





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 	UpperCamelCase_							(  unittest.TestCase     ):


							def __init__(				self     :List[str]				,					__A     :List[str]				,					__A     :bool = True				,					__A     :Dict[str, int] = None				,					__A     :int = 32				,					__A     :bool = True				,					__A     :Union[int, float] = 1 / 255				,					__A     :bool = True				,					__A     :bool = True				,					__A     :Optional[Union[float, List[float]]] = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				,					__A     :Optional[Union[float, List[float]]] = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				,					__A     :bool = True				,					__A     :List[str]=7				,					__A     :int=30				,					__A     :List[Any]=400				,					__A     :int=3				,					) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   do_resize
									SCREAMING_SNAKE_CASE__								=   size if size is not None else {"""shortest_edge""": 288}
									SCREAMING_SNAKE_CASE__								=   size_divisor
									SCREAMING_SNAKE_CASE__								=   do_rescale
									SCREAMING_SNAKE_CASE__								=   rescale_factor
									SCREAMING_SNAKE_CASE__								=   do_normalize
									SCREAMING_SNAKE_CASE__								=   do_center_crop
									SCREAMING_SNAKE_CASE__								=   image_mean
									SCREAMING_SNAKE_CASE__								=   image_std
									SCREAMING_SNAKE_CASE__								=   do_pad
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   num_channels
									SCREAMING_SNAKE_CASE__								=   min_resolution
									SCREAMING_SNAKE_CASE__								=   max_resolution


							def   _snake_case	(				self     :str		) -> int:



									"""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   _snake_case	(				self     :List[str]				,					__A     :Optional[Any]				,					__A     :int=False		) -> Dict:



									"""simple docstring"""



									if not batched:
											SCREAMING_SNAKE_CASE__								=   self.size["""shortest_edge"""]
											SCREAMING_SNAKE_CASE__								=   image_inputs[0]
											if isinstance(lowerCamelCase__				,					Image.Image		):
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   image.size
											else:
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   image.shape[1], image.shape[2]
											SCREAMING_SNAKE_CASE__								=   size / min(lowerCamelCase__				,					lowerCamelCase__		)
											if h < w:
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   size, scale * w
											else:
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   scale * h, size

											SCREAMING_SNAKE_CASE__								=   int((1333 / 800) * size		)
											if max(lowerCamelCase__				,					lowerCamelCase__		) > max_size:
													SCREAMING_SNAKE_CASE__								=   max_size / max(lowerCamelCase__				,					lowerCamelCase__		)
													SCREAMING_SNAKE_CASE__								=   newh * scale
													SCREAMING_SNAKE_CASE__								=   neww * scale

											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   int(newh + 0.5		), int(neww + 0.5		)
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   (
											    newh // self.size_divisor * self.size_divisor,
											    neww // self.size_divisor * self.size_divisor,
											)

									else:
											SCREAMING_SNAKE_CASE__								=   []
											for image in image_inputs:
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.get_expected_values([image]		)
													expected_values.append((expected_height, expected_width)		)
											SCREAMING_SNAKE_CASE__								=   max(lowerCamelCase__				,					key=lambda __A		: item[0]		)[0]
											SCREAMING_SNAKE_CASE__								=   max(lowerCamelCase__				,					key=lambda __A		: item[1]		)[1]

									return expected_height, expected_width




@require_torch
@require_vision
class 	UpperCamelCase_							(  __lowerCamelCase					,	unittest.TestCase     ):
							lowerCamelCase_							     =       BridgeTowerImageProcessor if is_vision_available() else None


							def   _snake_case	(				self     :int		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   BridgeTowerImageProcessingTester(self		)


							@property
							def   _snake_case	(				self     :str		) -> Optional[int]:



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def   _snake_case	(				self     :Tuple		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(lowerCamelCase__				,					"""image_mean"""		)		)
									self.assertTrue(hasattr(lowerCamelCase__				,					"""image_std"""		)		)
									self.assertTrue(hasattr(lowerCamelCase__				,					"""do_normalize"""		)		)
									self.assertTrue(hasattr(lowerCamelCase__				,					"""do_resize"""		)		)
									self.assertTrue(hasattr(lowerCamelCase__				,					"""size"""		)		)
									self.assertTrue(hasattr(lowerCamelCase__				,					"""size_divisor"""		)		)


							def   _snake_case	(				self     :Optional[int]		) -> str:



									"""simple docstring"""



									pass


							def   _snake_case	(				self     :Optional[int]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									SCREAMING_SNAKE_CASE__								=   prepare_image_inputs(self.image_processor_tester				,					equal_resolution=lowerCamelCase__		)
									for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__				,					Image.Image		)

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

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

									# Test batched
									SCREAMING_SNAKE_CASE__								=   image_processing(lowerCamelCase__				,					return_tensors="""pt"""		).pixel_values

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


							def   _snake_case	(				self     :str		) -> Optional[int]:



									"""simple docstring"""



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

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

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

									# Test batched
									SCREAMING_SNAKE_CASE__								=   image_processing(lowerCamelCase__				,					return_tensors="""pt"""		).pixel_values

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


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



									"""simple docstring"""



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

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

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

									# Test batched
									SCREAMING_SNAKE_CASE__								=   image_processing(lowerCamelCase__				,					return_tensors="""pt"""		).pixel_values

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


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowercase				      =						subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowercase				      =						(
    subprocess.check_output(f'git diff --diff-filter=d --name-only {fork_point_sha}'.split()).decode("""utf-8""").split()
)

__lowercase				      =						"""|""".join(sys.argv[1:])
__lowercase				      =						re.compile(rf'^({joined_dirs}).*?\.py$')

__lowercase				      =						[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")





