




'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor




class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def __init__(   self       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  List[str]=7      ,   __lowerCamelCase       :  Tuple=3      ,   __lowerCamelCase       :  Optional[int]=18      ,   __lowerCamelCase       :  Dict=30      ,   __lowerCamelCase       :  Dict=4_00      ,   __lowerCamelCase       :  List[str]=True      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  Tuple=True      ,   __lowerCamelCase       :  int=False      ,   __lowerCamelCase       :  str=True      ,   __lowerCamelCase       :  Tuple=True      ,   __lowerCamelCase       :  str=[0.5, 0.5, 0.5]      ,   __lowerCamelCase       :  str=[0.5, 0.5, 0.5]      ,   )      ->  Dict:
							a					=    parent
							a					=    batch_size
							a					=    num_channels
							a					=    image_size
							a					=    min_resolution
							a					=    max_resolution
							a					=    do_resize
							a					=    size if size is not None else {"height": 18, "width": 20}
							a					=    do_thumbnail
							a					=    do_align_axis
							a					=    do_pad
							a					=    do_normalize
							a					=    image_mean
							a					=    image_std





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  List[str]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_thumbnail": self.do_thumbnail,
							    "do_align_long_axis": self.do_align_axis,
							    "do_pad": self.do_pad,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}




@require_torch
@require_vision
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							str											=      DonutImageProcessor if is_vision_available() else None





				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Optional[int]:
							a					=    DonutImageProcessingTester(self							)





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





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					=    self.image_processing_class(**self.image_processor_dict							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_resize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "size"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_thumbnail"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_align_long_axis"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_pad"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "do_normalize"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_mean"							)							)
							self.assertTrue(hasattr(__lowerCamelCase      ,   "image_std"							)							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[str]:
							a					=    self.image_processing_class.from_dict(self.image_processor_dict							)
							self.assertEqual(image_processor.size      ,   {"height": 18, "width": 20}							)

							a					=    self.image_processing_class.from_dict(self.image_processor_dict      ,   size=42							)
							self.assertEqual(image_processor.size      ,   {"height": 42, "width": 42}							)

							# Previous config had dimensions in (width, height) order
							a					=    self.image_processing_class.from_dict(self.image_processor_dict      ,   size=(42, 84)							)
							self.assertEqual(image_processor.size      ,   {"height": 84, "width": 42}							)





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





				@is_flaky()
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Tuple:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PIL images
							a					=    prepare_image_inputs(self.image_processor_tester      ,   equal_resolution=__lowerCamelCase							)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase      ,   Image.Image							)

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

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    )      ,   )





				@is_flaky()
				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Dict:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random numpy tensors
							a					=    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
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    )      ,   )

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    )      ,   )





				@is_flaky()
				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Optional[int]:
							# Initialize image_processing
							a					=    self.image_processing_class(**self.image_processor_dict							)
							# create random PyTorch tensors
							a					=    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
							a					=    image_processing(image_inputs[0]      ,   return_tensors="pt"							).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    )      ,   )

							# Test batched
							a					=    image_processing(__lowerCamelCase      ,   return_tensors="pt"							).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    )      ,   )












import argparse
import os
import re


__lowerCAmelCase	:       Union[str, Any]     =	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase	:       Dict     =	re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__lowerCAmelCase	:       Any     =	re.compile(r'\s*\(\s*"(\S[^"]+)"')




def     __magic_name__       (				A	:      int,						A	:      bool = False     ):






			'''simple docstring'''







			with open(A,						"r",						encoding="utf-8"     ) as f:
						a					=    f.read()

			a					=    content.split("\n"     )
			a					=    []
			a					=    0
			while line_idx < len(A     ):
						if _re_intro_mapping.search(lines[line_idx]     ) is not None:
									a					=    len(re.search(R"^(\s*)\S",						lines[line_idx]     ).groups()[0]     ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(" " * indent + "("     ):
												new_lines.append(lines[line_idx]     )
												line_idx += 1

									a					=    []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															a					=    line_idx
															while not lines[line_idx].startswith(" " * indent + ")"     ):
																		line_idx += 1
															blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
												else:
															blocks.append(lines[line_idx]     )
												line_idx += 1

									# Sort blocks by their identifiers
									a					=    sorted(A,						key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]     )
									line_idx += 1

			if overwrite:
						with open(A,						"w",						encoding="utf-8"     ) as f:
									f.write("\n".join(A     )     )
			elif "\n".join(A     ) != content:
						return True




def     __magic_name__       (				A	:      bool = False     ):






			'''simple docstring'''







			a					=    [os.path.join(A,						A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
			a					=    [sort_auto_mapping(A,						overwrite=A     ) for fname in fnames]

			if not overwrite and any(A     ):
						a					=    [f for f, d in zip(A,						A     ) if d]
						raise ValueError(
						    F"""The following files have auto mappings that need sorting: {", ".join(A     )}. Run `make style` to fix"""
						    " this."     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		__lowerCAmelCase	:       Optional[Any]     =	parser.parse_args()

		sort_all_auto_mappings(not args.check_only)






