
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        _a(    unittest.TestCase    ):







		def __init__(    self ,						__snake_case ,						__snake_case = True ,						__snake_case = None ,						__snake_case = 3_2 ,						__snake_case = True ,						__snake_case = 1 / 2_5_5 ,						__snake_case = True ,						__snake_case = True ,						__snake_case = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,						__snake_case = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,						__snake_case = True ,						__snake_case=7 ,						__snake_case=3_0 ,						__snake_case=4_0_0 ,						__snake_case=3 ,						)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   parent
							_snake_case       :					Tuple				       =   do_resize
							_snake_case       :					str				       =   size if size is not None else {"shortest_edge": 2_8_8}
							_snake_case       :					List[str]				       =   size_divisor
							_snake_case       :					Union[str, Any]				       =   do_rescale
							_snake_case       :					List[str]				       =   rescale_factor
							_snake_case       :					int				       =   do_normalize
							_snake_case       :					Tuple				       =   do_center_crop
							_snake_case       :					Optional[int]				       =   image_mean
							_snake_case       :					Optional[Any]				       =   image_std
							_snake_case       :					str				       =   do_pad
							_snake_case       :					Optional[int]				       =   batch_size
							_snake_case       :					int				       =   num_channels
							_snake_case       :					List[Any]				       =   min_resolution
							_snake_case       :					Optional[Any]				       =   max_resolution







		def        lowercase       (    self				)      ->      List[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,
							    "size_divisor": self.size_divisor,
							}







		def        lowercase       (    self ,						__snake_case ,						__snake_case=False				)      ->      List[Any]:


							'''simple docstring'''

							if not batched:
												_snake_case       :					Optional[int]				       =   self.size["shortest_edge"]
												_snake_case       :					int				       =   image_inputs[0]
												if isinstance(__snake_case ,						Image.Image				):
																	_snake_case       , _snake_case       :					Optional[int]				       =   image.size
												else:
																	_snake_case       , _snake_case       :					Tuple				       =   image.shape[1], image.shape[2]
												_snake_case       :					str				       =   size / min(__snake_case ,						__snake_case				)
												if h < w:
																	_snake_case       , _snake_case       :					str				       =   size, scale * w
												else:
																	_snake_case       , _snake_case       :					str				       =   scale * h, size

												_snake_case       :					Dict				       =   int((1_3_3_3 / 8_0_0) * size				)
												if max(__snake_case ,						__snake_case				) > max_size:
																	_snake_case       :					Any				       =   max_size / max(__snake_case ,						__snake_case				)
																	_snake_case       :					Optional[int]				       =   newh * scale
																	_snake_case       :					Optional[int]				       =   neww * scale

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

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

							return expected_height, expected_width






@require_torch
@require_vision
class        _a(    __A	,   unittest.TestCase    ):
		lowerCamelCase__     :Tuple   				=		BridgeTowerImageProcessor if is_vision_available() else None







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   BridgeTowerImageProcessingTester(self				)







		@property
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							return self.image_processor_tester.prepare_image_processor_dict()







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


							'''simple docstring'''

							_snake_case       :					Tuple				       =   self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__snake_case ,						"image_mean"				)				)
							self.assertTrue(hasattr(__snake_case ,						"image_std"				)				)
							self.assertTrue(hasattr(__snake_case ,						"do_normalize"				)				)
							self.assertTrue(hasattr(__snake_case ,						"do_resize"				)				)
							self.assertTrue(hasattr(__snake_case ,						"size"				)				)
							self.assertTrue(hasattr(__snake_case ,						"size_divisor"				)				)







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


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					int				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							_snake_case       :					Union[str, Any]				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__snake_case				)
							for image in image_inputs:
												self.assertIsInstance(__snake_case ,						Image.Image				)

							# Test not batched input
							_snake_case       :					List[Any]				       =   image_processing(image_inputs[0] ,						return_tensors="pt"				).pixel_values

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

							# Test batched
							_snake_case       :					str				       =   image_processing(__snake_case ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					Union[str, Any]				       =   self.image_processor_tester.get_expected_values(__snake_case ,						batched=__snake_case				)
							self.assertEqual(
							    encoded_images.shape ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,						)







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					str				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							_snake_case       :					Dict				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__snake_case ,						numpify=__snake_case				)
							for image in image_inputs:
												self.assertIsInstance(__snake_case ,						np.ndarray				)

							# Test not batched input
							_snake_case       :					Any				       =   image_processing(image_inputs[0] ,						return_tensors="pt"				).pixel_values

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

							# Test batched
							_snake_case       :					str				       =   image_processing(__snake_case ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					List[str]				       =   self.image_processor_tester.get_expected_values(__snake_case ,						batched=__snake_case				)
							self.assertEqual(
							    encoded_images.shape ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,						)







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


							'''simple docstring'''

							_snake_case       :					int				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							_snake_case       :					Tuple				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=__snake_case ,						torchify=__snake_case				)
							for image in image_inputs:
												self.assertIsInstance(__snake_case ,						torch.Tensor				)

							# Test not batched input
							_snake_case       :					Dict				       =   image_processing(image_inputs[0] ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					List[Any]				       =   self.image_processor_tester.get_expected_values(__snake_case				)
							self.assertEqual(
							    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

							# Test batched
							_snake_case       :					List[Any]				       =   image_processing(__snake_case ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					Optional[Any]				       =   self.image_processor_tester.get_expected_values(__snake_case ,						batched=__snake_case				)
							self.assertEqual(
							    encoded_images.shape ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,						)






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file  # noqa: F401
from .default import default_command_parser
from .update import update_command_parser







def    A				(							UpperCAmelCase=None				):
					_snake_case       :					Union[str, Any]				       =   argparse.ArgumentParser(add_help=UpperCAmelCase  ,  allow_abbrev=UpperCAmelCase				)
					# The main config parser
					_snake_case       :					Tuple				       =   config_command_parser(UpperCAmelCase				)
					# The subparser to add commands to
					_snake_case       :					Any				       =   config_parser.add_subparsers(title="subcommands"  ,  dest="subcommand"				)

					# Then add other parsers with the parent parser
					default_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)
					update_command_parser(UpperCAmelCase  ,  parents=[parent_parser]				)

					return config_parser







def    A				(							):
					_snake_case       :					str				       =   get_config_parser()
					_snake_case       :					Union[str, Any]				       =   config_parser.parse_args()

					if not hasattr(UpperCAmelCase  ,  "func"				):
										config_parser.print_help()
										exit(1				)

					# Run
					args.func(UpperCAmelCase				)


if __name__ == "__main__":
							main()