCamelCase_ :       Tuple=4_00							,							UpperCamelCase_ :       List[Any]=3							,							)     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :List[str]						    = parent
													SCREAMING_SNAKE_CASE__     :Tuple						    = do_resize
													SCREAMING_SNAKE_CASE__     :List[Any]						    = size if size is not None else {'shortest_edge': 2_88}
													SCREAMING_SNAKE_CASE__     :str						    = size_divisor
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = do_rescale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rescale_factor
													SCREAMING_SNAKE_CASE__     :str						    = do_normalize
													SCREAMING_SNAKE_CASE__     :int						    = do_center_crop
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = image_mean
													SCREAMING_SNAKE_CASE__     :str						    = image_std
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = do_pad
													SCREAMING_SNAKE_CASE__     :Tuple						    = batch_size
													SCREAMING_SNAKE_CASE__     :List[str]						    = num_channels
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = min_resolution
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = max_resolution




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       str=False      )     ->				Optional[int]:
													if not batched:
																				SCREAMING_SNAKE_CASE__     :Dict						    = self.size['shortest_edge']
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = image_inputs[0]
																				if isinstance(UpperCamelCase_							,							Image.Image      ):
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = image.size
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = image.shape[1], image.shape[2]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = size / min(UpperCamelCase_							,							UpperCamelCase_      )
																				if h < w:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = size, scale * w
																				else:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = scale * h, size

																				SCREAMING_SNAKE_CASE__     :Any						    = int((13_33 / 8_00) * size      )
																				if max(UpperCamelCase_							,							UpperCamelCase_      ) > max_size:
																											SCREAMING_SNAKE_CASE__     :Tuple						    = max_size / max(UpperCamelCase_							,							UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = newh * scale
																											SCREAMING_SNAKE_CASE__     :Any						    = neww * scale

																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = int(newh + 0.5      ), int(neww + 0.5      )
																				SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    newh // self.size_divisor * self.size_divisor,
																				    neww // self.size_divisor * self.size_divisor,
																				)

													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = []
																				for image in image_inputs:
																											SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :str						    = self.get_expected_values([image]      )
																											expected_values.append((expected_height, expected_width)      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[0]      )[0]
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = max(UpperCamelCase_							,							key=lambda UpperCamelCase_      : item[1]      )[1]

													return expected_height, expected_width




@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[Any]     		= BridgeTowerImageProcessor if is_vision_available() else None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[str]						    = BridgeTowerImageProcessingTester(self      )




						@property
						def      __lowerCamelCase       (    self :       int      )     ->				Any:
													return self.image_processor_tester.prepare_image_processor_dict()




						def      __lowerCamelCase       (    self :       int      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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'      )      )
													self.assertTrue(hasattr(UpperCamelCase_							,							'size_divisor'      )      )




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




						def      __lowerCamelCase       (    self :       int      )     ->				Dict:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :Any						    = prepare_image_inputs(self.image_processor_tester							,							equal_resolution=UpperCamelCase_      )
													for image in image_inputs:
																				self.assertIsInstance(UpperCamelCase_							,							Image.Image      )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :Any						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Tuple:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :Dict						    = self.image_processing_class(**self.image_processor_dict      )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :Tuple						    = 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
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :int						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :str						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Any						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)




						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :List[str]						    = self.image_processing_class(**self.image_processor_dict      )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 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
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_processing(image_inputs[0]							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,							)

													# Test batched
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_processing(UpperCamelCase_							,							return_tensors='pt'      ).pixel_values

													SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :List[Any]						    = self.image_processor_tester.get_expected_values(UpperCamelCase_							,							batched=UpperCamelCase_      )
													self.assertEqual(
													    encoded_images.shape							,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,							)






