






'''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 					_snake_case						(			unittest.TestCase ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = 32					,UpperCamelCase = True					,UpperCamelCase = 1 / 255					,UpperCamelCase = True					,UpperCamelCase = True					,UpperCamelCase = [0.48145466, 0.4578275, 0.40821073]					,UpperCamelCase = [0.26862954, 0.26130258, 0.27577711]					,UpperCamelCase = True					,UpperCamelCase=7					,UpperCamelCase=30					,UpperCamelCase=400					,UpperCamelCase=3					,)						->    Any:
					snake_case__      :Union[str, Any]         =   parent
					snake_case__      :str         =   do_resize
					snake_case__      :str         =   size if size is not None else {"shortest_edge": 288}
					snake_case__      :Tuple         =   size_divisor
					snake_case__      :Optional[int]         =   do_rescale
					snake_case__      :Tuple         =   rescale_factor
					snake_case__      :List[Any]         =   do_normalize
					snake_case__      :List[str]         =   do_center_crop
					snake_case__      :List[Any]         =   image_mean
					snake_case__      :Optional[int]         =   image_std
					snake_case__      :Any         =   do_pad
					snake_case__      :List[str]         =   batch_size
					snake_case__      :Tuple         =   num_channels
					snake_case__      :List[str]         =   min_resolution
					snake_case__      :Union[str, Any]         =   max_resolution






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

									snake_case__      :Union[str, Any]         =   int((1_333 / 800) * size       )
									if max(UpperCamelCase					,UpperCamelCase       ) > max_size:
													snake_case__      :int         =   max_size / max(UpperCamelCase					,UpperCamelCase       )
													snake_case__      :Tuple         =   newh * scale
													snake_case__      :List[Any]         =   neww * scale

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

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

					return expected_height, expected_width




@require_torch
@require_vision
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   BridgeTowerImageProcessor if is_vision_available() else None






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :str         =   BridgeTowerImageProcessingTester(self       )






	@property
	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					return self.image_processor_tester.prepare_image_processor_dict()






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :str         =   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]:
					pass






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					# Initialize image processor
					snake_case__      :Union[str, Any]         =   self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case__      :List[str]         =   prepare_image_inputs(self.image_processor_tester					,equal_resolution=UpperCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase					,Image.Image       )

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

					snake_case__      :List[Any]         =   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
					snake_case__      :Optional[Any]         =   image_processing(UpperCamelCase					,return_tensors="pt"       ).pixel_values

					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,
					    )					,)






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

					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
					snake_case__      :Optional[int]         =   image_processing(UpperCamelCase					,return_tensors="pt"       ).pixel_values

					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,
					    )					,)






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

					snake_case__      :List[str]         =   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
					snake_case__      :str         =   image_processing(UpperCamelCase					,return_tensors="pt"       ).pixel_values

					snake_case__      :List[str]         =   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,
					    )					,)







import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [file for file in filepaths if file != file.lower()]
if upper_files:
	print(F'''{len(upper_files)} files contain uppercase characters:''')
	print("\n".join(upper_files) + "\n")

__UpperCAmelCase							:   Any							=  [file for file in filepaths if " " in file]
if space_files:
	print(F'''{len(space_files)} files contain space characters:''')
	print("\n".join(space_files) + "\n")

__UpperCAmelCase							:   str							=  [file for file in filepaths if "-" in file]
if hyphen_files:
	print(F'''{len(hyphen_files)} files contain hyphen characters:''')
	print("\n".join(hyphen_files) + "\n")

__UpperCAmelCase							:   Dict							=  [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(F'''{len(nodir_files)} files are not in a directory:''')
	print("\n".join(nodir_files) + "\n")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)